Why Trump is pushing tech that can break everything — and fix it again



The president’s executive order "Ushering in the Next Frontier of Quantum Innovation" arrived in language that sounded both urgent and inevitable. Americans have always needed a frontier. Close the continental one and you get the endless frontier of Vannevar Bush, who argued in 1945 that basic science was the territory left to settle.

Now, 81 years later, the covered wagons are cryostats ... and the territory is quantum.

Hype is a strategic tool for securing funding.

And what, exactly, is quantum? We'll get to that.

President Trump signed executive order 14413 on June 22, 2026. The stated goal is to make the United States the world’s quantum “superpower,” a word that does not appear in the order itself but in the accompanying fact sheet, in which ambitions are permitted to be louder. The order proper is more bureaucratic and more interesting, directing the creation of a national quantum strategy within 180 days and demanding technical specifications for a scientifically transformative quantum computer within 90 days. It orders sensor deployment plans extending to September 2028 and establishes the Quantum Computer for Application Development and Discovery Science effort, which aims to deliver at least one machine to a Department of Energy facility where real scientists can use it for real problems. The order is less a building than a set of blueprints.

Federal quantum policy is actually not new. Spending on quantum information science rose from $456 million in fiscal year 2019 to over a billion by 2023. The 2018 National Strategic Overview had already laid out grand challenges, workforce initiatives, and international cooperation frameworks. A 2009 vision document warned, with admirable prescience, that quantum technologies could render existing data-protection schemes obsolete. The new order tightens this apparatus. The posture shifts from encouragement to expectation, from “let us coordinate” to “let us specify what we are building and whether it works.”

Confidence or con?

Quantum computing has suffered from a particular condition: The distance between what is promised and what is delivered is large, and the people doing the promising know it. Physicists have acknowledged, in terms more candid than one might expect, that hype is a strategic tool for securing funding. DARPA, not an institution given to sentiment, created a Quantum Benchmarking Initiative whose purpose is to determine whether anyone’s machine can actually do something worth its cost by 2033. The executive order’s insistence on specifications, benchmarking centers, and public assessment institutions is therefore a response to a credibility problem. The government is trying to build institutions that can tell the difference between a breakthrough and a press release.

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The underlying physics explains why quantum computing is so difficult. A qubit, the fundamental unit of quantum information, can exist in a superposition of states, which permits certain computations to proceed in ways unavailable to classical machines. The trouble is that qubits are extraordinarily fragile. They decohere; their gates fail; their measurements are imperfect. The central engineering challenge is error correction: combining many noisy physical qubits into fewer, cleaner logical qubits whose effective error rates are low enough to be useful. Google reported a 101-qubit surface code that exceeded the lifetime of its best physical qubit by a factor of 2.4. Microsoft and Quantinuum demonstrated logical qubits with better-than-physical error rates on a trapped-ion processor.

These are genuine achievements, but are only way stations, and the order reflects an awareness that a useful quantum computer cannot be defined by qubit count alone. Instead, such a computer must be described in terms of gate fidelity, decoder latency, circuit depth, error-correction overhead, and performance on actual workloads. The romance of the blackboard gives way to the actuality of engineering and manufacturing.

The break and the fix

Trump's order treats quantum as a material civilization project, with supply-chain analyses, foundry access, domestic component development, and critical minerals, speaking of readiness, deployment, and trusted supply chains. Quantum policy is now a cousin of semiconductor policy, another domain in which the distance between theoretical capability and industrial reality turned out to be the whole story. The order also folds in workforce provisions: recruitment strategies, labor-market tracking, apprenticeships, and a network of national training institutes. Talent, chips, standards, and secrecy are treated as a single policy field.

There is a tension at the heart of the order. On the same day as the president’s signature, the White House issued a companion order on advanced cryptographic attacks. The reason is straightforward and unsettling. A sufficiently powerful quantum computer could break the public-key cryptography on which much of modern privacy, commerce, and infrastructure depends. NIST finalized its first three post-quantum cryptography standards in August 2024. Migration, the agency stresses, must begin now. The government is doing two things simultaneously: accelerating the technology that could undermine the current architecture of trust and building a new architecture of trust designed to survive the technology it is accelerating. The government has decided that the technology is too important to forgo and too dangerous to leave unsecured.

How to build a free internet



In February 1976, Bill Gates wrote an “open letter” to all those using home computers and sent it off to be published in a dozen computer-enthusiast zines. In the letter – barely a page long – Gates asks, “Will quality software be written for [home computer users]?” and tells users that the answer is up to them. The answer did not follow a philosophical musing nor an inspiring call to action. Instead, it was the equivalent of that peculiar “FBI Warning” at the beginning of movies: The people using his software were stealing his goods and needed to stop — end of discussion.

But what were they stealing exactly? Gates’ position was that his company’s software – in this case, Altair BASIC, or a piece of software enabling home users to write software – took time to write, required the labor of specialists, and that its theft would take bread out of the mouths of professional programmers’ children. The home computer users (or “hobbyists” as they were accurately called) were fundamentally stealing their time.

As Big Tech attempts to reduce humanity to machines emptied of dopamine and God, we depend on recognizing the simple tools that existed before it and still reside deep in its core.

Gates’ argument is familiar to Millennials in the form of Metallica’s harsh position toward super-fans downloading its music from file-sharing networks, but it also raises an important question: When you pay for an abstract work – an album, novel, or computer program – are you paying for some end-product, widget, or thing? Or are you paying for something else, more akin to an experience, a journey, a recipe, or a community?

Open source is an argument and movement of software writers, hardware developers, and other tinkerers who hold that whatever it is, it must certainly include the ability — or even the positive right — to study how it works, to modify it, and to share it. Computing itself would not exist without this predicate, just as music itself would not meaningfully exist without musicians studying, making, and sharing it.

The most prevalent software today is open source, and you may not know it. As you read this on your computer, you run thousands of interconnected programs and libraries, each produced by collaborative and independent efforts. Some were written on paper throughout development; others were distributed among developers using cassettes and floppy disks. Nowadays, their work, the code or recipes that influence how a computer behaves, is often published openly at repositories or places like GitHub and listed in innumerable directories. Open-source hardware and kits abound at sources like CrowdSupply, with much accessible to power users. No electrical engineering degree needed.

Open-source machines

Open-source software – used to generate documents that can be served to users through a web browser – powers perhaps a third of all websites today, ranging from personal blogs to major publications. The web browser you’re using may be Firefox, Chrome, or Brave, each substantially open source and composed of smaller units of open-source software. One such component of many thousands is SQLite, or an embedded database developed by people of a strong Christian ethic, which browsers may use to store your user settings on your hard drive.

None of this would get off the ground without a computer’s operating system, the servers hosting websites, or firmware enabling your power button to work. Much of the implicated software is open source or has an open-source variant or competitor, and the closed-source ones will depend on open-source pieces.

Open source means that the developer or hobbyist can study how all these work, guide the computer's process, and distribute what follows or some crucial aspect. Millions of developers can make tweaks or build upon such code, and billions of users can choose to learn and do the same. Anyone can access the code or recipe of the open-source program if they like, digging behind the vision projected on the screen. Present illiteracy in code need not foreclose future possibilities. Amateurs and professionals choose how far they wish to go and what value they seek to use and provide.

All this rests in some tension with copyright, which fundamentally must say that it is illegal to combine these words and to share this combination. However, open source challenges the most myopic individualist or collectivist account of human beings and their labor, suggesting decentralized or less-centralized work need not decay into collectivism or other forms of indentured labor.

The tools of freedom

Contrary to the skeptic’s illusions and Gates’ implication, open software development forecloses any practical need for indenture here. It’s voluntary training for those interested and with some aptitude. To the extent that a “thing” is produced, it is like a complicated handbook to be followed by specialists, raw material for iteration or adaptation, and only then becomes something that end-users find valuable.

My vocation leads me to ask if you want to learn to code. And if you do, let me show you how I did it. (To begin, pick one and stick with it.) At the same time, I have no illusions that you, the reader, are reading this to learn how to code or read these precise words in this order. When you subscribe to Return, are you paying developers to run software or paying writers to use some particular verb or notebook? Or are you paying for a journey, a recipe, a community, or the possibility of knowledge?

The latter may seem sentimental, but it is also ruthlessly pragmatic in preserving liberty and human-scale problems. You, the reader, have a challenge or problem, and you want a solution to that problem. Perhaps you’re faced with idle boredom and wish to satiate it. Or you’re looking to enrich yourself and take on some intellectual and experiential challenge. Perhaps you want to find and interact with others who share your quirk or interest and to build; your problem and the solution (or the mere start of a solution) will vary in scope and depth. The value you assign to it, through the use of money and your time, will vary accordingly. We can have a philosophical discussion on copyright or intellectual property or see what happens when we propose some challenge to its premises and sidestep any simple answer. Proper open source respects copyright while having its doubts.

In actual practice, open source gives rise not to tyrannical corporatism nor collectivistic authoritarianism but an aristocratic or republican form that permits and encourages virtue. It results neither in a marketplace of identical mass-produced products targeted at a collectivistic consumer nor an impoverished marketplace of stale or absent bread. It is the bazaar or an organized flea market of plentiful variety, rich options, and entrepreneurial small- and medium-sized creators. It’s not anarchistic but self-governing at its best.

Open source allows you to take action and exert your will while expecting the ordinary and leaving the possibility of virtue open. And you may begin as a casual reader (not that there’s anything wrong with that!), or orient yourself to becoming a more engaged participant. The bazaar marketplace extends from freelance developers and designers through to illustrators, culture and technology writers, support technicians, documentation writers, small businesses that want to catch up, and big businesses that want to catch up. Conservative, liberal, and libertarian manifestations and flavors of the open-source ethos exist in variouslicense manifestos: BSD, GPL, public domain, and many others. The proliferation of open source and its subsequent ecosystems casts some doubt on Gates’ early prediction of developer impoverishment.

Recall the modest SQLite database that I mentioned. Open source and free, the software is embedded in billions of devices to enable simple functionality that users expect. It’s also very obscure to users and relatively mature, so you might think the developers are forgotten and destitute. But far from impoverished, its authors command quite a bounty for their support services. Many thousands and millions derive and create value from the software, whether as developers using it as one tool in their kit, or, crucially but incidentally, as an end-user who likes to bookmark websites or use a functioning remote control.

Open source has powered much of our experience with computers, and its ideology is more relevant than ever before. As Big Tech attempts to reduce humanity to machines emptied of dopamine and God, we depend on recognizing the simple tools that existed before it and still reside deep in its core.

We needn’t return far to uncover a more fruitful path to human flourishing in technology. There is no need to rewrite networking or computers from scratch; many building blocks remain from the early blogosphere and computing history and are increasingly relevant. We need mostly the will, creativity, and courage to catechize the bots. There’s a map to building self-sustaining, virtuous, and indeed profitable institutions, and it can be found throughout open source. The question is whether we will look at it or stare right through it.

Google CEO admits he doesn't 'fully understand' how his AI works after it taught itself a new language and invented fake data to advance an idea



Google released Bard in March, an artificial intelligence tool touted as ChatGPT's rival. Just weeks into this public experiment, Bard has already defied expectations and ethical boundaries.

In an interview with CBS' "60 Minutes" that aired Sunday, Google CEO Sundar Pichai admitted that there is a degree of impenetrability regarding generative AI chatbots' reasoning.

"There is an aspect of this which we call ... a 'black box.' You know, you don't fully understand," said Pichai. "You can't quite tell why it said this or why it got wrong. We have some ideas, and our ability to understand this gets better over time. But that's where the state of the art is."

CBS' Scott Pelley asked, "You don't fully understand how it works and yet you've turned it loose on society?"

"Let me put it this way: I don't think we fully understand how a human mind works either," responded Pichai.

Despite citing ignorance on another subject as a rationale for blindly releasing new technology into the wild, Pichai was nevertheless willing to admit, "AI will impact everything."

Google describes Bard on its website as "a creative and helpful collaborator" that "can supercharge your imagination, boost your productivity, and help you bring your ideas to life—whether you want help planning the perfect birthday party and drafting the invitation, creating a pro & con list for a big decision, or understanding really complex topics simply."

While Pichai and other technologists have highlighted possible benefits of generative AI, Goldman Sachs noted in a March 26 report, "Generative AI could expose the equivalent of 300 million full-time jobs to automation."

In 2019, then-candidate Joe Biden told coal miners facing unemployment to "learn to code." In a twist of fate, the Goldman Sachs report indicated that coders and technologically savvy white-collar workers face replacement by Bard-like AI models at higher rates than those whose skills were only yesteryear denigrated by the president.

Legal, engineering, financial, sales, forestry, protective service, and education industries all reportedly face over 27% workforce exposure to automation.

In addition to losing hundreds of millions of jobs, truth may also be lost in the corresponding inhuman revolution.

"60 Minutes" reported that James Manyika, Google's senior vice president of technology and society, asked Bard about inflation. Within moments, the tool provided him with an essay on economics along with five recommended books, ostensibly as a means to bolster its claims. However, it soon became clear that none of the books were real. All of the titles were pure fictions.

Pelley confronted Pichai about the chatbot's apparent willingness to lie, which technologists reportedly refer to as "error with confidence" or "hallucinations."

For instance, according to Google, when prompted about how it works, Bard will often times lie or "hallucinate" about how it was trained or how it functions.

"Are you getting a lot of hallucinations?" asked Pelley.

"Yes, you know, which is expected. No one in the, in the field, has yet solved the hallucination problems. All models do have this as an issue," answered Pichai.

The Google CEO appeared uncertain when pressed on whether AI models' eagerness to bend the truth to suit their ends is a solvable problem, though noted with confidence, "We'll make progress."

\u201cOne AI program spoke in a foreign language it was never trained to know. This mysterious behavior, called emergent properties, has been happening \u2013 where AI unexpectedly teaches itself a new skill. https://t.co/v9enOVgpXT\u201d
— 60 Minutes (@60 Minutes) 1681687340

Bard is not just a talented liar. It's also an autodidact.

Manyika indicated Bard has evidenced staggering emergent properties.

Emergent properties are the attributes of a system that its constituent parts do not have on their own but arise when interacting collectively or in a wider whole.

Britannica offers a human memory as an example: "A memory that is stored in the human brain is an emergent property because it cannot be understood as a property of a single neuron or even many neurons considered one at a time. Rather, it is a collective property of a large number of neurons acting together."

Bard allegedly had no initial knowledge of or fluency in Bengali. However, need precipitated emergence.

"We discovered that with very few amounts of prompting in Bengali, it can now translate all of Bengali. So now, all of a sudden, we now have a research effort where we're now trying to get to a thousand languages," said Manyika.

These talented liars capable of amassing experience unprompted may soon express their competence in the world of flesh and bone — on factory floors, on soccer fields, and in other "human environments."

Raia Hadsell, vice president of research and robotics at Google's DeepMind, told "60 Minutes" that engineers helped teach their AI program how to emulate human movement in a soccer game. However, they prompted the self-learning program not to move like a human, but to learn how to score.

Accordingly, the AI, preoccupied with the ends, not the means, evolved its understanding of motion, discarding ineffective movements and optimizing its soccer moves in order to ultimately score more points.

"This is the type of research that can eventually lead to robots that can come out of the factories and work in other types of human environments. You know, think about mining, think about dangerous construction work or exploration or disaster recovery," said Hadsell.

The aforementioned Goldman Sachs report on jobs lost by automation did not appear to factor in the kind of self-learning robots Hadsell envisions marching out into the world.

Prior to the conquest of human environments by machines, there are plenty of threats already presented by these new technologies that may first need to be addressed.

Newsweek prompted Bard's competitor ChatGPT about risks that AI technology could pose, and it answered: "As AI becomes more advanced, it could be used to manipulate public opinion, spread propaganda, or launch cyber-attacks on critical infrastructure. AI-powered social media bots can be used to amplify certain messages or opinions, creating the illusion of popular support or opposition to a particular issue. AI algorithms can also be used to create and spread fake news or disinformation, which can influence public opinion and sway elections."

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