In 1839, J.M.W. Turner painted The Fighting Temeraire. The old warship, once a hero of the Battle of Trafalgar in 1805, glides like a ghost across the canvas, towed by a small steam tug belching smoke on its final voyage to the ship-breakers. The image shows a clear moment of change: sail giving way to steam, and with it, a major shift in power. The ship relied on timber, rope, canvas, and Britain’s seafaring towns. The tug depended on coal mines and iron foundries that supplied machine shops in the Midlands. Turner showed the tension of this time, when new technology changed who held power.

By Turner’s time, the United States had already defeated Britain’s navy in two wars—one for liberty on land, another for freedom of the seas. The 13 colonies used new technology in creative ways to win their freedom, and by keeping up with innovation, they managed to defend their freedom. Now, as the U.S. celebrates its 250th anniversary, we can ask: What does it really mean for a country to be independent?

We tend to focus on how nations and individuals defend freedom but rarely turn that focus to the tools and systems that sustain freedom. Declaring independence is only the beginning: Independence must still be engineered.

Forging freedom

Long before the first shots were fired at Lexington and Concord in 1775, Britain had drawn the lines of conflict through technology. The Wool Act of 1699 choked colonial textile exports. The Hat Act of 1732 crushed local hat-making. The Iron Act of 1750 forbade finished iron goods. Each statute tightened the knot: Colonial capability existed only at Britain’s discretion. The Boston Tea Party may have been a loud response, but resistance also took subtler, more empowering forms. At a 1769 Virginia ball, more than a hundred women arrived in homespun gowns. Every thread was defiance.

When war came, everyday tradespeople pivoted to the fight. Farmers turned plowshares into gun barrels, while clockmakers turned their precision skills to making firing mechanisms. By 1777, two weapons production models had emerged—centralized sites like the Springfield Armory that could produce high-quality guns in large quantities, and household workshops that were more agile and could meet local needs. In parallel, the new nation developed an equally important source of supplies and support: France sent gunpowder and loans and eventually opened a second naval front in 1781, which proved as decisive as any weapon.

After the war, the young republic pursued industrial strength with the same resolve it had shown in battle. In 1789, Samuel Slater arrived from England with textile-spinning technology that he’d memorized, sowing the seeds of U.S. manufacturing, whose early growth rested on domestic cotton, slave labor, and copied techniques. By 1816, gun manufacturer Simeon North’s milling machines were producing interchangeable metal parts, allowing the armed forces to cannibalize parts. In 1822, Thomas Blanchard’s copying lathe automated the shaping of gunstocks. In the 1830s, the federal government imposed tariffs that shielded infant industries, fulfilling Alexander Hamilton’s vision for industrial policy: Build capacity first, then compete.

At the 1851 Great Exhibition in London, American revolvers and reapers with swappable parts stunned international observers. By the 1860s, land-grant colleges were spreading technical education across the nation. Engineering moved into the mainstream, from niche to national necessity, driving broad, though uneven, prosperity. As the Industrial Revolution bloomed, the early U.S. focus on industrial capacity via farms, factories, and formidable wealth positioned the country to compete with the most advanced industrial powers in the world.

The right and responsibility to repair

For nearly two centuries, that ethos endured, with government-guided infrastructure and markets deciding the details. But around the U.S. bicentennial, in 1976, a conviction took hold across party lines. Finance began to outrank fabrication, and Wall Street prioritized futures contracts over companies owning the factories that made up their supply chains. Domestic factories closed or moved offshore, and companies turned to just-in-time manufacturing and shipping, ostensibly as a way to save on costs. Shipbuilding felt this shift as much as any industry. Shipyards closed, and suppliers of specialized castings and components disappeared along with them, as did skilled technical workers who retired without replacement. Now the U.S. Navy struggles to build submarines fast enough to replace its aging fleet.

Other changes took hold, among them the idea that the company that builds your tractor or medical equipment could prevent you from fixing it yourself. Invasive “terms of service” prevented customers from reaching for a wrench, instead allowing companies to keep reaching into customers’ pockets. These changes are symptoms of both structural and infrastructural fragility. When we lose the ability to understand and sustain the systems we rely on, we lose control—bit by bit.

RELATED: Why We Must Fight for the Right to Repair Our Electronics

No nation can build everything alone, of course. From hand-forged muskets to finely printed microchips, the sovereignty etched into our tools demands a prudent calculus: what to make at home, what and with whom to trade. Engineering is how a nation keeps its independence alive. Independence requires both the courage to innovate and the stewardship to maintain what has been built. The American Revolution was itself an act of engineering—daring in vision and deliberate in pairing anvil and alliance. Generations later, can a nation that cannot see its own dependencies, build and maintain its critical tools, or repair what breaks still call itself free?

Turner’s Snow Storm—Steam-Boat off a Harbour’s Mouth, completed three years after The Fighting Temeraire, captures this part of the story. Sea and sky dissolve into a churning vortex around the ship. Turner claimed he had himself lashed to the ship’s mast for four hours so that he could paint the sensation of standing inside a system too vast and tangled to comprehend. A nation that loses sight of what it depends on stands there too: lashed to nothing except the churn.

Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why.

Dan Page

To be honest, we were amazed. Dozens of research groups and companies all around the world have spent many millions of U.S. dollars over the past 20 years trying to emulate these neural behaviors using experimental memristor-like devices and other things, with limited success, mainly due to reliability and cost issues. We managed it in the cheapest and most industry-standard device: the MOSFET. This result was so shocking that we decided to confirm it using microchips from a different foundry. It was successful: All the behaviors could be reproduced, and perfect yield was achieved once again.

We were happy with the results and had started the process of filing for a patent and writing up our findings for the journal Nature, when our lab made another astonishing discovery: The same kind of MOSFET could act as a synapse, too!

Recall that in ordinary operation some electrons crash into silicon atoms to create pairs of electrons and holes. We noticed that at specific values of bulk resistance a significant amount of the charge from this impact ionization would get trapped in the gate dielectric. This trapped charge interferes with the flow of current through the MOSFET, effectively changing the device’s conductance. Importantly, this new conductance is stable and adjustable at will. It was then that we realized the MOSFET could also be used as an electronic synapse.

As it was in the neuron transistor, the bulk terminal was the key. A negative bulk-source voltage drives electrons into the dielectric, decreasing conductance. A positive one pushes holes in, increasing it.

From neuromorphic device to circuit to system

Here’s how the MOSFET synapse and the MOSFET neuron, together called a neurosynaptic random-access memory, or NSRAM, could work together to achieve a simple neural circuit: Say you had a circuit consisting of three synapse MOSFETs and a neuron MOSFET. The synapses have already been programmed as we’ve described, so that each has a different conductance. Spikes of voltage with different patterns and frequencies are applied to the gate of each of these transistors. What emerges from their drains are spikes of current with amplitudes modulated by the synapses conductance values.

The spikes converge at the drain of the neuron MOSFET. With each spike, impact ionization causes charge to build in the bulk of the silicon. Some of it will drain away, but if enough spikes arrive in a short enough period of time, the bulk voltage will reach a value at which the “hidden” transistor triggers a spike of current through the MOSFET. This current would then go on to become the input to other MOSFET synapses, and so on. The behavior is exactly the kind of integrate-and-fire action real neural circuits deliver.

The competitive advantage of our single-MOSFET electronic neurons and synapses is straightforward: We can produce with only one or two transistors the electronic signals that today require, at an industrial level, dozens and sometimes even hundreds of components. And moreover, unlike other emerging technologies, our solution is fully compatible with today’s silicon manufacturing lines and exhibits a yield of 100 percent in key figures of merit with near-zero variability.

Building functional circuits for brain-inspired computing and AI based on this technology is as exciting as it is laborious. It will require us to improve our computer models to resemble the behavior of both devices more accurately and to do so with computational efficiency. We must also perform accurate circuit- and system-level simulations to validate computing architectures, design peripheral circuitry to drive and convert signals, and undergo multiple fabrication rounds to optimize performance.

But all that will be worthwhile, because it could result in brain-inspired microchips for AI with better energy efficiencies than what we have now. These chips will first be a fit for smaller-scale, “edge-AI” tasks, such as bringing greater intelligence to battery-powered systems. But if we can scale up such chips, maybe in the long run they can compete with state-of-the-art GPUs.

Sylvie Andrews and her partner didn’t just lose the new house they’d helped build when the Eaton Fire ripped through Altadena, California, in January 2025. They lost an entire decade’s worth of sacrifices they’d made to put down roots in their hometown, and the community they’d created. “We put a lot of blood, sweat, and tears into it,” Andrews said. “That’s what we lost in the fire.”

That fire, along with the Palisades Fire to the west, destroyed more than 16,000 structures and killed 31 people. But while Andrews and thousands of Angelenos were racing to evacuate, other people saw a financial opportunity. Using Polymarket, the world’s largest prediction market platform, they made bets on the fires—how they would grow, how long they would last, and how much they would destroy.

Prediction markets are essentially gambling websites where people bet on the outcome of events, including elections, sports, the weather, and more. Anything is fair game, from oil prices and the spread of infectious diseases to international incidents. Markets usually frame questions in a “yes” or “no” fashion, with the price of a “contract” fluctuating between $0 and $1. A price of 50 cents on a “yes” contract means that the people doing the betting collectively believe the event has a 50 percent chance of happening. Market hosts make money by charging a fee on wagers.

In January 2025, Polymarket listed almost 20 questions, created by the platform’s “markets team,” related to the wildfires burning up Southern California. How many acres will the Palisades Fire burn by Friday, three days after it ignited on a Tuesday? Will the Palisades Fire reach Santa Monica by Sunday? When will the Palisades fire be 50 percent contained? Will the Palisades and Eaton fires be contained before February?

People spent $1.2 million betting on these queries, according to Aeon Magazine. “Wow,” Andrews said repeatedly when she learned the figure. “My first take is that it’s morally reprehensible,” she said. “The fact that someone would feel OK doing that flabbergasts me.”

“The prediction markets are just the wild, wild West,” said Susan Sherman, who grew up in Pacific Palisades. She lost her childhood home in the Palisades Fire; her late parents had owned it since 1963, and now it was gone. She sold the empty lot a few months ago. “I look at (betting on the fires) as just being very crass and heartless.”

As prediction markets boom and a new wildfire season begins, fire survivors and ethicists say that the betting encourages and rewards callous thinking—and dangerous behavior.

One major concern stemming from wildfire prediction markets is arson. “That’s what has me nervous,” Sherman said. Theoretically, making a bet could give someone the perverse incentive to start a fire or help one grow. Unlike other disasters, such as hurricanes, flooding, or extreme heat, a fire can be manipulated in minutes by just one person. “Systems that tie financial gain to wildfire outcomes risk encouraging misuse, including arson, and are not compatible with our mission,” a spokesperson for the US Forest Service said.

“Imagine what a bad actor might do,” said Ann Skeet, the senior director of leadership ethics at the Markkula Center for Applied Ethics at Santa Clara University. “A market that might support that kind of activity, I think, is a dangerous market.” Firefighters or land managers with exclusive information about a fire’s behavior or an agency’s firefighting plans could even be tempted to bet on a fire, which would be considered insider trading.

He was born into a storm, lightning split the summer sky, in a
village the world had not yet heard of.
The midwife called it a bad omen, his mother called it a sign. Your first
life began in a storm, under open sky.

One winter night you ran your hand along a cat’s back, and the
darkness cracked open with sparks.
Your mother warned the house could burn.
You were already chasing what you learned: Light would return.

Your second life came underwater, in the current deep. No light,
no air, the river pulling you under,
the surface closing above you without a sound, and
something in you refused to sink or sleep.

Your third life came at the dam.
The water rose. The wall held you in place.
One flash, you turned your body and rose back into air, and left
the weight of water without a trace.

Your fourth life came in stone and dark. Entombed for a
night in a mountain chapel,
visited by no one. Only silence and the memory of a spark. You called
it an awful experience and left it there, untold.

Your fifth life came in fever,
nine months cholera held you down,
until your father said: Survive, and choose your own ground. You rose.
Not from the prayer, but from the promise he made.

Your sixth life came in silence, and it stayed.
Every sound cut through you, a clock three rooms away,
a ringing that would not leave, a noise you learned to bear, until you
lived inside that noise and made a home in there.

Your seventh life burned on Fifth Avenue, not your body, but your work. Not a thief
of fire, but one who stayed with the blaze.
A modern Prometheus, your life’s work turned to ash,
“I must begin again,” you said, and turned to new ways.

Your eighth life came in the street.
No storm. No warning. A taxi struck without a sign. A
sudden impact: ribs breaking, breath gone.
No diagram this time. Only the body, slow to keep up.

The ninth life came on quiet wings.
That dove found you in the dark, and your spirit rose. She did
not move. A beam of light fell from above.
The life you would not return from, the one you loved.

Your mother thought you had nine lives, nine close
brushes with death.
Each close call, a lesson. A hand that would lead you out of the
darkness and into the dynamo of eternal light. The world profits
from the mystery of your mind,
Upon your imagination we stand.

While many TVs released between 2010 and 2015 supported 3D, using the feature required clearing a series of annoying hurdles. You had to buy 3D glasses, which ranged from $10 to $20 for passive frames, to upwards of $50 for active glasses that required constant charging. You had to make sure your Blu-ray player supported 3D discs. And you had to pay a premium for those 3D Blu-rays, assuming you could find them in stock.

For the niche media geeks who cleared those roadblocks, 3D Blu-rays did a decent job of replicating the theatrical 3D experience. But the results depended heavily on the size and viewing distance of your TV. If you’re too far away from a 42-inch or even 50-inch set, you won’t really be immersed by Avatar’s world of Pandora. It was also extra annoying if you wanted to have a 3D watch party with a crowd — you’d either have to buy a ton of extra glasses, or hope your nerdy friends had their own.

Worst of all, 3D TVs with passive glasses effectively halved the resolution of 1080p, since they had to deliver a separate image. 3D projectors and higher-end TVs avoided that issue since they relied on active glasses, but the expense and battery limitations of those frames made viewing parties all but impossible.

Outside of 3D Blu-rays, it was also tough to find much 3D content. Networks like the BBC and ESPN broadcast a handful of 3D shows and games, but they both gave up on the format in 2013. “I have never seen a very big appetite for 3D television in the UK,” Kim Shillinglaw, the BBC’s head of 3D, said in a 2013 interview with Radio Times (via The Independent). “Watching 3D is quite a hassly experience in the home. You have got to find your glasses before switching on the TV. I think when people watch TV they concentrate in a different way. When people go to the cinema they go and are used to doing one thing. I think that’s one of the reasons that take up of 3D TV has been disappointing.”

As the hype around 3D TVs waned, 4K sets with HDR started to crop up with more immediate benefits. They looked noticeably sharper and brighter than earlier HDTVs, and they were buoyed by a ton of 4K content from Netflix and other streaming services. There was no need to buy a Blu-ray player, no need to put on glasses and no need to look hard for special content. It’s no wonder 4K took off. (And even if you’re not viewing 4K content, those newer TVs still made your older SD and HD shows look better than ever.)

According to a recent study by Precision Reports, around 25 percent of households with 3D TVs actually used the technology during the peak period between 2010 and 2018. Less than 10 percent of households kept using the technology after three years. The same report also found that 65 percent of users stopped using 3D because of a lack of content, 50 percent noted discomfort for long viewing sessions and 42 percent gave up due to high equipment costs.

Despite the many issues, though, Precision Reports also predicts that the 3D TV category will grow by 15 percent by 2036 thanks to the rise of glasses-free 3D sets, commercial implementations and gaming. I’ve yet to be impressed by any glasses-free 3D TVs, personally, and they typically don’t support multiple viewers since they rely on sophisticated eye tracking to function.

These may be the last days of Amazon’s Mechanical Turk.

An announcement on the Mechanical Turk website says that on July 30, 2026, the crowdsourcing service will close to new customers. Amazon Web Services says the decision was made after “careful consideration,” adding, “Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Mechanical Turk, but we do not plan to introduce new features.”

In other words, Amazon isn’t completely pulling the plug, but the service is very much on life support.

First launched in 2005, Mechanical Turk was a marketplace where people were paid tiny amounts to perform simple tasks that resisted full automation — things like completing CAPTCHA challenges or identifying the basic sentiment in a sentence.

In its heyday, the service was at the center of debates around the ethics of crowdsourced labor, and it even played a small role in the early stages of the Facebook-Cambridge Analytica scandal. 

Beginning in 2018, Amazon also began billing it as a way for companies to annotate data to train neural networks as part of its SageMaker AI service.

Less overtly, Mechanical Turk has also been described as the hidden enabler for companies taking a fake-it-till-you-make-it approach to AI, where products marketed as Ai are actually being performed by the Mechanical Turk workforce — all the more fitting since the original Mechanical Turk was itself a hoax, with a hidden human chess player pretending to be a chess-playing machine

Over time, the relationship between Mechanical Turk and AI models grew even more complicated. In a snake-eating-its-own-tail irony, a 2023 analysis found that between 33% and 46% of workers on the platform were using large language models to complete their tasks, raising questions about the reliability of data annotated on the platform and also about whether humans needed to be in the loop at all.

This week, after Amazon’s decision became public, one Reddit user suggested the platform died “years ago,” with workers and researchers abandoning it due to bots and fraud. The user predicted, “Someone at Amazon is going to decide keeping the Mturk servers running is a waste of time and resources and pull the plug entirely.”

Erling Haaland’s Viking warriors set sail for New Jersey as Norway battle five-time winners Brazil for a place in the FIFA World Cup 2026 quarter-finals — and you can live stream the last-16 clash around the world for free.

Unsurprisingly, Stale Solbakken’s men have relied heavily on the goals of talismanic striker Haaland on their run to the last 16. The Manchester City star bagged an 86th-minute winner against Ivory Coast in the round of 32 and remains the Vikings’ biggest attacking threat, scoring exactly half of their 10 goals at the tournament. How Haaland fares against Brazil’s defence – particularly Arsenal defender Gabriel, who he has clashed with in the Premier League – could well be the defining storyline of this match. Interestingly, Norway have never lost to A Selecao in men’s international football and secured a famous 2-1 victory at the 1998 World Cup.

Donald Trump arrives at next week’s NATO summit in Ankara holding unusual leverage, because the US decides which allies get access to the world’s most advanced AI, Politico reports. The alliance meets on 7 and 8 July with AI security questions hovering over the agenda.

A new wave of models from Anthropic and OpenAI can find and exploit security flaws better than most human specialists. Anthropic’s Claude Mythos surfaced vulnerabilities in classified US systems within hours during a government test.

“AI is fundamentally changing the threat landscape, and NATO needs to adapt accordingly,” Estonian cyber ambassador Helen Popp told Politico. Every capability available to adversaries is also available to allies, she argued, if they move first.

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US agencies including the NSA and CISA have been testing Mythos for cyber defence and digital espionage. European allies have clamoured for access, and EU institutions have openly demanded it, with only a few countries, including the UK, initially allowed to run evaluations.

Anthropic expanded its Project Glasswing programme in June to around 150 organisations across more than 15 countries, including the EU. The scramble followed weeks of whiplash from Washington.

In early June, the Trump administration imposed export controls on Anthropic’s most cyber-capable models, banning foreign nationals from using them and forcing a worldwide shutdown. The controls were lifted on 30 June after an 18-day blackout.

The White House has also limited the rollout of OpenAI’s latest model to a small group of approved US firms, per Politico. The push and pull has frustrated allies, prompted a rare Five Eyes warning on AI cyber threats, and left frontier models moving between governments faster than regulators can follow.

Quiet corridors, loud subtext

The summit agenda includes a track on emerging and disruptive technologies, but an official told Politico that AI and cyber will get only brief mentions in the closing statement. Former NATO cyber policy leader Heli Tiirmaa-Klaar said allies avoid formally discussing topics that lack consensus, predicting talks in the margins instead.

The US State Department’s cyber bureau is not sending a representative amid an internal reorganisation, Politico reports. Senator Jeanne Shaheen said she will attend partly to reassure allies that the US will not “alienate them” over access to AI models.

Trump has separately signed NSPM-11, ordering the US military to adopt AI faster and shield models from China. Europe is hedging by building its own capability, including the defence AI alliance between Helsing and Mistral.

The war in Ukraine, now past its fourth year, keeps the stakes concrete, and allies have pledged 1.5% of GDP to protecting critical infrastructure. Laura Galante of the Center for European Policy Analysis called Ukraine the blueprint for operating in AI-fuelled warfare.

A State Department spokesperson said every ally must adopt “trusted leading-edge AI capabilities”. Which capabilities count as trusted, and who grants the trust, is precisely what Ankara will not quite discuss.