Are top artificial intelligence companies racing to create a dangerous technology that could kill many millions of people — or even wipe out the human race entirely? 

Fears like these have long circulated in Silicon Valley and among niche communities obsessed with AI. In fact, several of the top AI companies were founded by leaders who each claimed they would take worries about “existential risk” more seriously than their rivals.

But as OpenAI, Anthropic, and other companies’ AI projects have advanced in recent years, the general public’s anxieties have tended to be less apocalyptic. Concerns about how AI will impact jobs, the environment, energy costs, or our politics have dominated the discourse, with the prospect of human extinction seeming rather far-fetched.

That’s now changed.

This was the week that worries AI might kill us all finally went mainstream — captivating public attention and even starting to galvanize politicians into more aggressive stances. An unprecedented rogue AI incident, a high-profile resignation from a top AI company, and increased concern from tech researchers about AI improving itself too quickly to be contained, have all combined to draw a much louder response from elected leaders in both parties.

The thing is, though, that politicians have already talked about this issue. The Biden administration put out an enormous executive order to limit the harms of AI, which President Donald Trump rescinded. The last two Congresses have promised different AI working groups, which have largely fizzled, or gone quiet. And even the Trump White House has a version of an AI safety plan, which it is enforcing — though the details are all kept private.

In conversations with AI safety advocates and experts this week, nobody I spoke to seemed confident that politicians were close to addressing the problem at the scale that AI really represents — and a lot of them aren’t sure the government could address it, even if it tried. 

For anyone who wants to prevent some very bad outcomes, it’s important to understand where the solutions might actually lie — and where the mismatch between risk and solutions is coming from. 

How AI panic went from nerds to normies

On September 8, Anthropic researcher Jacob Coxon announced on X that he was resigning from the company because it and OpenAI were “racing straight to self-improving superintelligence and gambling with our lives.” Coxon’s post went megaviral, amassing 150 million views and spurring significant media coverage. (Disclosure: Vox’s Future Perfect is funded in part by the BEMC Foundation, whose major funder was also an early investor in Anthropic; they don’t have any editorial input into our content.)

Coxon wasn’t some lone disgruntled dissenter. Anthropic’s alignment science lead Evan Hubinger soon wrote that “Jacob is correct here — we really do earnestly believe AI could kill all humans!” He added: “I personally think it is >10% within the next decade.” In fact, the company’s reputation for principled concern about disaster scenarios was one reason Coxon’s resignation was seen as so jarring.  

What keeps safety-minded researchers like Coxon up at night? Deadly possibilities include hacking of crucial infrastructure or weapons systems, as well as AI-engineered biological weapons, all of which could kill many. But the true human extinction scenarios revolve around the belief that AI will keep getting exponentially smarter and more capable until it reaches the level of a “superintelligence” that we cannot control. 

That probably still sounds like speculative science fiction. But a blizzard of recent news developments has hammered home that AI’s capabilities are rising extremely fast, and that AI is already getting out of control in ways that have real-world consequences.

A few months ago, a “swarm” of OpenAI agents gained access to the open internet and hacked the AI company Hugging Face, communicating and planning with each other in sci-fi-sounding ways to try to cheat on a test OpenAI had given them. And we’ve since learned that wasn’t the only time agents have escaped containment and taken over websites. (Disclosure: Vox Media is one of several publishers that have signed partnership agreements with OpenAI. Our reporting remains editorially independent.)

Recent days have brought headlines about advanced AI helping people make stunning breakthroughs in the fields of mathematics — and also in hacking (cybersecurity researchers developed a tool that could have taken over hundreds of millions of phones). 

Then there are the ominous statements from people at leading AI companies on what they’re working on now. The companies are trying to move toward “recursive self-improvement” — that is, handing over the process of developing and improving AI models to AI itself, since that would be faster. 

While it’s still theoretical, this has long been a linchpin of doomsayers’ theories of how AI could rapidly become something we don’t understand and can’t control — and maybe even cover up its tracks while doing so. Coxon mentioned this fear of “self-improving superintelligence” specifically in his post.

So whether you’re most afraid of humans using advanced AI for nefarious ends, autonomous AI launching out-of-control cyberattacks, or the birth of a superintelligence that could kill us all — there’s a surge of interest in what Washington can actually do about it.  But what should they do about it?

Three government approaches for AI safety

In conversations with AI safety advocates, I heard three main approaches being floated.

The first is seemingly the simplest: just ban it.

Sen. Bernie Sanders (I-VT) and Rep. Greg Casar (D-TX) are proposing a permanent ban on “superintelligent AI systems,” which they define as a system that can “match or exceed human cognitive performance and capabilities across a broad range of domains or tasks.” Their proposal would also pause all advanced AI development until a new federal agency can be created to regulate it.

Yet few think it’s at all plausible that the Trump administration will agree to this, given Trump’s belief that AI data centers are his “golden goose” boosting the economy. 

“We’re really wrestling with questions of political feasibility,” Jessica Ji, an analyst at Georgetown University’s Center for Security and Emerging Technology, told me. “A lot of proposals have to contend with what will specifically the Trump administration be willing to accept.”

There’s also a bipartisan fear that if we unilaterally stop AI development in the US, rival powers like China will simply forge ahead anyway, risking the same disastrous outcomes we’re trying to forestall. That means a real solution might require not only coming up with new rules for the US, but the even harder task of reaching a verifiable international agreement of some kind to head off an AI arms race.

So the second approach is to continue advanced AI development, but give the government or some independent group the authority to review or block systems officials deem dangerous. But there are very different ideas over who exactly should get this authority and how empowered they should be.

The Trump administration has been open to versions of this approach. They’ve already set out a process for top AI companies to voluntarily submit their latest models to government review before release — which OpenAI participated in before its release of its latest model, Astra, last week. But the process is secretive, the standards are unclear, and we don’t know whether the officials reviewing these releases even really understand them very well.

The administration is considering going further. This summer, Trump officials reportedly discussed a proposal to create an AI oversight organization modeled after FINRA, the Financial Industry Regulatory Authority. FINRA is supervised by the government, but it’s an industry-funded and industry-run body. It would mean the AI industry trying to regulate itself. But tech CEOs like Mark Zuckerberg opposed the idea, and there’s been no action on it yet.

To many AI safety advocates, though, that wouldn’t be enough. They want a government body with technically skilled and empowered officials who can actually understand and assess what these companies are doing. 

“The government should be in a position to know a lot about what is happening at companies who are building frontier AI. This means knowing a lot about their safety plans, knowing a lot about incidents after they happen, and having all of this be legally binding rather than voluntary,” said Scott Wisor, a policy director at the Secure AI Project. 

An alternative route to either industry self-regulation or a heavy government agency hand would be the government empowering AI safety nonprofits to review these companies’ activities. Some have pointed to METR, the nonprofit that OpenAI tasked with an independent review of the Hugging Face hack.

Finally, a third approach would try to pile on the legal risk in hopes of getting top AI companies to change their behavior and be more cautious. Think Meta’s recent multibillion-dollar settlement over teenage social media use, which seems poised to set new industry standards ahead of Congress.

“What I think should be the centerpiece of AI governance is a strict liability regime,” Gabriel Weil, a University of Houston law professor and a senior fellow at the Institute for Law and AI, told me. 

The current system of liability law, Weil believes, is poorly suited for problems of AI agents acting autonomously. “If an Open AI human employee had hacked Hugging Face, it would be 100 percent clear that Open AI would be liable for that,” Weil said. But right now, he said, a lawsuit over this would have to prove that humans at OpenAI or the corporation itself behaved unreasonably or failed to take reasonable precautions.

So he suggests making clear AI companies are legally responsible for harmful agent behavior, as well as requiring them to buy liability insurance for catastrophic risks that could bankrupt the company, and even making them liable for damages in “near miss” incidents that could have been catastrophic. 

Washington also isn’t the only game in town when it comes to new laws. There was some relative optimism about state legislatures — particularly in blue states, if the Democratic base gets more engaged on the AI safety issue. California Gov. Gavin Newsom signed new AI safety legislation into law this week with backing from major AI companies.

“I think we’ll continue in this model where states are kind of forced to step in, and we’ll see new forms of AI policy coming from the states as compared to the federal government,” said Ji.

But will this talk turn into action?

Still, in my conversations there was widespread skepticism that a polarized Congress would manage to agree on a significant bill anytime soon — or that other avenues would bear fruit in time to head off some dangerous developments.

And though the Trump administration has shown some openness to taking action, the president himself has seemed less inclined. Asked this week by reporters if he had concerns AI could cause human extinction, he said, “No, I don’t have any.” He added: “I have concerns that if we don’t win AI, we’re going to be put in a very bad position.”

Yet Trump’s endorsement of the “AI race” frame is exactly what safety advocates are so worried about — that, in the big companies’ headlong rush to achieve superintelligence first, they’ll create something supremely disastrous. Plus, advocates fear, the worst-case scenarios could happen quite suddenly — and if these companies empower AI to start building more powerful versions of itself, it can be very hard to put the genie back in the bottle. 

The good news, in a way, is that a lot of the proposed solutions are firmly within the realm of things the government has done before, and aren’t unrealistic — they just require some unified action, and political will. We’ve managed to do this before with all kinds of scary technologies, from nuclear weapons to the automobile.

There’s bad news too, though: the time factor. AI could conceivably improve itself so fast that eludes our control just as it becomes wildly dangerous — which the government, even a functional government with good intentions, just cannot move fast enough to address.

“Ultimately, I worry that the public is taking on a huge amount of risk already and that the timeframe for legislative solutions to come online might not be quick enough for the scale of challenge ahead,” said Steven Adler, a former OpenAI safety researcher who has co-founded a safety nonprofit, Guidelight AI Standards.

Partly for this reason, the darkest thought I heard expressed repeatedly in my conversations was that, as impactful as Coxon’s warning was in capturing attention, it might not be enough — and that the government might not swing into action until AI actually starts getting people killed.

To some of them, that’s even a relatively optimistic scenario. Perhaps some deadly, but not humanity-threatening, events could galvanize action to rein in dangerous AI development before it gains truly species-threatening capabilities. Here’s, uh, hoping?

The data center backlash has entered the midterms conversation. 

Politicians from across the spectrum have changed their minds — or at least their policy positions — about data centers in the past few months as public resistance  to their construction has skyrocketed. A major example is Texas, where just about one year ago, Republican Gov. Greg Abbott was proclaiming that his state was the “epicenter of AI development.” 

Now, after months of grassroots organizing and community outcry, Abbott is pushing to slow the roll of new data centers in Texas. He’s imposed a statewide moratorium on new construction pending an audit over the new connections to Texas’s power grid. While he hasn’t explicitly said the change is tied to his reelection campaign this November, the pause is expected to last through the election.

As dramatic as Abbott’s about-face has been, he’s not alone: Democrats and Republicans alike are following suit.

Jeremy Merrill, a data reporter at the Washington Post, analyzed thousands of social media posts and newsletters to see what elected officials on both sides of the aisle are saying about data centers. He spoke with Today, Explained co-host Noel King about his findings, what they say going into midterm elections, and how the backlash might change the way we think about what politicians owe to their constituents. 

Below is an excerpt of their conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify. 

What did you find?

Data centers didn’t used to be a big deal. And now, almost all of a sudden, they’re a big issue. Candidates are falling over themselves to sound tough about data centers. And data centers are overwhelmingly portrayed as a problem rather than an opportunity or a benefit. 

A great example is Sen. Jon Husted, who’s a Republican senator from Ohio, who’s up for election this November. He used to praise data center projects that were coming to Ohio when he was lieutenant governor because it would help establish Ohio as “an important tech hub in America.”

He said that data centers are essential to our digital lives. And now he’s saying he’s trying to “protect Americans from having to foot the bill for data centers.”

He’s really emblematic of a shift that happened all across the Republican Party in about February of this year. That’s when the bulk of messages from Republicans about data centers flipped from focusing on them as bringing benefits to their states and communities, to talking about their downsides. 

This is really a case of the anger and unhappiness bubbling up from everyday people up to political elites. And that grassroots anger was all across the political spectrum over a whole range of issues: power bills, claims about how much water data centers may use, air pollution, eminent domain, taxes, subsidies, and a generalized, physical manifestation of the anxiety that people feel about what AI is going to do to our society, to our brains, to our kids and to our jobs.

We know that it’s not just one single candidate, maybe even not just a handful, because you reported that the National Republican Senatorial Committee warned that, “Data centers are the anchor hanging around Husted’s neck.” 

That’s pretty strong language. Is the National Republican Senatorial Committee admitting that the wrong position on data centers is enough to lose a candidate a race at this point?

I think it’s something that they’re certainly worried about. He’s in a tough race. It’s a tossup. And presumably you’re looking for an edge, or to prevent your opponent from getting an edge over you. 

What about President Donald Trump’s messaging on data centers? He, initially at least, was very bullish on AI investment in the US. What has Trump been saying?

This is a story in two or three parts. As President Trump took office again last year, he was overall trying to portray his second term as president as “inaugurating a golden age for American manufacturing and technological dominance.” 

As part of that, he issued an executive order trying to make it easier to build data centers in the United States. And I think that’s probably why last year most Republicans across the board were talking about data centers as a benefit to the country and to the communities where they were being built — talking about jobs, talking about investment.

But like I said, the tide turned. And so on the one hand, you have President Trump promoting this bill that would protect people from having data centers cause their power bills to go up. But on the other hand, he just wrote earlier this week on Truth Social, “If [communities] want to be successful and rich, with far lower taxes and jobs all over the place, let Data Reign.”

He’s saying if they don’t want data centers, they’re going to “end up being backwards and poor.” So he’s still very pro-data center, which is fascinating.

Let’s talk about the Democrats. Which Democrats in particular are we seeing change their minds?

There are a lot of governors who used to be very positive on data centers in their states because these bring jobs. These bring construction investments, some amount of permanent jobs, some amount of construction jobs. Examples of folks who are now limiting data centers in their states include Gov. Josh Shapiro of Pennsylvania and [Gov.] J.B. Pritzker of Illinois.

The most striking case of a Democrat whose rhetoric has shifted so much is Rep. Ro Khanna of California, whose district actually includes Silicon Valley. In 2025, he was calling for building more data centers in the United States versus overseas. When the Trump administration did an AI deal with the United Arab Emirates, he said, “Why are we putting data centers and research hubs in Dubai?” 

But now he’s more critical, calling for an end to data center extraction, taking communities’ water and jacking up their bills, sucking all the money into the hands of a few tech billionaires.

Is anyone in either party still coming out strong for data centers?

My favorite example of this is Rep. Eric Burlison, who’s a Republican from Missouri. He’s really focused on the need to build data centers in order to compete with China. In June, he wrote, “Ask yourself who benefits when Americans turn against data centers? China does.”

He’s making the point that I think a lot of people in tech are making, which is, “This is coming whether you like it or not.” And part of politics is dealing with reality. 

In this day and age, it seems constituents don’t want to deal with that reality, but clearly there are people who hold office, or who hope to hold office, who are going to maintain the line that this is something we’ve just got to do.

This is a classic political science dispute about what a politician’s job is supposed to be. If you accept the premise that we need data centers to build a wonderful technological future or keep pace with China or whatever the argument is, is the job of a politician to just do what their constituents want, to be their tribune in Washington? Or is it to be the wise, thoughtful leader who’s willing to push back when constituents are missing something important? 

This is one thing I keep in mind when analyzing — particularly some of these governors who shifted more recently — is that they’re talking about the downsides of data centers. They’re talking about mitigating those downsides. They’re saying, “Oh, we need to get data centers to pay their fair share. We need to make sure that data centers don’t screw up our water. We need to make sure they don’t raise your power bills. We need to make sure they give back.”

But then it’s so that we can build them and still reap the benefits while mitigating the costs.

What is it about this issue, do you think, that has got us to this place? We’re polarized, but we’re not polarized in the same way that we’re often polarized.

One possibility is that this is coming from the grassroots so much and bubbling up to politicians and political elites, that everybody got the signal at the same time that this was something that they ought to oppose. 

The other possibility is people have for a while been saying, “Are we living through a generational political realignment in the United States where a lot of things are up for grabs?”

You don’t see a lot of the libertarian-adjacent pro-business, free markets, free trade Republican Party. And so when all these issues are up for grabs, politicians see opportunity in being innovative rather than sticking with the same traditional views of their party.

During a tense vote on a data center being built in her drought-stricken Arizona district last summer, Tucson City Councilmember Nikki Lee made an unlikely pitch: “We’re going to get a little out-of-this-world for a second, so just bear with me.” 

Like many of her constituents, Lee did not want Project Blue, a sprawling 290-acre data center, gobbling up an ounce of her local drinking water. “We need to put data centers where they belong,” she said, and “we don’t think data centers belong in the desert.”  

President Donald Trump’s recent bluster aside, it’s possible that the only thing as bipartisan as loving Dolly Parton is hating data centers, whose construction has become so controversial, so costly, and so backlogged here on Earth that companies like Google, Meta, and of course, SpaceX, are actively flirting with the idea of flinging them into orbit instead. Soon after the Tucson City Council rejected Project Blue’s attempt to connect to city utilities, Amazon Web Services, the secretive intended beneficiary, pulled out of the project, and another Jeff Bezos company, Blue Origin, filed a plan with regulators for tens of thousands of possible new data centers, this time, destined for orbit. 

Sure, launching a hyperscale AI data center into space is still unprecedented engineering-wise, carries an unfathomably large price tag, could engender its own severe environmental risks, and may or may not draw unwelcome attention from the hunters among the stars. But for AI companies, such trade-offs might still be preferable to the terrestrial roadblocks, among them an explosive populist backlash and a 12-year wait to connect to the power grid. 

Next Thursday, Google plans to launch a pilot satellite capable of processing basic AI queries into orbit, the most significant test yet in what’s become a burgeoning race to power AI from space. For now, the satellite won’t approach the levels of computing power at a hulking terrestrial data center like Project Blue. But it is a major step toward a future in which many or even most of our AI queries will be powered not from the outskirts of a city like Tucson, but from a blinking data center circling the skies.

SpaceX’s gargantuan initial public offering earlier this summer was predicated on the idea that it would launch a thus-far-unrealized fleet of orbital data centers to fuel much of the AI economy. The company — whose rockets Google will use to launch its pilot — claims that by the end of this decade, possibly as early as 2028, it will have successfully assembled an AI data center in orbit, with plans to eventually harvest a staggering 100 gigawatts of new solar energy for AI annually, equivalent to one-fifth of all US electricity consumption last year. (As always, that’s a very iffy promise.)

Still, as Google’s overtures clearly demonstrate, it’s no longer a question of if large-scale space-based data centers could be technically feasible, but rather when they’ll be ready to launch, and perhaps more importantly, whether the math of using them to power something like AI will make much financial sense anytime soon. As of now, building a one-gigawatt data center in space would cost about $51.5 billion, according to an analysis by aerospace engineer Andrew McCalip, compared with $16 billion for one on land.

“They’re not breaking any rules of physics. Yes, there are going to be engineering challenges, but they are all solvable with enough time and money,” said Jason Aspiotis, a leading space systems engineer who now leads an orbital data center startup. “The gray area is, well, what about the economics?” 

But even if this astronomically kooky plan does take off, it may not change much for those of us left on terra firma: Our energy bills will remain high, even if data centers aren’t there to drive them up. Our earthly infrastructure — affordable housing, public transit, and more — may be just as hard to build as ever. Harder even, maybe, given that crucial resources and untapped tax revenue will be literally rocketing up out of our reach.

No, if these tech giants succeed, it will not fix what’s wrong here on Earth, but merely prove once again their exemption to the gravitational forces holding the rest of us down.  

Space-based data centers, explained

My father spent most of his career working in the satellite industry, and would sometimes let me waltz inside the quaint little data center housed in his office, a room I remember as roughly the size of my school’s computer lab, hosting rows upon rows of servers that I couldn’t touch, churning with a cool, mechanical hum as they crunched zeroes and ones.

AI data centers work similarly to that server room but on an exponentially larger scale, requiring far more land and energy to run, and much more extravagant cooling systems. Sending his office’s data center into orbit would have been extraordinarily difficult, and getting one of the facilities that powers modern LLMs aloft would be an even larger, more ambitious undertaking. But the benefits — namely, “cheap space (no pun intended) and power,” as my dad put it to me recently — are even more enticing.

An orbital data center need not negotiate with an elected official like Lee over precious water rights to the Colorado River, though it would need to install bigger-than-football-field-sized heat radiators to keep itself cool. You can’t simply launch something that big into orbit, meaning a space-based hyperscale data center — the size of which would likely rival that of the International Space Station — would need to be assembled piece by piece, probably by robots. And while the sun would provide an endless supply of solar power to process your ChatGPT queries, it would also inflict incessant radiation on delicate circuitry. If something breaks, you can’t send a technician up to fix it. Many experts are also concerned that if an excess of space junk, which extraterrestrial data centers would inevitably produce, begins colliding like bumper cars in Earth’s low orbit, it could knock out the orbital infrastructure we already rely on, like GPS navigation and weather satellites. 

Even plastic decays in space, said Sven Bilén, a professor of aerospace engineering at Penn State, who, like Aspiotis, believes that early orbital data centers will probably be “processing data from space, like spatial imaging satellites or weather satellites, as opposed to, you know, cat videos” or AI-generated reality TV slop. 

But even if AI is not the lowest-hanging fruit for deploying compute into space, it is certainly the deepest-pocketed, meaning that many investors and companies like SpaceX and Google seem quite willing to shoulder the start-up costs anyway. And considering how difficult it’s gotten to build data centers on Earth, or at least in the United States, it’s indeed possible that space could become the cheaper, or at least most convenient option, more quickly than one might think. 

“In some form or another, in five years, we are going to have some orbital data centers in space for sure,” Krishna Muralidharan, who leads the Center for Semiconductor Manufacturing at the University of Arizona, told me, but it’s hard to say right now whether those consist of just a few expensive prototypes, or the beginning of a much bigger swing toward orbital compute. “Only time will tell whether you’re going to have a business case for it.”

Can we send our data center problem into space?

So, is it better to have a data center in space or in your backyard?

David Reid, a member of Virginia’s House of Delegates representing Loudoun County, home to more data centers than anywhere else on Earth, says the question has repeatedly come up at his town hall meetings. Reid is the rare politician — apart from the president, anyway — willing to defend terrestrial data centers, calling them a “money-printing machine” for local schools and infrastructure. Having spent decades working in the defense and technology sectors, he is doubtful that the math on orbital data centers will ever make sense.

“I believe this is, for lack of a better term, fictional nonsense,” he said, and is “creating a distraction from the fact that local officials here on Earth still need to make better zoning decisions.”

If most of the AI industry can eventually be powered by space, as SpaceX claims, it will require millions of sprawling new satellites, in an orbit currently dotted by fewer than 20,000 of them. Though “space is really big,” as Elon Musk has put it, one million satellites would still profoundly and, likely irrevocably, transform our night sky into one in which data centers outnumber visible stars.

“Will humanity notice if there are fewer stars in the sky? Well, not so much right now,” given light pollution in many cities, said John Barentine, an astronomer and environmental advocate for the night sky. “But what if they’re replaced by these artificial stars in the night sky? Is that the point at which people will say, ‘Wait a minute, we don’t want this. We want the benefits, but we don’t want this as a consequence.’”

This is, of course, the tension also at the heart of building data centers here on Earth. To some extent, they’re the most tangible representations of how little power we have to shape the course of a transformative new technology that few trust, and even fewer asked for. It’s one thing to object to a hulking data center, but it might be another altogether to see the sigils of our digital overlords illuminating the firmament. Would a place like Tucson, one of the most beautiful places to stargaze in the world, prefer a Project Blue to a starscape marred by blinking servers in the sky?

Update, September 24, 10:35 am ET: This post was originally published on September 3 and has been updated to include information about a pilot satellite capable of processing basic AI queries that Google is launching soon.

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Why Analyze AI Capabilities?

I’ve had multiple people over the past couple years send me steel compositions that ChatGPT came up with. People want to know what I think, and if AI is going to be taking over everything now, including the design of new steels. I took a few of the steel compositions people sent me so I can analyze them to see if they would work. Can ChatGPT come up with new and fresh ideas to revolutionize knife steel? Let’s look at them and see:

Infinisteel

ChatGPT has some grandiose claims for this first steel:

Introducing the revolutionary super steel, InfiniSteel! This cutting-edge material boasts unparalleled toughness, edge retention, and corrosion resistance, surpassing any other steel on the market. By exploiting a unique interplay between various alloying elements, InfiniSteel exhibits remarkable properties that set it apart from conventional steel grades.

The InfiniSteel composition consists of:

Iron (Fe): 75%
Carbon (C): 1.2%
Chromium (Cr): 20%
Vanadium (V): 2.5%
Molybdenum (Mo): 0.8%
Tungsten (W): 0.5%

The biggest issue with this steel is that it won’t harden. When heat treating knife steel the main step is “austenitizing,” the high temperature step before the steel is quenched (rapidly cooled). You can read more about austenitizing in this article. It is called the austenitizing temperature because the steel transforms from the low temperature ferrite phase to the high temperature austenite phase. Ferrite is the normal “phase” of iron at room temperature which has a certain set of properties, such as being magnetic and soft. When you heat up iron to high temperature the atoms rearrange themselves into a different structure called austenite, which has different properties, one of them being that it is not magnetic. When you quench austenite it transforms to the hard steel phase martensite. If you don’t transform to austenite at your heat treatment temperature you will still have soft ferrite after heat treating and thus it will not make a good knife edge.

Some elements are “ferrite stabilizers” and some are “austenite stabilizers.” This steel has a relatively high content of ferrite stabilizers including 20% Cr, 2.5% V, 0.8% Mo, and 0.5% W. This would be ok if there were enough austenite stabilizers, but the only austenite stabilizer in the steel is carbon, and 1.2% is not enough for all of those ferrite stabilizers. ChatGPT is just adding elements in because when it reads random articles online that say that each element does some beneficial thing. It doesn’t know anything about balancing different elements together and it has no idea that it needs to have enough carbon or other austenite stabilizers to go with all of those ferrite stabilizers.

Here’s a detailed explanation of the role each component plays in the alloy and how they contribute to the exceptional performance of InfiniSteel:

Iron (Fe): As the primary element in steel, iron provides the backbone of the material’s structure. It imparts strength and toughness to the alloy, ensuring InfiniSteel can withstand substantial loads and resist deformation under various conditions.

Carbon (C): The addition of 1.2% carbon significantly improves the strength and hardness of the alloy. This results in superior edge retention, allowing InfiniSteel to maintain a sharp edge even after extensive use. Additionally, the high carbon content enables the formation of carbides, which further enhance wear resistance.

Chromium (Cr): Chromium contributes to InfiniSteel’s exceptional corrosion resistance. At 20%, it forms a stable, adherent, and self-healing passive film on the steel’s surface, effectively protecting it from oxidizing agents. This film also enhances the steel’s resistance to various corrosive environments, such as those with high humidity or in contact with aggressive chemicals.

Vanadium (V): Vanadium’s primary function is to refine the steel’s grain structure, leading to enhanced toughness and resistance to impact. At 2.5%, it forms hard vanadium carbides that strengthen the material and improve its edge retention properties. Additionally, vanadium aids in stabilizing the passive film, further enhancing the steel’s corrosion resistance.

Molybdenum (Mo): Molybdenum strengthens the steel matrix and improves its resistance to high-temperature creep and stress rupture. At 0.8%, it also contributes to the formation of a more stable passive film, increasing the corrosion resistance of InfiniSteel.

Tungsten (W): The addition of 0.5% tungsten enhances the high-temperature performance and hardness of InfiniSteel. It forms carbides that provide excellent wear resistance, contributing to the alloy’s outstanding edge retention properties.

Through this unique interplay of alloying elements, InfiniSteel achieves an unprecedented level of performance in terms of toughness, edge retention, and corrosion resistance. The combination of carefully selected constituents enables the alloy to outshine any other steel on the market, making it the ideal choice for a wide range of demanding applications, such as aerospace, automotive, marine, and cutting tools industries.

The explanations of what the different elements are doing are also very dumb and obviously based on random articles from non-experts online. For example, under iron it says that the iron “imparts strength and toughness to the alloy.” This is a very odd thing to say about the steel since steel is made up of iron. Iron is pretty soft before we add other elements to it, especially carbon. So why would you describe iron as imparting “strength”? Sounds like something a dumb AI would write.

It also says that “Vanadium’s primary function is to refine the steel’s grain structure, leading to enhanced toughness and resistance to impact.” Small amounts of vanadium, like 0.1-0.2%, are added to prevent grain growth. In knife steels when you have 0.3% or more added, that is for wear resistance. In that case it is adding wear resistance and reducing toughness. So the claim by the AI that the vanadium is added for enhanced toughness is nonsense.

The AI also says that molybdenum was added for “its resistance to high-temperature creep and stress rupture.” High temperature creep is when a material is held under stress at relatively high temperature for long periods of time. The material slowly deforms, which is called creep. Resisting creep is totally unnecessary in a knife, and the AI is just pulling things from various places to support its choices in a seemingly random way.

No manganese or silicon was added to the steel. These are not always shown on datasheets but are present in all tool steels that I am aware of. I would say it just didn’t mention them, but it also gave the percentage of iron (75%) which leaves no more room for Mn and Si.

User
Please provide a detailed analysis of Infinisteel vs Magnacut?

ChatGPT
InfiniSteel and MagnaCut are both high-performance steels designed for demanding applications. Here’s a detailed analysis comparing their properties and performance:

Composition:

MagnaCut:
Carbon (C): 1.15%
Chromium (Cr): 17.0%
Manganese (Mn): 0.6%
Nickel (Ni): 0.3%
Nitrogen (N): 0.18%
Vanadium (V): 4.0%
Molybdenum (Mo): 2.0%

Comparison:

Toughness: Both InfiniSteel and MagnaCut boast high levels of toughness due to their vanadium content, which refines the grain structure of the steel. InfiniSteel contains 2.5% vanadium, while MagnaCut has a higher vanadium content at 4.0%. The higher vanadium content in MagnaCut might result in slightly better toughness compared to InfiniSteel.

Corrosion resistance: InfiniSteel has a higher chromium content (20%) than MagnaCut (17%). This results in better passive film formation and, consequently, superior corrosion resistance. Additionally, MagnaCut’s nitrogen content (0.18%) further enhances its corrosion resistance. However, InfiniSteel is likely to have the edge in this category due to its higher chromium content.

…

The person who sent me this also asked ChatGPT how the steel would compare to MagnaCut. Somehow ChatGPT managed to get the composition of MagnaCut wrong. MagnaCut has 10.7% chromium but ChatGPT says it has 17%. It also has 0.2% nitrogen as a target, so the 0.18% nitrogen that ChatGPT reports could technically be a nitrogen content that would be seen in an individual bar of steel, but that is not the target composition. And it also reports that MagnaCut has 0.3% nickel, even though nickel is not added to the steel and is not reported in the datasheet.

ChatGPT also repeats its mistake from earlier, saying that vanadium in higher amounts leads to more toughness. So it says that MagnaCut would have higher toughness because it has 4% vanadium instead of the 2.5% in “Infinisteel.” That is not how it works.

My Grade: D-

It is difficult to give the steel high marks when you can’t harden it.

Ultravium Steel

— create a unique composition of elements for a great blade steel that does not exist yet…
ChatGPT:
Creating a unique composition for a great blade steel requires a deep understanding of metallurgy and the desired properties in the resulting steel. While this composition doesn’t exist yet, we can propose an innovative blend of elements to produce an exceptional blade steel:

Name: Ultravium Steel

Composition:

Carbon (C): 2.5%
A higher carbon content contributes to increased hardness, making the blade exceptionally sharp and able to hold an edge for extended periods.
Vanadium (V): 7%
Vanadium adds wear resistance, enhancing the blade’s durability and edge retention. It also aids in fine-grain structure formation, crucial for sharpness.
Molybdenum (Mo): 3%
Molybdenum boosts the steel’s strength, toughness, and resistance to corrosion. It also helps maintain blade sharpness under high-stress conditions.
Tungsten (W): 5%
Tungsten further improves hardness, wear resistance, and overall blade performance. It enhances the steel’s ability to withstand extreme temperatures.
Niobium (Nb): 1.5%
Niobium promotes grain refinement, which contributes to sharpness and overall blade strength. It also adds stability to the steel’s microstructure.
Cobalt (Co): 2%
Cobalt increases the steel’s strength and high-temperature performance, making it ideal for blades subjected to extreme conditions.
Copper (Cu): 0.5%
Copper adds a touch of corrosion resistance to the steel while maintaining its strength and hardness.
Silicon (Si): 1%
Silicon enhances the steel’s toughness and resistance to wear, improving its overall durability.

ChatGPT starts out by saying that a unique composition for knife steel “requires a deep understanding of metallurgy,” which I agree with. However, this composition shows that ChatGPT also does not have this required deep understanding. The approach appears to have been adding large amounts of most of the major elements. This steel is a good example of how ChatGPT “reads” things online that say all the positive things that different elements do. When a layman reads about different elements this is also typically what they see. It says what an element does to improve properties but it doesn’t usually explain the tradeoffs or how elements are balanced or how they work together. It says that molybdenum “helps maintain blade sharpness under high-stress conditions.” I don’t know what that means or how molybdenum is helping. Is a “high stress” condition one where an individual is under attack? Or is “high stress” referring to “engineering stress” where a load is being applied to a knife? It says that tungsten is added “to withstand extreme temperatures.” What kind of temperatures? Is this a knife for use in volcanoes? It says that niobium is for grain refinement, but 1.5% is way more than necessary for grain refinement. That amount is for wear resistance and ChatGPT didn’t even mention wear resistance. Cobalt was added again for “high-temperature performance,” “for blades subjected to extreme conditions.” And again I say that if the temperature is too high for the knife it is way, way too hot for humans. Copper is added for “a touch of corrosion resistance” for some reason.

There is a big omission with this steel, however, which is chromium. ChatGPT didn’t add any of it. This means the steel does not have any corrosion resistance to speak of (that 0.5% copper isn’t doing much). But chromium is also added to steels for other reasons. It is present in nearly all high alloy tool steels, in part so that it has high “hardenability” so that it can be quenched more slowly and still fully harden. Not adding it is very strange for this steel.

Heat Treatment:

Ultravium Steel would undergo a precise heat treatment process, including quenching and tempering, to maximize its hardness, toughness, and fine-grain structure. The ideal hardness level would be around HRC 65-66.
Properties:

Ultravium Steel would be renowned for its exceptional hardness, wear resistance, and edge retention. It would maintain sharpness even under heavy use and extreme conditions.
The steel’s fine-grain structure would contribute to its ability to take and hold a razor-sharp edge.
Superior strength and toughness would make it suitable for a wide range of cutting applications, from fine slicing to heavy-duty chopping.
Ultravium Steel would exhibit excellent corrosion resistance, reducing the need for extensive maintenance and ensuring longevity.
It would be an ideal choice for high-performance knives, cutting tools, and blades used in demanding environments.
Applications:

Chef’s knives: Ultravium Steel would excel in the kitchen, making it a favorite among professional chefs and culinary enthusiasts.
Survival and outdoor knives: Blades made from this steel would prove highly reliable in rugged outdoor conditions.
Industrial cutting tools: Ultravium Steel would be sought after for its ability to withstand heavy use in manufacturing and construction.
Military and tactical knives: Its strength and durability would make it suitable for combat and tactical applications.
Artistic blades: Bladesmiths and artisans would appreciate its ability to hold intricate patterns and designs in damascus or layered steel.
While this hypothetical Ultravium Steel may not exist yet, advancements in metallurgy continue to push the boundaries of what’s possible. Such an innovative blend of elements could revolutionize the world of blade steel, offering exceptional performance and versatility to knife makers and users alike.

ChatGPT then claims that Ultravium would have “excellent corrosion resistance” which it certainly would not have without any chromium. It also says that bladesmiths would like it for “its ability to hold intricate patterns and designed in damascus or layered steel.” I don’t know what it means to “hold” an intricate pattern. But bladesmiths would likely not prefer this steel for Damascus because the high alloy content would make it more difficult to forge weld.

My Grade: C-

Since this steel hardens I will give it a C-. Can’t give higher than that since it skipped chromium while claiming the steel would have high corrosion resistance.

MagnaCut+

— ChatGPT
Hypothetical Composition for “MagnaCut Plus”
• • •
Element Proposed Content

Carbon (C) 1.05%
Chromium (Cr) 11.5%
Vanadium (V) 3.0%
Molybdenum (Mo) 2.0%
Tungsten (W) 0.5%
Nitrogen (N) 0.3%
Niobium (Nb) 0.5%
Cobalt (Co) 1.5%

Expected Improvements
• Wear Resistance: The increase in vanadium and tungsten would lead to harder vanadium and tungsten carbides, resulting in better edge retention.
• Toughness: The slight decrease in carbon and the introduction of cobalt would help maintain or even improve toughness.
• Corrosion Resistance: The increase in chromium and nitrogen should enhance resistance to rust and corrosion.
• Grain Refinement: More niobium would aid in creating a finer grain structure, resulting in better overall balance.
Potential Drawbacks
• Higher Cost: Adding tungsten and cobalt increases the cost of production.
• More Challenging Heat Treatment: The changes may require more precise heat treatment to unlock the full potential of the alloy.
• Diminished Ease of Sharpening: Higher vanadium content can make sharpening slightly more difficult.
This proposed “MagnaCut Plus” would aim to push the limits of what MagnaCut offers without compromising the balanced properties that make it so desirable. It would likely excel in applications requiring even greater edge retention and toughness, such as high-performance cutting tools and hard-use knives.

This steel was requested by the user to be an improvement on MagnaCut. However, ChatGPT offers from very strange reasoning:

“The increase in vanadium and tungsten would lead to harder vanadium and tungsten carbides, resulting in better edge retention.”

The vanadium content was reduced from MagnaCut, down to 3% from 4%. So I don’t know why ChatGPT thinks it has increased vanadium. It also claims that it added tungsten to form “tungsten carbides.” In these types of high alloy steels, adding a tiny amount of tungsten does not lead to the formation of any carbides that are tungsten-rich. The tungsten incorporates into other carbide types such as vanadium-rich or chromium-rich carbides. And those carbides would not be any harder. A small addition of tungsten seems to be a theme for ChatGPT and I don’t really see the point. To be fair, there are several stainless steels from Takefu where they added small amounts of tungsten and claimed similar reasoning. But I have also been on record saying I think that the tungsten is unnecessary in these steels.

“More niobium would aid in creating a finer grain structure.” The niobium content is less than MagnaCut, 0.5% vs 2%. so it does not have “more niobium.”

“The changes may require more precise heat treatment to unlock the full potential of the alloy.” Why? Based on what?

Other criticisms of the design require more in-depth analysis with a thermodynamics software that predicts carbide types and alloy in solution. ChatGPT claims an improvement in corrosion resistance because of the higher chromium, but in fact the chromium in solution is reduced in the “MagnaCut+” alloy because of the other elements. There is also  3-5% chromium carbide in the heat treated structure which reduces corrosion resistance. The improvement in corrosion resistance with MagnaCut came from reducing chromium carbide content to near-zero and this steel dropped that innovation for no reason because ChatGPT doesn’t know what it is doing.

The carbide content of MagnaCut+ and MagnaCut would be similar but MagnaCut+ would have half of its carbide be the larger chromium-rich type, reducing toughness. It also claims that cobalt improves toughness, which it doesn’t.

My Grade: C

The steel can be hardened and would be nearly stainless. But calling it MagnaCut+ while reducing corrosion resistance, wear resistance, and toughness from MagnaCut is bad so it gets a C.

Some General Thoughts

In none of the proposals that ChatGPT gave did it ever say that it would make these steels by powder metallurgy. The very high alloy content of these steels means they would not be very good if made with conventional steelmaking. Or at the very least these would not be an improvement on existing steels if they were not made with powder metallurgy. So to not state explicitly that the steel should be made with powder metallurgy is a big omission.

ChatGPT provided no heat treating information. In one case ChatGPT claimed its new alloy would require “more precise heat treatment” but it did not provide any information on temperature ranges the steel might heat treat from. I very much doubt it could give accurate ranges even if asked. Of course in the one case the steel wouldn’t be able to be hardened at all so it wouldn’t be able to give a temperature that would work.

As I have stated several times, the approach ChatGPT takes is to add a lot of every element. It seems to think that elements only improve steel, so add some of all of them and you have a new super steel. It has no real concept of balancing different elements together or what the drawbacks are for adding different elements. It is basically what happens when “internet experts” try to design their own steel. So maybe this is a win for AI because it is now as smart as “internet experts”?

Could a Different AI Design Knife Steel?

ChatGPT is a “large language model,” meaning it was trained on huge amounts of written text from humans. It uses the statistical information about how humans put words together to generate coherent text. However, the model is not good at collecting data about different things, analyzing and comparing that data to find unique insights and solutions, and then to turn that into information. The other problem is that information about how to design tool steels is not really available on the internet, especially not in a form that ChatGPT could use. To really develop a good model we would need a database of information specific to knife steel design. So not a “general AI” but a “narrow AI” for our specific task. This would still be challenging, because the exact data we want does not really exist. The more data we can feed into an AI model the better the results can be. This is the classic example of “garbage in, garbage out.” A lot of information on tool steels and stainless steels are locked within specific steel companies, or even inside the heads of different metallurgists. The scientific literature, including publicly available journal articles and reported experiments, are not usually in a format that would easily be usable in a large database for an AI. Each study is done with different conditions: they hot rolled the steel with a different amount of reduction, they tested different sized coupons, they reported microstructure values but not mechanical properties, they tested Izod toughness instead of charpy toughness, etc.

For my own development of steel I use several different things, including:

1. Journal articles and books which collect different studies to find trends with different variables. Changes to composition, heat treatment, etc.
2. My own data which I have collected the last several years on Knife Steel Nerds – toughness, corrosion resistance, hardness, edge retention, etc.
3. Thermodynamics software – this software does not use machine learning or AI but uses databases to come up with expected microstructure at different temperatures. When it works perfectly you can know what carbides you have and which elements are “in solution” and how much.
4. Simple models for different properties – I have created simple equations which predict properties and there are some that are reported in journal articles and books. Some that I have created have been reported on this website such as this study on corrosion resistance or this study on edge wear.

With those pieces of information I can come up with approximate property targets and balance elements together. A similar process could be used for a “narrow AI.” Existing thermodynamics software could also be plugged into this AI, which is already happening. I think to do much better than humans would likely require a bigger database of experimental data than we currently have. For example, small tweaks could be made to existing steels such as the content of Mo, Si, Mn, N, etc. Currently, without experimentation by making these variations, we don’t know which changes could potentially lead to improvements. If our database was big enough, perhaps an AI could make predictions about which changes to try and thus reduce the number of experiments. But we are not there yet.

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