#invention — Public Fediverse posts

“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

* Robert Henry Thurston

###

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.

source

“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

* Robert Henry Thurston

###

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.

source

“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

* Robert Henry Thurston

###

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.

source

“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

* Robert Henry Thurston

###

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.

source

“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

* Robert Henry Thurston

###

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.

source

"[L'invention mathématique] ne consiste pas à faire de nouvelles combinaisons avec des êtres mathématiques déjà connus. Cela, n’importe qui pourrait le faire, mais les combinaisons que l’on pourrait former ainsi seraient en nombre infini, et le plus grand nombre serait absolument dépourvu d’intérêt. Inventer, cela consiste précisément à ne pas construire les combinaisons inutiles et à construire [...]" – Henri Poincaré (1854-1912)
#citation #mathématiques #invention #maths #math

✏️🧼 While Joseph Priestley named #rubber for its ability to rub out marks, erasers actually rely on adhesion.

Chemist Nikhilesh Joardar explains how rubber particles use van der Waals forces to grab graphite more strongly than paper does. Modern versions now include vinyl and polymers for cleaner, more precise corrections.

👉 https://www.popsci.com/science/how-do-erasers-work/

#science #physics #chemistry #history #invention #education #engineering #innovation

3 Teens Win Earth Prize for Asia Inventing Tamarind Powder That Easily Removes Microplastics

https://fed.brid.gy/r/https://www.goodnewsnetwork.org/3-teens-win-earth-prize-for-asia-inventing-tamarind-powder-that-easily-removes-microplastics/

3 Teens Win Earth Prize for Asia Inventing Tamarind Powder That Easily Removes Microplastics

https://fed.brid.gy/r/https://www.goodnewsnetwork.org/3-teens-win-earth-prize-for-asia-inventing-tamarind-powder-that-easily-removes-microplastics/

3 Teens Win Earth Prize for Asia Inventing Tamarind Powder That Easily Removes Microplastics

https://web.brid.gy/r/https://www.goodnewsnetwork.org/3-teens-win-earth-prize-for-asia-inventing-tamarind-powder-that-easily-removes-microplastics/

3 Teens Win Earth Prize for Asia Inventing Tamarind Powder That Easily Removes Microplastics

https://fed.brid.gy/r/https://www.goodnewsnetwork.org/3-teens-win-earth-prize-for-asia-inventing-tamarind-powder-that-easily-removes-microplastics/

3 Teens Win Earth Prize for Asia Inventing Tamarind Powder That Easily Removes Microplastics

https://fed.brid.gy/r/https://www.goodnewsnetwork.org/3-teens-win-earth-prize-for-asia-inventing-tamarind-powder-that-easily-removes-microplastics/

Cela faisait 5668 jours que la contremarque de la chasse au trésor La Guitare du Maladroit était enterrée !
CodeBarre nous l'a annoncé le mardi 12 mai 2026 : elle a été découverte.

https://www.chasses-au-tresor.com/chasses/le-tresor-de-la-guitare-du-maladroit-decouvert.html

#tresor #chasseautresor #decouverte #invention #musique #guitare

#invention : contrivance or construction of that which has not before existed

- French: invention

- Italian: invenzione

- Portuguese: invenção

- Spanish: invento

------------

Fill in missing or incorrect translations @ https://wordofthehour.org/r/translations

Offshore Wind Turbine Prototype Powers an Onboard Data Center, While the Sea Disperses the Heat

https://fed.brid.gy/r/https://www.goodnewsnetwork.org/offshore-wind-turbine-prototype-powers-an-onboard-data-center-while-the-sea-disperses-the-heat/

https://www.europesays.com/afrique/95267/ Innovation en Tunisie : le Concours national de l’invention attire 130 projets pour sa 4e édition #APII #concours #ConcoursNationalDeL'invention #invention #Tunisie

#children #childhood #parenting #parents #lol #funny #humor #joke #comedy #mother #mom #nostalgia #learning #teaching #dad #father #comedian #standup #show #horror #kids #family #writer #crazy #prank #stunt #health #healthy #wellness #love #therapy #women #driving #car #highway #tv #seniors #elderly #aging #technology #AI #invention #cars #trucks #driver

#children #childhood #parenting #parents #lol #funny #humor #joke #comedy #mother #mom #nostalgia #learning #teaching #dad #father #comedian #standup #show #horror #kids #family #writer #crazy #prank #stunt #health #healthy #wellness #love #therapy #women #driving #car #highway #tv #seniors #elderly #aging #technology #AI #invention #cars #trucks #driver

#children #childhood #parenting #parents #lol #funny #humor #joke #comedy #mother #mom #nostalgia #learning #teaching #dad #father #comedian #standup #show #horror #kids #family #writer #crazy #prank #stunt #health #healthy #wellness #love #therapy #women #driving #car #highway #tv #seniors #elderly #aging #technology #AI #invention #cars #trucks #driver

#children #childhood #parenting #parents #lol #funny #humor #joke #comedy #mother #mom #nostalgia #learning #teaching #dad #father #comedian #standup #show #horror #kids #family #writer #crazy #prank #stunt #health #healthy #wellness #love #therapy #women #driving #car #highway #tv #seniors #elderly #aging #technology #AI #invention #cars #trucks #driver

⌚🧠 Student researchers engineered a wearable biosensor that tracks heart rate and skin conductance to provide early warnings for people with #epilepsy.

The #technology uses specialized #algorithms to detect physiological changes that occur before a seizure starts.

👉 https://www.smithsonianmag.com/innovation/these-young-innovators-have-created-a-fitbit-to-predict-epileptic-seizures-180988630/

#innovation #healthtech #health #medicine #engineering #biotech #science #tech #accessibility #invention #wearables

⌚🧠 Student researchers engineered a wearable biosensor that tracks heart rate and skin conductance to provide early warnings for people with #epilepsy.

The #technology uses specialized #algorithms to detect physiological changes that occur before a seizure starts.

👉 https://www.smithsonianmag.com/innovation/these-young-innovators-have-created-a-fitbit-to-predict-epileptic-seizures-180988630/

#innovation #healthtech #health #medicine #engineering #biotech #science #tech #accessibility #invention #wearables

⌚🧠 Student researchers engineered a wearable biosensor that tracks heart rate and skin conductance to provide early warnings for people with #epilepsy.

The #technology uses specialized #algorithms to detect physiological changes that occur before a seizure starts.

👉 https://www.smithsonianmag.com/innovation/these-young-innovators-have-created-a-fitbit-to-predict-epileptic-seizures-180988630/

#innovation #healthtech #health #medicine #engineering #biotech #science #tech #accessibility #invention #wearables

⌚🧠 Student researchers engineered a wearable biosensor that tracks heart rate and skin conductance to provide early warnings for people with #epilepsy.

The #technology uses specialized #algorithms to detect physiological changes that occur before a seizure starts.

👉 https://www.smithsonianmag.com/innovation/these-young-innovators-have-created-a-fitbit-to-predict-epileptic-seizures-180988630/

#innovation #healthtech #health #medicine #engineering #biotech #science #tech #accessibility #invention #wearables

⌚🧠 Student researchers engineered a wearable biosensor that tracks heart rate and skin conductance to provide early warnings for people with #epilepsy.

The #technology uses specialized #algorithms to detect physiological changes that occur before a seizure starts.

👉 https://www.smithsonianmag.com/innovation/these-young-innovators-have-created-a-fitbit-to-predict-epileptic-seizures-180988630/

#innovation #healthtech #health #medicine #engineering #biotech #science #tech #accessibility #invention #wearables

https://www.europesays.com/videos/17917/ Genius & Gadgets: Discovering Paris’s annual invention competition • FRANCE 24 English #France #FRANCE24 #FRANCE24English #FRANCE24 #FRANCE24English #FrenchConnection #gadgets #genius #invention #Lépine #news #Paris #Paris’sAnnualInventionCompetition

Regenerative Break On A Trailer

This is after a failure, but i made this about 8 years ago and the company i did it with made it go down hill

#automotive #electronics #electronic #project #prototype #invention

The Better Screw: A Canadian Grip

In 1908, Canadian P. L. Robertson invented the square-socketed screw to prevent 'cam-out'—the slipping that plagues other screw types. This superior grip made it highly efficient. While the Phillips head became more common globally, the Robertson screw remains widely preferred in Canadian construction and manufacturing for its reliability. #Canada #Innovation #Invention #Tools

https://en.wikipedia.org/wiki/Robertson_screwdriver

World’s Largest Battery Maker to ‘Spare No Effort’ in Electrifying Marine Cargo Industry

https://fed.brid.gy/r/https://www.goodnewsnetwork.org/worlds-largest-battery-maker-to-spare-no-effort-in-electrifying-marine-cargo-industry/

Seeing Around Corners

The phrase “seeing around corners” gets tossed around boardrooms and strategy meetings as though it were a compliment, a kind of secular beatification for the executive or thinker who got there first. But the phrase deserves closer scrutiny, because what it actually describes is a discipline, and one that most people refuse to practice because the conclusions it produces are uncomfortable.

The spatial metaphor is simple enough. Walking down a city street, you cannot see what waits beyond the next corner. A person who could would hold an obvious tactical advantage, whether the thing around the bend is an opportunity or a threat. When we apply that metaphor to business, politics, or creative life, we are talking about pattern recognition operating at a high level: the ability to read weak signals in the present and extrapolate them into likely futures before those futures become obvious to everyone else.

Andy Grove understood this better than most. When he recognized in the mid-1980s that Intel’s commodity memory chip business was dying, the financial data had not yet made the case undeniable. Competitors in Japan were undercutting prices, margins were thinning, and the trajectory pointed toward irrelevance. Grove asked his colleague Gordon Moore a question that has since become famous in business history: “If we got kicked out and the board brought in a new CEO, what would he do?” The answer was clear. He would get out of memory chips. So Grove and Moore did exactly that, pivoting Intel toward microprocessors and building the foundation for decades of dominance. Grove did not predict the future. He read the present more honestly than his peers were willing to, and then followed the logic to its conclusion.

That distinction matters. Seeing around corners is a discipline of interpretation, not a form of prophecy. Prophecy implies access to information no one else possesses. What Grove had was the same data available to every other semiconductor executive in the industry. The difference was his willingness to accept what the data meant rather than constructing reasons to ignore it. Most strategic failures originate in interpretation, or more precisely, in nerve. The signals were there. The pattern was legible. Someone chose not to read it.

In publishing, the same principle applies with brutal regularity. The collapse of the traditional bookstore model did not arrive without warning. Independent booksellers had been losing ground to chains for years, and the chains were losing ground to online retail long before Borders filed for bankruptcy in 2011. The warning signs were visible a decade earlier to anyone who cared to look: declining foot traffic, rising real estate costs, a consumer base increasingly habituated to the convenience of clicking rather than browsing. Publishers who saw around that particular corner had time to build direct relationships with readers, to invest in digital infrastructure, to rethink distribution. Those who waited for the crisis to arrive in full view found themselves scrambling with no lead time and fewer options.

Lead time is the currency that seeing around corners produces. The insight itself has limited value if it does not convert into action, and action requires time. Recognizing a collapsing market six months before it collapses gives you six months to prepare. Recognizing it three years out gives you three years to build alternatives, test them, and refine them before the pressure arrives. The earlier the recognition, the wider the range of possible responses. Wait too long and the range narrows to one: react.

This is why the phrase carries an implicit warning whenever someone says it is “important” to see around corners. The word “important” is doing real work in that sentence. It signals that reactive thinking is insufficient for the situation at hand, that the stakes are high enough to demand anticipation rather than response. A doctor who sees around corners catches the early indicators of a disease before it presents with symptoms. A playwright who sees around corners recognizes that audience expectations are shifting before the box office receipts confirm it. In each case, the advantage belongs to the person who treats the present as evidence rather than as a settled condition.

The discipline has a cost, though. Seeing around corners often means arriving at conclusions that no one else shares, and defending those conclusions against people who are emotionally or financially invested in the current arrangement. It also means accepting the risk that your reading of the signals is wrong, that you are abandoning a viable position based on a pattern that never materializes. Grove faced enormous internal resistance when he proposed abandoning memory chips, a product line that had defined Intel since its founding. The resistance was not irrational. People had built careers around that business. Factories were tooled for it. Customers expected it. Telling an organization that the thing it does best is the thing it needs to stop doing requires a tolerance for isolation that most people do not possess, and a willingness to own the consequences if the foresight proves mistaken.

The real question, then, is whether you are willing to act on what you see. Everyone grants that seeing around corners is a useful skill. Fewer people reckon with the fact that the history of failed enterprises is full of leaders who recognized a coming disruption, documented it in internal memos, discussed it in private meetings, and then did nothing because the present was still comfortable enough to justify inaction. Seeing is the first step. Acting on what you see, before the evidence is so overwhelming that everyone else sees it too, is the step that separates foresight from regret.

Seeing Around Corners

The phrase “seeing around corners” gets tossed around boardrooms and strategy meetings as though it were a compliment, a kind of secular beatification for the executive or thinker who got there first. But the phrase deserves closer scrutiny, because what it actually describes is a discipline, and one that most people refuse to practice because the conclusions it produces are uncomfortable.

The spatial metaphor is simple enough. Walking down a city street, you cannot see what waits beyond the next corner. A person who could would hold an obvious tactical advantage, whether the thing around the bend is an opportunity or a threat. When we apply that metaphor to business, politics, or creative life, we are talking about pattern recognition operating at a high level: the ability to read weak signals in the present and extrapolate them into likely futures before those futures become obvious to everyone else.

Andy Grove understood this better than most. When he recognized in the mid-1980s that Intel’s commodity memory chip business was dying, the financial data had not yet made the case undeniable. Competitors in Japan were undercutting prices, margins were thinning, and the trajectory pointed toward irrelevance. Grove asked his colleague Gordon Moore a question that has since become famous in business history: “If we got kicked out and the board brought in a new CEO, what would he do?” The answer was clear. He would get out of memory chips. So Grove and Moore did exactly that, pivoting Intel toward microprocessors and building the foundation for decades of dominance. Grove did not predict the future. He read the present more honestly than his peers were willing to, and then followed the logic to its conclusion.

That distinction matters. Seeing around corners is a discipline of interpretation, not a form of prophecy. Prophecy implies access to information no one else possesses. What Grove had was the same data available to every other semiconductor executive in the industry. The difference was his willingness to accept what the data meant rather than constructing reasons to ignore it. Most strategic failures originate in interpretation, or more precisely, in nerve. The signals were there. The pattern was legible. Someone chose not to read it.

In publishing, the same principle applies with brutal regularity. The collapse of the traditional bookstore model did not arrive without warning. Independent booksellers had been losing ground to chains for years, and the chains were losing ground to online retail long before Borders filed for bankruptcy in 2011. The warning signs were visible a decade earlier to anyone who cared to look: declining foot traffic, rising real estate costs, a consumer base increasingly habituated to the convenience of clicking rather than browsing. Publishers who saw around that particular corner had time to build direct relationships with readers, to invest in digital infrastructure, to rethink distribution. Those who waited for the crisis to arrive in full view found themselves scrambling with no lead time and fewer options.

Lead time is the currency that seeing around corners produces. The insight itself has limited value if it does not convert into action, and action requires time. Recognizing a collapsing market six months before it collapses gives you six months to prepare. Recognizing it three years out gives you three years to build alternatives, test them, and refine them before the pressure arrives. The earlier the recognition, the wider the range of possible responses. Wait too long and the range narrows to one: react.

This is why the phrase carries an implicit warning whenever someone says it is “important” to see around corners. The word “important” is doing real work in that sentence. It signals that reactive thinking is insufficient for the situation at hand, that the stakes are high enough to demand anticipation rather than response. A doctor who sees around corners catches the early indicators of a disease before it presents with symptoms. A playwright who sees around corners recognizes that audience expectations are shifting before the box office receipts confirm it. In each case, the advantage belongs to the person who treats the present as evidence rather than as a settled condition.

The discipline has a cost, though. Seeing around corners often means arriving at conclusions that no one else shares, and defending those conclusions against people who are emotionally or financially invested in the current arrangement. It also means accepting the risk that your reading of the signals is wrong, that you are abandoning a viable position based on a pattern that never materializes. Grove faced enormous internal resistance when he proposed abandoning memory chips, a product line that had defined Intel since its founding. The resistance was not irrational. People had built careers around that business. Factories were tooled for it. Customers expected it. Telling an organization that the thing it does best is the thing it needs to stop doing requires a tolerance for isolation that most people do not possess, and a willingness to own the consequences if the foresight proves mistaken.

The real question, then, is whether you are willing to act on what you see. Everyone grants that seeing around corners is a useful skill. Fewer people reckon with the fact that the history of failed enterprises is full of leaders who recognized a coming disruption, documented it in internal memos, discussed it in private meetings, and then did nothing because the present was still comfortable enough to justify inaction. Seeing is the first step. Acting on what you see, before the evidence is so overwhelming that everyone else sees it too, is the step that separates foresight from regret.

Seeing Around Corners

The phrase “seeing around corners” gets tossed around boardrooms and strategy meetings as though it were a compliment, a kind of secular beatification for the executive or thinker who got there first. But the phrase deserves closer scrutiny, because what it actually describes is a discipline, and one that most people refuse to practice because the conclusions it produces are uncomfortable.

The spatial metaphor is simple enough. Walking down a city street, you cannot see what waits beyond the next corner. A person who could would hold an obvious tactical advantage, whether the thing around the bend is an opportunity or a threat. When we apply that metaphor to business, politics, or creative life, we are talking about pattern recognition operating at a high level: the ability to read weak signals in the present and extrapolate them into likely futures before those futures become obvious to everyone else.

Andy Grove understood this better than most. When he recognized in the mid-1980s that Intel’s commodity memory chip business was dying, the financial data had not yet made the case undeniable. Competitors in Japan were undercutting prices, margins were thinning, and the trajectory pointed toward irrelevance. Grove asked his colleague Gordon Moore a question that has since become famous in business history: “If we got kicked out and the board brought in a new CEO, what would he do?” The answer was clear. He would get out of memory chips. So Grove and Moore did exactly that, pivoting Intel toward microprocessors and building the foundation for decades of dominance. Grove did not predict the future. He read the present more honestly than his peers were willing to, and then followed the logic to its conclusion.

That distinction matters. Seeing around corners is a discipline of interpretation, not a form of prophecy. Prophecy implies access to information no one else possesses. What Grove had was the same data available to every other semiconductor executive in the industry. The difference was his willingness to accept what the data meant rather than constructing reasons to ignore it. Most strategic failures originate in interpretation, or more precisely, in nerve. The signals were there. The pattern was legible. Someone chose not to read it.

In publishing, the same principle applies with brutal regularity. The collapse of the traditional bookstore model did not arrive without warning. Independent booksellers had been losing ground to chains for years, and the chains were losing ground to online retail long before Borders filed for bankruptcy in 2011. The warning signs were visible a decade earlier to anyone who cared to look: declining foot traffic, rising real estate costs, a consumer base increasingly habituated to the convenience of clicking rather than browsing. Publishers who saw around that particular corner had time to build direct relationships with readers, to invest in digital infrastructure, to rethink distribution. Those who waited for the crisis to arrive in full view found themselves scrambling with no lead time and fewer options.

Lead time is the currency that seeing around corners produces. The insight itself has limited value if it does not convert into action, and action requires time. Recognizing a collapsing market six months before it collapses gives you six months to prepare. Recognizing it three years out gives you three years to build alternatives, test them, and refine them before the pressure arrives. The earlier the recognition, the wider the range of possible responses. Wait too long and the range narrows to one: react.

This is why the phrase carries an implicit warning whenever someone says it is “important” to see around corners. The word “important” is doing real work in that sentence. It signals that reactive thinking is insufficient for the situation at hand, that the stakes are high enough to demand anticipation rather than response. A doctor who sees around corners catches the early indicators of a disease before it presents with symptoms. A playwright who sees around corners recognizes that audience expectations are shifting before the box office receipts confirm it. In each case, the advantage belongs to the person who treats the present as evidence rather than as a settled condition.

The discipline has a cost, though. Seeing around corners often means arriving at conclusions that no one else shares, and defending those conclusions against people who are emotionally or financially invested in the current arrangement. It also means accepting the risk that your reading of the signals is wrong, that you are abandoning a viable position based on a pattern that never materializes. Grove faced enormous internal resistance when he proposed abandoning memory chips, a product line that had defined Intel since its founding. The resistance was not irrational. People had built careers around that business. Factories were tooled for it. Customers expected it. Telling an organization that the thing it does best is the thing it needs to stop doing requires a tolerance for isolation that most people do not possess, and a willingness to own the consequences if the foresight proves mistaken.

The real question, then, is whether you are willing to act on what you see. Everyone grants that seeing around corners is a useful skill. Fewer people reckon with the fact that the history of failed enterprises is full of leaders who recognized a coming disruption, documented it in internal memos, discussed it in private meetings, and then did nothing because the present was still comfortable enough to justify inaction. Seeing is the first step. Acting on what you see, before the evidence is so overwhelming that everyone else sees it too, is the step that separates foresight from regret.

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"There are only two genders! It's basic biology!" is the shows you stopped learning in 2nd grade equivalent of "Thomas Edison invented the light bulb! It's basic science / history!"

"There are only two genders! It's basic biology!" is the shows you stopped learning in 2nd grade equivalent of "Thomas Edison invented the light bulb! It's basic science / history!"

"There are only two genders! It's basic biology!" is the shows you stopped learning in 2nd grade equivalent of "Thomas Edison invented the light bulb! It's basic science / history!"

"There are only two genders! It's basic biology!" is the shows you stopped learning in 2nd grade equivalent of "Thomas Edison invented the light bulb! It's basic science / history!"

"There are only two genders! It's basic biology!" is the shows you stopped learning in 2nd grade equivalent of "Thomas Edison invented the light bulb! It's basic science / history!"

The Rental Life: What Happens When You Own Nothing and They Own You

In July 2009, Amazon reached into the Kindle devices of thousands of customers and deleted copies of George Orwell’s 1984 and Animal Farm. The company had discovered that the third-party publisher selling those editions lacked the rights to distribute them in the United States. Amazon issued refunds. Then it erased the books. A high school student in Michigan lost his annotated copy mid-assignment. A class-action lawsuit followed. Amazon’s CEO called the decision “stupid, thoughtless, and painfully out of line with our principles.” The company settled and promised not to do it again, unless a court ordered it, or unless the company determined it was necessary to protect consumers from malicious code, or unless the consumer failed to keep paying.

That string of qualifications matters more than the apology. Amazon conceded only that it would try to restrain a power it confirmed it possessed. And the definition of a good reason for using that power remained, as it remains today, in Amazon’s hands.

This episode from seventeen years ago now reads as a rehearsal for the present. The subscription economy has grown from a few hundred billion dollars in 2020 to an estimated $558 billion in 2025, with projections approaching $1.2 trillion by 2030 and nearly $2 trillion by 2035. Those numbers track a civilization that has been steadily converting ownership into tenancy. Your music, your software, your games, and in some cases even your car’s heated seats exist only as long as you keep paying, keep complying with terms you did not write, and keep trusting that the company on the other end of the wire will still be there tomorrow.

The Counterfeit of Possession

When you walk into a bookstore and buy a hardcover, the transaction is finished the moment you hand over your money. The book belongs to you. You can lend it, sell it, burn it, annotate its margins, or leave it to your grandchildren. No one from the publisher’s office will appear at your door to confiscate it because a licensing agreement expired. The relationship between you and the object is complete and sovereign.

When you “buy” a digital book, a digital album, or a digital game, the word “buy” is performing a conjuring trick. You are purchasing a license, a permission slip that can be revoked. California recognized the deception clearly enough to pass Assembly Bill 2426, which took effect January 1, 2025. The law prohibits sellers of digital goods from using the words “buy” or “purchase” unless they disclose, separately and conspicuously, that the consumer is receiving a revocable license and not ownership. Plaintiffs’ firms have already begun filing class actions under the statute. Yet the law addresses only the label. After January 1, 2025, companies must tell you that “buy” means “rent.” They are under no obligation to offer you the option of actually buying.

The fact that California had to pass a law telling companies to stop lying about what the word “buy” means tells you everything about where we are. Commercial language has been hollowed out. Familiar verbs of transaction, “buy,” “own,” “purchase,” still circulate in the marketplace, but they no longer carry their historical weight. They have become costume jewelry worn over a bare finger.

Adobe provides the corporate template. In 2013, the company began phasing out perpetual licenses for its professional software. By 2017, Creative Suite 6 was pulled from sale entirely. Photoshop, Illustrator, InDesign, Premiere Pro: tools that graphic designers, photographers, filmmakers, and publishers had owned outright for decades became rental properties. If you stop paying, the software stops working. One user on Adobe’s own community forum described how the company shut down the activation server for a version he had purchased with a perpetual license years earlier. When he called support, they told him the product had been discontinued and urged him to subscribe. The perpetual license, it turned out, was not so perpetual after all.

Subscription defenders point to accessibility: Adobe’s Creative Cloud at $55 per month costs less upfront than the $2,000 that Creative Suite Master Collection once demanded. The argument sounds reasonable until you run the numbers over time. A designer who paid $2,000 in 2012 still owns functional software in 2026. A designer who has paid $55 per month since 2013 has spent more than $8,500 and owns nothing. The moment the payments stop, the tools vanish. Lower barriers to entry become higher barriers to exit, and the total cost of permanent rental exceeds the cost of ownership within a few years.

You Will Own Nothing and You Will Browse the Store

Ubisoft, the French video game publisher, demonstrated the logical endpoint of this model with a bluntness that bordered on parody. In December 2023, the company delisted its racing game The Crew from all digital storefronts without advance warning. Three months later, on March 31, 2024, it shut down the game’s servers. Because the game required an always-online connection, the shutdown rendered it permanently unplayable for everyone who had bought it, whether digitally or on disc. Players who attempted to launch the game were met with a notification: “You no longer have access to this game. Why not check the Store to pursue your adventures?”

That message deserves to be read twice. The company that took your money for a product, then destroyed that product, then invited you to spend more money in the same store, saw nothing strange about the sequence. By April 2024, Ubisoft began revoking the game licenses themselves and moved the title into an “inactive games” section. Its lawyers argued in subsequent legal proceedings that customers had never purchased “unfettered ownership rights” but merely “a limited access license.” The French consumer rights organization UFC-Que Choisir filed suit, calling the arrangement an “abusive contract.” The European consumer movement Stop Killing Games emerged in direct response.

What happened with The Crew followed the subscription model’s internal logic with mechanical precision. Your product was never yours. No transaction was ever complete. The seller retained the power to terminate the relationship unilaterally, and when that power became convenient to exercise, the seller exercised it.

The Heated Seat and the Cold Principle

If the digital domain were the only territory being converted to rental, the problem would be serious but contained. The alarm escalates when subscription logic migrates into physical objects you have already paid for.

In July 2022, BMW began charging customers a monthly fee to activate the heated seats already installed in their vehicles. The hardware was present in the car. Wiring was in place. Heating elements were embedded in the leather. But the function was locked behind software, and unlocking it cost $18 per month. The company framed this as consumer choice, a way for buyers to add features later without committing at the time of purchase. Consumers framed it differently: they were being asked to rent access to machinery they had already bought.

BMW eventually retreated from heated-seat subscriptions after sustained backlash, but the retreat was tactical while the philosophy remained intact. The company’s board member for sales, Pieter Nota, told the press that the approach “was probably not the best way to start.” BMW remains, in its own words, “fully committed” to its ConnectedDrive subscription environment and continues to offer features like adaptive suspension, adaptive cruise control, and parking assistance as paid unlocks. Tesla has moved in the same direction, paywalling features behind its Full Self-Driving subscription. Mercedes-Benz charges annual fees to unlock additional performance in its electric vehicles. Automakers have settled the principle even if the specific application of heated seats proved too visible a provocation.

Here is what that principle means in practice: you purchased the car, but the car contains capabilities that do not belong to you. Your manufacturer retains a residual claim on the object sitting in your driveway. Your property is, in a meaningful legal and functional sense, not entirely your property.

The Surgical Table

The migration of subscription logic into medicine deserves particular attention because it involves the point where commercial arrangements meet human bodies. Intuitive Surgical, the manufacturer of the da Vinci robotic surgery system, has built its business model around recurring revenue. A February 2026 report from the American College of Surgeons noted that approximately 85% of Intuitive’s revenue now comes from recurring costs rather than from the sale of the machines themselves. The surgical instruments are designed to be disposable, usable for ten to eighteen procedures before the system requires new ones. Hospitals buy the robot, but the robot’s ongoing capacity to function depends on a continuous stream of purchases that the manufacturer controls.

This is subscription logic applied to the operating room, and it differs from the ordinary fact that scalpel blades and sutures have always been disposable. Unlike traditional surgical consumables, which are generic, interchangeable, and available from competing suppliers, Intuitive’s instruments are proprietary, coded to the machine, and designed with built-in usage limits that require replacement from a single manufacturer after a fixed number of procedures. The distinction matters: a hospital using traditional instruments can switch vendors tomorrow, while a hospital locked into the da Vinci ecosystem cannot. Whether a hospital can perform surgery on you depends on whether it has remained current on its instrument purchases, whether the manufacturer continues to supply compatible parts, and whether the financial arrangement between hospital and vendor remains intact. Patients on the table have no visibility into any of these commercial relationships, yet those relationships determine whether the machine works when the surgeon reaches for it.

What Disappears When Access Replaces Ownership

Individual inconveniences of the subscription economy, a deleted book, a locked seat heater, a revoked game license, accumulate into a structural transformation that is worth examining in its constituent damages.

Cultural memory suffers first. Archives depend on permanence. A library works because the books on its shelves will still be there next year, and the year after, and a century from now. Digital content governed by revocable licenses cannot be archived in any meaningful sense because the license holder retains the right to make that content vanish. When Ubisoft destroyed The Crew, it did not merely inconvenience the people who were still playing it. It erased a cultural artifact, a ten-year-old piece of interactive art that can no longer be experienced, studied, or referenced by anyone. Modders managed to bring the game back to life in 2025, proving that an unofficial preservation solution was technically possible. Ubisoft’s response was to revoke licenses to prevent even that.

Autonomy erodes alongside memory. Ownership confers the right to modify, repair, resell, and repurpose. A farmer who owns a tractor can fix it when it breaks. Someone who owns a book can lend it to a friend. Any photographer who owns a copy of Photoshop CS6 can keep using it for twenty years without asking anyone’s permission. Subscription models extinguish these rights. You cannot modify software you are renting. Reselling a license you do not own is impossible. Nor can you repair a feature that has been locked behind a paywall in a car you have already paid for. The subscription economy converts users into dependents, people who must ask permission to use things that are already in their possession.

Democratic resilience takes the deepest wound. When the infrastructure of daily life, the tools people use to work, to communicate, to create, to travel, is governed by corporate access gates, citizens become tenants of their own civilization. A government can be voted out. A regulatory body can be reformed. But a corporation that controls whether your software works, whether your car’s features are enabled, and whether your surgical robot has fresh instruments operates outside the democratic feedback loop. Its power flows from contracts of adhesion, and those contracts are written by the party holding all the leverage.

The Counterfeit Bargain

There is a direct line between the subscription economy and the taxonomy of fakery that structures The Counterfeit Bargain. When a company sells you a “purchase” that is a license, it is offering a counterfeit transaction. Every element of the exchange looks like buying. Its language says buying. Its interface mimics buying. But the substance is rental, and the landlord holds the keys.

The average American household now maintains roughly twelve paid subscriptions, with younger consumers averaging seventeen. A Kearney survey found that 72% of consumers underestimate their total monthly subscription spending by an average of 40%. This is the arithmetic of the counterfeit: people who believe they are accumulating possessions are instead accumulating obligations. Each subscription is a thread tying them to a provider who can raise prices, change terms, degrade service, or simply disappear, leaving the subscriber holding nothing.

The subscription economy asks us to accept a bargain that previous generations of consumers would have found absurd: pay for something, and receive in return only the conditional, temporary, revocable right to use it. When the condition changes, or the term expires, or the revocation is exercised, what you have left is exactly what you started with. Nothing. The word for a person who pays to live in a space owned by someone else is “tenant.” We have become tenants of our own tools, tenants of our own entertainment, and in the case of locked car features and subscription-gated surgical instruments, tenants of our own machines. The landlord class just learned to code.

This Swedish company is 3D‑printing glass — and it’s not Sci‑Fi

https://youtube.com/shorts/o6hBD9nV4f0?is=7IyAIVzueFZ2j3_-

> Lasers 3D-printing glass for medical applications?No, this is not from a sci-fi movie – we're talking about a Swedish invention!

Printing fricking glass from scratch, just imagine the possibilities with that technology and its ongoing improvement. 🤯🤩

We're living in interesting times. 😁

#Sweden #Swedish #3D #Printing #Glass #invention

Architect Creates App to Show Which Pub Gardens are Sunny – Using Shadow Simulations From Buildings https://www.allforgardening.com/1695846/architect-creates-app-to-show-which-pub-gardens-are-sunny-using-shadow-simulations-from-buildings/ #apps #architecture #community #creativity #garden #gardening #invention #London #Social #technology #uk #urban #weather!

Architect Creates App to Show Which Pub Gardens are Sunny – Using Shadow Simulations From Buildings

https://fed.brid.gy/r/https://www.goodnewsnetwork.org/architect-creates-app-to-show-which-pub-patios-are-sunny/

A man who wanted to ‘see music’ paired a piano with bioluminescent algae. It’s magical to watch.

https://fed.brid.gy/r/https://www.upworthy.com/htx-studio-makes-bioluminescent-algae-piano-to-see-music/?utm_source=rss&utm_medium=rss&utm_campaign=htx-studio-makes-bioluminescent-algae-piano-to-see-music

A man who wanted to ‘see music’ paired a piano with bioluminescent algae. It’s magical to watch.

https://web.brid.gy/r/https://www.upworthy.com/htx-studio-makes-bioluminescent-algae-piano-to-see-music/?utm_source=rss&utm_medium=rss&utm_campaign=htx-studio-makes-bioluminescent-algae-piano-to-see-music

A man who wanted to ‘see music’ paired a piano with bioluminescent algae. It’s magical to watch.

https://web.brid.gy/r/https://www.upworthy.com/htx-studio-makes-bioluminescent-algae-piano-to-see-music/?utm_source=rss&utm_medium=rss&utm_campaign=htx-studio-makes-bioluminescent-algae-piano-to-see-music

A man who wanted to ‘see music’ paired a piano with bioluminescent algae. It’s magical to watch.

https://web.brid.gy/r/https://www.upworthy.com/htx-studio-makes-bioluminescent-algae-piano-to-see-music/?utm_source=rss&utm_medium=rss&utm_campaign=htx-studio-makes-bioluminescent-algae-piano-to-see-music

Our Latest project...
The Automatic Ovoid Scrivener is here.

Watch it in action 👇
youtu.be/tj7ck5u2jzM

#Maker #DIY #Engineering #STEM #Robotics #Easter #Invention #Tech #CreativeTech #eggs #easteregg @GurgleApps
Https://youtu.be/tj7ck5u2jzM

Our Latest project...
The Automatic Ovoid Scrivener is here.
Watch it in action 👇
https://youtu.be/tj7ck5u2jzM

#Maker #DIY #Engineering #STEM #Robotics #Easter #Invention #Tech #CreativeTech #easteregg

Our Latest project...
The Automatic Ovoid Scrivener is here.

Watch it in action 👇
https://youtu.be/tj7ck5u2jzM

#Maker #DIY #Engineering #STEM #Robotics #Easter #Invention #Tech #CreativeTech #easteregg

The high school student whose filter uses magnetic oil to trap microplastics

https://web.brid.gy/r/https://www.optimistdaily.com/2026/03/the-high-school-student-whose-filter-uses-magnetic-oil-to-trap-microplastics/