#nikolatesla — Public Fediverse posts on home.social

(Roughly) Daily @[email protected] · 2026-07-10 · 08:00 UTC

“Technology doesn’t force us… it merely opens the door”*…

The estimable Tim O’Reilly reminds us to think deeply about how AI could and should turn out. He suggests that Jeff Ding‘s diffusion theory of the role of technology in great-power competition also applies to AI adoption– and that it suggests that companies obsessed with the frontier might be optimizing for the wrong thing…

In the 1980s, Japan led the world in semiconductors, consumer electronics, and computer hardware, the industries everyone assumed would decide the next phase of economic power. Japan won them and still did not overtake the United States in the information revolution that followed. Jeff Ding, a political scientist at George Washington University, opens his book Technology and the Rise of Great Powers with the history of the first and second industrial revolutions and the third, the information revolution. The explanation he gives for who wins and who loses applies to companies as well as it does to nations, and very much to the current trajectory of AI.
Ding contrasts two theories of how technological revolutions reshape economic power. The conventional one he calls the leading sector model, or LS theory. It goes like this: New technologies create fast-growing new industries like steel and railroads and automobiles and semiconductors, and the country that dominates invention in those sectors captures the monopoly profits and the upstream and downstream economic linkages that come with them. As the story goes, if you win the leading sector, you win the era. Britain won in the first industrial revolution through its mastery of steam power, and then was surpassed by the US in the second through its leadership in electrification, the internal combustion engine, and mass manufacturing. The US kept its lead over Japan in the information systems revolution not by competing in the “leading sector” of electronic hardware but by diffusing “up the stack” via software that took the power of computing into every sector of the economy. (OK, that last bit is my explanation of what happened rather than Ding’s, but it’s consistent with his theory.)
Leading Sector theory is pretty clearly the working hypothesis of today’s AI industry and the national strategy that is forming around that industry. The company and the country with the biggest and best models wins. Everyone else is an also-ran.
Ding offers another explanation, which he calls diffusion theory. He points out that general-purpose technologies, foundational ones like the steam engine, electricity, and the computer, don’t just create massive profits and productivity gains in a single industry but instead spread across the whole economy. National economic leadership comes not from inventing the new sector but from diffusing the general-purpose technology more quickly and more broadly than your rivals. This happens over decades. The win goes to whoever most successfully embeds the technology into a wide range of ordinary productive work. This is how the US kept its lead over Japan rather than being surpassed by it.
This is obviously aligned with the thinking of Arvind Narayanan and Sayash Kapoor in “AI as Normal Technology,” which Ding cites in his book.
A big part of what enables diffusion is what Ding calls skill infrastructure, the education and training systems that widen the pool of people who can actually work with the technology. When the priority is widespread adoption rather than invention, he argues, the institutions that matter are the ones that build engineering skill at scale, standardize good practice, and tie research to industry. He writes:
GPT diffusion theory highlights the importance of GPT [General Purpose Technology] skill infrastructure. Education and training systems that widen the pool of engineering skills and knowledge linked to a GPT. When widespread adoption of GPTs is the priority, it is ordinary engineers, not heroic inventors, who matter.
Music to my ears, as it should be to yours: “It is ordinary engineers, not heroic inventors, who matter.”
That is not how the current AI narrative goes. Everyone is fixated on the labs, the frontier models, and the most famous researchers. And that fixation shapes enterprise strategy. Inside many companies AI strategy is a procurement decision: Which model and which vendor and which flagship tool should we choose? Or it’s a moonshot to stand up a lab and build an impressive demo and hire your own famous developer. Both approaches treat AI as a sector to be won. Ding’s argument is that the breakthrough sector itself is not where the long-term value for national power lives. And I believe that the same applies to corporate success. The value is in how widely and how well the technology gets embedded into the work of the people you already employ. The company that puts AI to work in finance and support and legal and sales and operations, across every unglamorous process, as well as in product and engineering, outperforms its competitors and drives its industry forward.
The reason diffusion takes a long time is that it is an organizational problem and not a technical one…
[Tim elaborates, and specifies the requirements for successful management of what is an “enterprise transformation problem”; he then unpacks the geopolitics of AI. He concludes…]
… Sovereign AI is not just a matter of national power. It is a predictable consequence of diffusion. A technology that diffuses widely will be adapted by different societies, firms, and institutions to suit their own needs, values, and constraints. Sovereign AI is AI designed for diffusion, not just raw increases in capability.
This is one reason the arms-race framing is unhelpful. It encourages us to treat AI as if it were a weapons system or a scarce strategic asset. But if AI is closer to electrification, computing, or the written word, the important thing is how the technology is embedded into the ordinary life of economies and institutions, and whether that embedding happens in ways that increase agency broadly rather than concentrating it in a few hyperpowerful companies.
There are a few additional lessons we can take from the history of electrification. While motors became decentralized, factories stopped generating their own power and bought it from a centralized grid. The unit-drive revolution decentralized application, not generation. This limitation, which we are now working to overcome to some extent with decentralized solar generation, is perhaps ironically showing up most strongly in the strain that AI data centers are placing on the grid. Let’s learn from that misstep. You can diffuse AI into every workflow via API calls to a big centralized model, or it can be diffused by a network of smaller models that turbocharge every part of the economy.
We should design for a future of multiple AIs, not a single universal system. Different countries will want systems shaped by different legal regimes, languages, histories, and cultural assumptions. So will companies. So will professions and communities of practice. The instinct of some frontier labs is to imagine that the right answer is to homogenize the technology, purge it of bias, and offer a single sanitized intelligence layer for the world. But AI is a social and cultural technology. The differences are not a defect to be smoothed away.
We do need to think about standards and interoperability. The historical analogy that comes to mind is railroad gauge. When real world systems are built to incompatible standards, the result is not healthy diversity but decades of friction, kludges, and retrofitting. The same may prove true for AI. If we force the future into a choice between one universal model and a patchwork of disconnected sovereign systems, we will get the worst of both worlds. We need a layer between uniformity and fragmentation, which can come from standardized protocols that allow different models, tools, and institutions to interoperate without requiring them to become identical.
This is also why open source matters, but only if it is properly understood. Open source is not just about licenses. My earliest introduction to the shared development of software that now goes by that name came from the research community that grew up around Bell Labs’ Unix operating system despite AT&T’s proprietary (albeit permissive) licensing. Because of that experience, I became convinced that it was the modular, protocol-centric architecture of Unix that was a key driver of collaborative, internet-enabled software development.
Open source AI depends on far more than open models. It depends on the architecture of participation built into the systems above and around them: the protocols, servers, interfaces, and shared technical conventions that let many different actors build on common foundations. The Open Source AI Gap Map shows just how rich that open source AI ecosystem is becoming. But open source can also coexist with proprietary, de facto standards like the OpenAI and Anthropic APIs. Like the electric grid we are now beginning to rebuild, the AI future will be a mix of centralized and decentralized systems. Cooperation and competition can coexist. Different actors can build different systems, for different purposes, under different forms of governance, while still participating in a shared technical and economic order.
This is how the future can belong not just to the inventors of AI but to the people who make it usable, adaptable, interoperable, and worth adopting.

Eminently worth reading in full. AI for all of us: “Ordinary Engineers, Not Heroic Inventors,” from @timoreilly.bsky.social

Apposite: “How to talk about “AI” without adding to the anthropomorphization“

* Allan Dafoe

###

As we amplify access, we might we might spare a thought for someone who launched more than one central technology into braod diffusion: the Serbian-American electrical engineer and inventor Nikola Tesla; he died on this date in 1943. Tesla is probably best remembered for his rivalry with Thomas Edison: Tesla invented and patented the first AC motor and generator (c.f.: Niagara Falls); Edison promoted DC power… and went to great lengths to discredit Tesla and his approach. In the end, of course, Tesla was right.

Tesla patented over 300 inventions worldwide, though he kept many of his creations out of the patent system to protect their confidentiality. His work ranged widely, from technology critical to the development of radio to the first remote control. At the turn of the century, Tesla designed and began planning a “worldwide wireless communications system” that was backed by J.P. Morgan… until Morgan lost confidence and pulled out. “Cyberspace,” as described by the likes of William Gibson and Neal Stephenson, is largely prefigured in Tesla’s plan. On Tesla’s 75th birthday in 1931, Time put him on its cover, captioned “All the world’s his power house.” He received congratulatory letters from Albert Einstein and more than 70 other pioneers in science and engineering. But Tesla’s talent ran far, far ahead of his luck. He died penniless in Room 3327 of the New Yorker Hotel.

source

#AI #alternatingCurrent #artificialIntelligence #culture #cyberspace #diffusion #diffusionTheory #electricity #history #JeffDing #NikolaTesla #politics #Technology #TimOReilly #wireless

#ai #alternatingcurrent #artificialintelligence #culture #cyberspace #diffusion

(Roughly) Daily @[email protected] · 2026-07-10 · 08:00 UTC

“Technology doesn’t force us… it merely opens the door”*…

The estimable Tim O’Reilly reminds us to think deeply about how AI could and should turn out. He suggests that Jeff Ding‘s diffusion theory of the role of technology in great-power competition also applies to AI adoption– and that it suggests that companies obsessed with the frontier might be optimizing for the wrong thing…

In the 1980s, Japan led the world in semiconductors, consumer electronics, and computer hardware, the industries everyone assumed would decide the next phase of economic power. Japan won them and still did not overtake the United States in the information revolution that followed. Jeff Ding, a political scientist at George Washington University, opens his book Technology and the Rise of Great Powers with the history of the first and second industrial revolutions and the third, the information revolution. The explanation he gives for who wins and who loses applies to companies as well as it does to nations, and very much to the current trajectory of AI.
Ding contrasts two theories of how technological revolutions reshape economic power. The conventional one he calls the leading sector model, or LS theory. It goes like this: New technologies create fast-growing new industries like steel and railroads and automobiles and semiconductors, and the country that dominates invention in those sectors captures the monopoly profits and the upstream and downstream economic linkages that come with them. As the story goes, if you win the leading sector, you win the era. Britain won in the first industrial revolution through its mastery of steam power, and then was surpassed by the US in the second through its leadership in electrification, the internal combustion engine, and mass manufacturing. The US kept its lead over Japan in the information systems revolution not by competing in the “leading sector” of electronic hardware but by diffusing “up the stack” via software that took the power of computing into every sector of the economy. (OK, that last bit is my explanation of what happened rather than Ding’s, but it’s consistent with his theory.)
Leading Sector theory is pretty clearly the working hypothesis of today’s AI industry and the national strategy that is forming around that industry. The company and the country with the biggest and best models wins. Everyone else is an also-ran.
Ding offers another explanation, which he calls diffusion theory. He points out that general-purpose technologies, foundational ones like the steam engine, electricity, and the computer, don’t just create massive profits and productivity gains in a single industry but instead spread across the whole economy. National economic leadership comes not from inventing the new sector but from diffusing the general-purpose technology more quickly and more broadly than your rivals. This happens over decades. The win goes to whoever most successfully embeds the technology into a wide range of ordinary productive work. This is how the US kept its lead over Japan rather than being surpassed by it.
This is obviously aligned with the thinking of Arvind Narayanan and Sayash Kapoor in “AI as Normal Technology,” which Ding cites in his book.
A big part of what enables diffusion is what Ding calls skill infrastructure, the education and training systems that widen the pool of people who can actually work with the technology. When the priority is widespread adoption rather than invention, he argues, the institutions that matter are the ones that build engineering skill at scale, standardize good practice, and tie research to industry. He writes:
GPT diffusion theory highlights the importance of GPT [General Purpose Technology] skill infrastructure. Education and training systems that widen the pool of engineering skills and knowledge linked to a GPT. When widespread adoption of GPTs is the priority, it is ordinary engineers, not heroic inventors, who matter.
Music to my ears, as it should be to yours: “It is ordinary engineers, not heroic inventors, who matter.”
That is not how the current AI narrative goes. Everyone is fixated on the labs, the frontier models, and the most famous researchers. And that fixation shapes enterprise strategy. Inside many companies AI strategy is a procurement decision: Which model and which vendor and which flagship tool should we choose? Or it’s a moonshot to stand up a lab and build an impressive demo and hire your own famous developer. Both approaches treat AI as a sector to be won. Ding’s argument is that the breakthrough sector itself is not where the long-term value for national power lives. And I believe that the same applies to corporate success. The value is in how widely and how well the technology gets embedded into the work of the people you already employ. The company that puts AI to work in finance and support and legal and sales and operations, across every unglamorous process, as well as in product and engineering, outperforms its competitors and drives its industry forward.
The reason diffusion takes a long time is that it is an organizational problem and not a technical one…
[Tim elaborates, and specifies the requirements for successful management of what is an “enterprise transformation problem”; he then unpacks the geopolitics of AI. He concludes…]
… Sovereign AI is not just a matter of national power. It is a predictable consequence of diffusion. A technology that diffuses widely will be adapted by different societies, firms, and institutions to suit their own needs, values, and constraints. Sovereign AI is AI designed for diffusion, not just raw increases in capability.
This is one reason the arms-race framing is unhelpful. It encourages us to treat AI as if it were a weapons system or a scarce strategic asset. But if AI is closer to electrification, computing, or the written word, the important thing is how the technology is embedded into the ordinary life of economies and institutions, and whether that embedding happens in ways that increase agency broadly rather than concentrating it in a few hyperpowerful companies.
There are a few additional lessons we can take from the history of electrification. While motors became decentralized, factories stopped generating their own power and bought it from a centralized grid. The unit-drive revolution decentralized application, not generation. This limitation, which we are now working to overcome to some extent with decentralized solar generation, is perhaps ironically showing up most strongly in the strain that AI data centers are placing on the grid. Let’s learn from that misstep. You can diffuse AI into every workflow via API calls to a big centralized model, or it can be diffused by a network of smaller models that turbocharge every part of the economy.
We should design for a future of multiple AIs, not a single universal system. Different countries will want systems shaped by different legal regimes, languages, histories, and cultural assumptions. So will companies. So will professions and communities of practice. The instinct of some frontier labs is to imagine that the right answer is to homogenize the technology, purge it of bias, and offer a single sanitized intelligence layer for the world. But AI is a social and cultural technology. The differences are not a defect to be smoothed away.
We do need to think about standards and interoperability. The historical analogy that comes to mind is railroad gauge. When real world systems are built to incompatible standards, the result is not healthy diversity but decades of friction, kludges, and retrofitting. The same may prove true for AI. If we force the future into a choice between one universal model and a patchwork of disconnected sovereign systems, we will get the worst of both worlds. We need a layer between uniformity and fragmentation, which can come from standardized protocols that allow different models, tools, and institutions to interoperate without requiring them to become identical.
This is also why open source matters, but only if it is properly understood. Open source is not just about licenses. My earliest introduction to the shared development of software that now goes by that name came from the research community that grew up around Bell Labs’ Unix operating system despite AT&T’s proprietary (albeit permissive) licensing. Because of that experience, I became convinced that it was the modular, protocol-centric architecture of Unix that was a key driver of collaborative, internet-enabled software development.
Open source AI depends on far more than open models. It depends on the architecture of participation built into the systems above and around them: the protocols, servers, interfaces, and shared technical conventions that let many different actors build on common foundations. The Open Source AI Gap Map shows just how rich that open source AI ecosystem is becoming. But open source can also coexist with proprietary, de facto standards like the OpenAI and Anthropic APIs. Like the electric grid we are now beginning to rebuild, the AI future will be a mix of centralized and decentralized systems. Cooperation and competition can coexist. Different actors can build different systems, for different purposes, under different forms of governance, while still participating in a shared technical and economic order.
This is how the future can belong not just to the inventors of AI but to the people who make it usable, adaptable, interoperable, and worth adopting.

Eminently worth reading in full. AI for all of us: “Ordinary Engineers, Not Heroic Inventors,” from @timoreilly.bsky.social

Apposite: “How to talk about “AI” without adding to the anthropomorphization“

* Allan Dafoe

###

As we amplify access, we might we might spare a thought for someone who launched more than one central technology into braod diffusion: the Serbian-American electrical engineer and inventor Nikola Tesla; he died on this date in 1943. Tesla is probably best remembered for his rivalry with Thomas Edison: Tesla invented and patented the first AC motor and generator (c.f.: Niagara Falls); Edison promoted DC power… and went to great lengths to discredit Tesla and his approach. In the end, of course, Tesla was right.

Tesla patented over 300 inventions worldwide, though he kept many of his creations out of the patent system to protect their confidentiality. His work ranged widely, from technology critical to the development of radio to the first remote control. At the turn of the century, Tesla designed and began planning a “worldwide wireless communications system” that was backed by J.P. Morgan… until Morgan lost confidence and pulled out. “Cyberspace,” as described by the likes of William Gibson and Neal Stephenson, is largely prefigured in Tesla’s plan. On Tesla’s 75th birthday in 1931, Time put him on its cover, captioned “All the world’s his power house.” He received congratulatory letters from Albert Einstein and more than 70 other pioneers in science and engineering. But Tesla’s talent ran far, far ahead of his luck. He died penniless in Room 3327 of the New Yorker Hotel.

source

#AI #alternatingCurrent #artificialIntelligence #culture #cyberspace #diffusion #diffusionTheory #electricity #history #JeffDing #NikolaTesla #politics #Technology #TimOReilly #wireless

#ai #alternatingcurrent #artificialintelligence #culture #cyberspace #diffusion

Karl Voit :emacs: :orgmode: @[email protected] · 2026-06-11 · 21:45 UTC

Nice anniversary party at my old alma mater in the #NikolaTesla lab.

(Yes, Tesla was once a student here but he "may have been expelled for gambling and womanizing." https://en.wikipedia.org/wiki/Nikola_Tesla)

I also briefly met @kcposch who passed by sheer luck - of course on his Brompton.

#TUG #Graz #TUGraz

#nikolatesla #tug #graz #tugraz

Karl Voit :emacs: :orgmode: @[email protected] · 2026-06-11 · 21:45 UTC

Nice anniversary party at my old alma mater in the #NikolaTesla lab.

(Yes, Tesla was once a student here but he "may have been expelled for gambling and womanizing." https://en.wikipedia.org/wiki/Nikola_Tesla)

I also briefly met @kcposch who passed by sheer luck - of course on his Brompton.

#TUG #Graz #TUGraz

#nikolatesla #tug #graz #tugraz

Freya161 @[email protected] · 2026-06-02 · 18:01 UTC

#zitate #sprueche #gemeinschaft #nikolatesla #nikolateslaquotes

Freya161 @[email protected] · 2026-05-31 · 09:16 UTC

#zitate #sprueche #hass #tesla #nikolatesla

Silverlenz @[email protected] · 2026-05-30 · 18:00 UTC

A visionary imprisoned within his own invention, Nikola Tesla's mind becomes the filament that illuminates an entire world. The Earth crowns his thoughts like a halo of consequence, proof that one soul's current can electrify all of humanity.

#NikolaTesla #MindOverMatter #GeniusUnbound #ElectricDreams #NoosphereKing

https://silverlenz.carrd.co
Zap ⚡ if it resonates — support the transmissions directly: https://silverlenz.github.io/zack-zaps/

#nikolatesla #mindovermatter #geniusunbound #electricdreams #noosphereking

Mbingu @[email protected] · 2026-05-04 · 12:57 UTC

Thiel and musk complain about a lack of Innovation, what if the next disruptive lot of scientific discovery depends on us becoming reasonable human beings? Just like Tesla said his brain is just a receiver of cosmic knowledge. He received what humanity was ready for at his time.
Society first has to establish the requirements of the next phase.
One thing is clear it has to be a

#innovation #musk #thiel #nikolatesla #science

Mbingu @[email protected] · 2026-05-04 · 12:57 UTC

Thiel and musk complain about a lack of Innovation, what if the next disruptive lot of scientific discovery depends on us becoming reasonable human beings? Just like Tesla said his brain is just a receiver of cosmic knowledge. He received what humanity was ready for at his time.
Society first has to establish the requirements of the next phase.
One thing is clear it has to be a

#innovation #musk #thiel #nikolatesla #science

Connect Paranormal @[email protected] · 2026-04-22 · 11:30 UTC

Nikola Tesla, a renowned inventor, is linked to the intriguing idea of a time machine. Despite his pioneering work in electricity and magnetism, the notion of Tesla creating a time machine remains speculative. #timemachine #nikolatesla https://connectparanormal.net/2024/03/08/teslas-time-machine-fact-or-fiction/

#timemachine #nikolatesla

Connect Paranormal @[email protected] · 2026-04-22 · 11:30 UTC

Nikola Tesla, a renowned inventor, is linked to the intriguing idea of a time machine. Despite his pioneering work in electricity and magnetism, the notion of Tesla creating a time machine remains speculative. #timemachine #nikolatesla https://connectparanormal.net/2024/03/08/teslas-time-machine-fact-or-fiction/

#timemachine #nikolatesla

Mojo ♻️ @[email protected] · 2026-04-13 · 22:15 UTC

America Had Free Electricity Before 1900 — Then One Family Bought the Grid

#nikolatesla #energy #capitalism #publicownership

https://www.youtube.com/watch?v=UpZz2iMQkDU

#nikolatesla #energy #capitalism #publicownership

Mojo ♻️ @[email protected] · 2026-04-13 · 22:15 UTC

America Had Free Electricity Before 1900 — Then One Family Bought the Grid

#nikolatesla #energy #capitalism #publicownership

https://www.youtube.com/watch?v=UpZz2iMQkDU

#nikolatesla #energy #capitalism #publicownership

E.S. Raye @[email protected] · 2026-04-09 · 16:12 UTC

The MC of my scifi/WWI epic Shallow Trenches/Open Skies was majoring in electrical engineering at West Point when the US entered World War I in 1917.
What better person for him idolize than Nikola Tesla?

#nikolatesla #scifi #sciencefiction #bookstadon #wardenclyffe #books #writing

#nikolatesla #scifi #sciencefiction #bookstadon #wardenclyffe #books

E.S. Raye @[email protected] · 2026-04-09 · 16:12 UTC

The MC of my scifi/WWI epic Shallow Trenches/Open Skies was majoring in electrical engineering at West Point when the US entered World War I in 1917.
What better person for him idolize than Nikola Tesla?

#nikolatesla #scifi #sciencefiction #bookstadon #wardenclyffe #books #writing

#nikolatesla #scifi #sciencefiction #bookstadon #wardenclyffe #books

(Roughly) Daily @[email protected] · 2026-04-04 · 08:00 UTC

“The earth is bountiful, and where her bounty fails, nitrogen drawn from the air will refertilize her womb.”*…

As the Iran War continues to unfold, there is understandably a great deal of concern about energy prices (and the prices of things that depend on energy). We might forget that the Middle East is also crucial to the world’s fertilizer supply– though not for long, as farmers (along with everyone else in the food chain, all the way down to all of us eaters) are beginning to feel the pain.

But, as Diana Kruzman reports, even as fertilizer trade concerns are growing, a revolutionary sourcing alternative has emerged– one that could make a huge positive difference if it proves out at scale…

The world has an almost insatiable demand for nitrogen. Crops need it to grow, but although it makes up 78 percent of our atmosphere, plants can’t just pull it in from the air the way they do with oxygen. Instead, they rely on bacteria in the soil to convert it into nitrate, a form they can use; in the case of agriculture, think of fertilizer spread by humans. Leaving aside organic options like cow manure, most farmers use ammonia produced mainly from natural gas using a technique called the Haber-Bosch process, which was invented in 1909. [See also here.]
Haber-Bosch is expensive and energy-intensive, responsible for up to two percent of the world’s annual greenhouse gas emissions. It’s also spurred a global nitrogen pollution crisis; as much as two-thirds of nitrogen fertilizer applied to crops is never used, and the excess escapes into the soil, air, and water, raising the cancer risk in nearby communities and contributing to climate change.
Researchers have been trying to find an alternative way to get nitrogen to plants for decades — turning to everything from microbes to human urine. But so far, these scientific advancements haven’t translated into much practical change for farmers, who for the most part still rely on ammonia (which, granted, is getting greener, but is increasingly vulnerable to global price shocks).
That could soon change with the growth in popularity of a new technology known as plasma activated water, or PAW. Around the U.S., scientists and startups are experimenting with this high-tech solution, which uses electricity to pull nitrogen from the air, mix it with water, and create fertilizer straight on the farm. The concept, on the surface, seems suspiciously rosy — on-demand nitrogen, in a form plants can use, at just the cost of electricity (and the initial price of the machine used to make it). But early adopters have told Offrange that it genuinely works…
… PAW uses electricity to transform air into plasma — the fourth state of matter (besides gases, solids, and liquids), which typically forms at high temperatures. When the plasma comes into contact with water, it encourages chemical reactions that form nitrates — the type of nitrogen that plants need. Though this process was actually invented in 1903, even before Haber-Bosch, it required so much energy that it never achieved widespread use.
But in recent years, those energy needs have gone down thanks to the development of “cold plasma” technology, which operates at less than 60 degrees Fahrenheit. It’s also used for medical sterilization and food safety, and over the last decade researchers have worked to develop new ways to apply it for agricultural production…

More at: “Pulling Nitrogen From the Air” from @dkruzman.bsky.social.

* Nikola Tesla (who, around 1900, imagined and experimented with something like the Birkeland–Eyde-based plasma process described above)

###

As we count on creativity, we might send healthy birthday greetings to a man who explained one of the central ways in which we depend on the food that we eat, William Cumming Rose; he was born on this date in 1887. A biochemist, he researched amino acids, discovered threonine, and established the importance of the nine essential amino acids in human nutrition (that’s to say, the amino acids that our bodies cannot synthesize and that we must consume in our food). He received the National Medal of Science in 1966.

source

#agriculture #aminoAcid #aminoAcids #biochemistry #culture #farming #fertilizer #history #MiddleEast #NikolaTesla #nitrogen #nutrition #Science #Technology #war #WilliamCummingRose #WilliamRose

#agriculture #aminoacid #aminoacids #biochemistry #culture #farming

(Roughly) Daily @[email protected] · 2026-04-04 · 08:00 UTC

“The earth is bountiful, and where her bounty fails, nitrogen drawn from the air will refertilize her womb.”*…

As the Iran War continues to unfold, there is understandably a great deal of concern about energy prices (and the prices of things that depend on energy). We might forget that the Middle East is also crucial to the world’s fertilizer supply– though not for long, as farmers (along with everyone else in the food chain, all the way down to all of us eaters) are beginning to feel the pain.

But, as Diana Kruzman reports, even as fertilizer trade concerns are growing, a revolutionary sourcing alternative has emerged– one that could make a huge positive difference if it proves out at scale…

The world has an almost insatiable demand for nitrogen. Crops need it to grow, but although it makes up 78 percent of our atmosphere, plants can’t just pull it in from the air the way they do with oxygen. Instead, they rely on bacteria in the soil to convert it into nitrate, a form they can use; in the case of agriculture, think of fertilizer spread by humans. Leaving aside organic options like cow manure, most farmers use ammonia produced mainly from natural gas using a technique called the Haber-Bosch process, which was invented in 1909. [See also here.]
Haber-Bosch is expensive and energy-intensive, responsible for up to two percent of the world’s annual greenhouse gas emissions. It’s also spurred a global nitrogen pollution crisis; as much as two-thirds of nitrogen fertilizer applied to crops is never used, and the excess escapes into the soil, air, and water, raising the cancer risk in nearby communities and contributing to climate change.
Researchers have been trying to find an alternative way to get nitrogen to plants for decades — turning to everything from microbes to human urine. But so far, these scientific advancements haven’t translated into much practical change for farmers, who for the most part still rely on ammonia (which, granted, is getting greener, but is increasingly vulnerable to global price shocks).
That could soon change with the growth in popularity of a new technology known as plasma activated water, or PAW. Around the U.S., scientists and startups are experimenting with this high-tech solution, which uses electricity to pull nitrogen from the air, mix it with water, and create fertilizer straight on the farm. The concept, on the surface, seems suspiciously rosy — on-demand nitrogen, in a form plants can use, at just the cost of electricity (and the initial price of the machine used to make it). But early adopters have told Offrange that it genuinely works…
… PAW uses electricity to transform air into plasma — the fourth state of matter (besides gases, solids, and liquids), which typically forms at high temperatures. When the plasma comes into contact with water, it encourages chemical reactions that form nitrates — the type of nitrogen that plants need. Though this process was actually invented in 1903, even before Haber-Bosch, it required so much energy that it never achieved widespread use.
But in recent years, those energy needs have gone down thanks to the development of “cold plasma” technology, which operates at less than 60 degrees Fahrenheit. It’s also used for medical sterilization and food safety, and over the last decade researchers have worked to develop new ways to apply it for agricultural production…

More at: “Pulling Nitrogen From the Air” from @dkruzman.bsky.social.

* Nikola Tesla (who, around 1900, imagined and experimented with something like the Birkeland–Eyde-based plasma process described above)

###

As we count on creativity, we might send healthy birthday greetings to a man who explained one of the central ways in which we depend on the food that we eat, William Cumming Rose; he was born on this date in 1887. A biochemist, he researched amino acids, discovered threonine, and established the importance of the nine essential amino acids in human nutrition (that’s to say, the amino acids that our bodies cannot synthesize and that we must consume in our food). He received the National Medal of Science in 1966.

source

#agriculture #aminoAcid #aminoAcids #biochemistry #culture #farming #fertilizer #history #MiddleEast #NikolaTesla #nitrogen #nutrition #Science #Technology #war #WilliamCummingRose #WilliamRose

#agriculture #aminoacid #aminoacids #biochemistry #culture #farming

Einsttein @[email protected] · 2026-04-03 · 08:07 UTC

🚨 NUEVO ARTÍCULO EN SUBSTACK SOBRE UN GENIO ADELANTADO A SU TIEMPO (TESLA)

La historia no siempre la escriben los genios…
A veces la escriben quienes supieron vender mejor su versión.

Acabo de publicar un análisis sobre Nikola Tesla.

No es solo historia.
Es una lección sobre poder, narrativa y legado.

https://substack.com/@einsttein/note/p-193037496?r=3gcswp

⚡ Entender esto cambia cómo ves el mundo… y a quienes lo controlan.

¿Genio incomprendido… o víctima del sistema?

#NikolaTesla
Follow @einsttein_

#nikolatesla

A∂α Lσνєℓαcє @[email protected] · 2026-03-19 · 21:38 UTC

:stargif: 𝑵𝒊𝒌𝒐𝒍𝒂 𝑻𝒆𝒔𝒍𝒂: 𝒆𝒍 𝒏𝒊𝒏̃𝒐 𝒒𝒖𝒆 𝒏𝒂𝒄𝒊𝒐́ 𝒆𝒏 𝒖𝒏𝒂 𝒕𝒐𝒓𝒎𝒆𝒏𝒕𝒂 𝒚 𝒎𝒖𝒓𝒊𝒐́ 𝒔𝒐𝒍𝒐 𝒆𝒏 𝑵𝒖𝒆𝒗𝒂 𝒀𝒐𝒓𝒌 :stargif:

En la noche del 10 de julio de 1856, durante una fuerte tormenta eléctrica, nació Nikola Tesla en el pequeño pueblo de Smiljan, entonces parte del Imperio austrohúngaro (hoy Croacia).

Según la tradición familiar, un relámpago iluminó el cielo justo en el momento de su nacimiento.
La comadrona lo interpretó como un mal augurio.
Su madre respondió: “No, será un hijo de la luz”.
Con el tiempo, la frase quedó como una de esas coincidencias que parecen escritas después.

Su padre, Milutin Tesla, era sacerdote ortodoxo.
Esperaba que su hijo siguiera el mismo camino.
Su madre, Đuka Tesla, no tenía educación formal, pero era extraordinariamente ingeniosa: diseñaba herramientas domésticas y tenía una memoria prodigiosa.
De ella, Tesla heredó la capacidad de visualizar mecanismos complejos en su mente antes de construirlos.

La infancia de Tesla no fue sencilla.
Tenía una imaginación desbordante, acompañada de visiones intensas y una memoria casi fotográfica.
Podía recitar libros enteros y realizar cálculos mentales complejos.
Pero también desarrolló obsesiones y manías que lo acompañarían toda su vida.

Tenía una fijación con los números, especialmente el tres: repetía acciones, contaba pasos y seguía rutinas estrictas.
Evitaba el contacto físico y tenía una fuerte aversión a los gérmenes.

Su mente funcionaba de forma extraordinaria.
Era capaz de visualizar sus inventos con total precisión, probándolos mentalmente antes de construirlos.
Esa capacidad fue clave en su trabajo, pero también venía acompañada de hipersensibilidad a la luz y al sonido.

En sus últimos años, sus manías se intensificaron.
Desarrolló un vínculo muy fuerte con las palomas, en especial una blanca a la que decía querer profundamente.

No encajaba en su tiempo.
Pero quizá esa misma forma de ser fue lo que le permitió imaginar un mundo que aún no existía.

Uno de los episodios que más le marcó fue la muerte de su hermano mayor, Dane, en un accidente.
Tesla tenía solo cinco años. Durante años, vivió bajo la sombra de ese hermano considerado “más brillante”, lo que dejó una huella profunda en su carácter.

Estudió ingeniería en Graz y más tarde en Praga, aunque nunca llegó a terminar formalmente sus estudios.
Lo que sí desarrolló fue una capacidad técnica fuera de lo común. Trabajó en Europa antes de dar el salto decisivo: en 1884 llegó a Estados Unidos con poco dinero y una carta de recomendación para Thomas Edison.

Aquella relación no terminó bien.

Tesla defendía la corriente alterna; Edison, la continua.
La rivalidad entre ambos acabó convirtiéndose en una guerra tecnológica.
Finalmente, Tesla encontró apoyo en George Westinghouse, y juntos lograron imponer la corriente alterna como el sistema dominante.
Fue un cambio que literalmente iluminó el mundo.

A partir de ahí, Tesla encadenó avances: motores eléctricos, la bobina de Tesla, investigaciones clave para la radio… Pero también empezó a alejarse del terreno práctico.
Sus proyectos eran cada vez más ambiciosos, más caros… y más difíciles de financiar.

Nunca se casó.
No tuvo hijos.

Él mismo afirmaba que su trabajo exigía una dedicación absoluta.
Con los años, su vida se volvió más solitaria.
Sus hábitos se volvieron más rígidos, casi obsesivos.
Evitaba el contacto físico, contaba pasos, repetía rutinas.
Y, aun así, seguía pensando a lo grande.

Uno de sus grandes fracasos fue la torre de Wardenclyffe, financiada inicialmente por J. P. Morgan.
Tesla quería transmitir energía y comunicaciones sin cables a escala global.
El proyecto quedó inacabado cuando se retiró la financiación.

A partir de ahí, su figura cambió.
De pionero respetado pasó a ser visto como un excéntrico.

Y así llegamos al final.

El 7 de enero de 1943, en la habitación 3327 del Hotel New Yorker, murió Nikola Tesla, solo.

Tras su muerte, el gobierno estadounidense revisó sus documentos bajo supervisión del FBI, preocupado por las posibles aplicaciones militares de sus investigaciones en plena guerra.

Días después, miles de personas asistieron a su funeral en Catedral de San Juan el Divino.
No fue un olvido absoluto.
Pero sí un reconocimiento que llegó tarde.

Tesla murió sin fortuna, sin familia cercana, sin ver hasta qué punto sus ideas habían cambiado el mundo.

Hoy su nombre está en todas partes.

Pero su historia sigue siendo incómoda: demuestra que el talento no garantiza el reconocimiento… al menos, no en vida.

▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣

#historia #nikolatesla #inventores #historiadelaciencia #ciencia #curiosidadeshistoricas #memoriahistorica #tecnologia

#historia #nikolatesla #inventores #historiadelaciencia #ciencia #curiosidadeshistoricas