In 2024, Donald Trump’s victory was powered in large part by massive gains with Latino voters. He won 48 percent of those voters that year, up from 36 percent in 2020 and 28 percent in 2016. But now, that support is plummeting; Trump’s approval rating among Latinos who voted for him recently fell from 93 percent to 66 percent, where it was at the beginning of his second term.

Why? It has a lot to do, as with everything these days, with the economy. 

It was the same in 2024. During that election, voters were experiencing rising costs and stagnant wages — the same trend that has continued until today. In 2024, though, Joe Biden and his administration — which also meant Vice President Kamala Harris — took the blame from all voters, and particularly Latinos. Today, the blame is falling on Trump. 

America, Actually host Astead Herndon caught up with Stephanie Valencia, who co-founded Equis Research, a political research firm that specializes in Latino voters, to unpack some of these recent shifts and explain them. 

“I think one important data point to understand about the Latino voter is Latinos make up more of the wage workforce than any other part or any other racial subgroup,” she said on the podcast. “And so what that means is when wages are down and inflation is high, Latinos feel it more.” 

That’s why, when the economy is bad, this group is primed to be a “swinging giant” in US politics, Valencia explains — and right now, it appears to be swinging hard against Trump. 

Below is an excerpt of the conversation, edited for length and clarity. There’s much more in the full show, so listen to America, Actually wherever you get your podcasts or watch it on Vox’s YouTube channel.

Equis has called Latinos one of the last great wild cards in American politics. Is that because of some of the double-digit swings that we’ve seen among this voting group over the past few elections?

Yes, it absolutely has to do with that. Latinos often get given the title the “sleeping giant,” which I just want to get rid of from everybody’s vocabulary. 

We should be called the “swinging giant” based on how much shift we have seen election to election. A third of Latinos consider themselves kind of independent or apolitical, and that swinging middle really is deciding every single election.

Your playbook gets into messengers and particularly how we reach folks where they are. It makes a Spanish-Spanglish distinction talking about the different types of media that Latino voters consume. 

I wanted to ask, in that vein, what’s the single biggest mistake campaigns make in terms of not reaching folks where they are?

Oh man, how long do we have so I can tell you how many mistakes? They’re really simple unforced errors. I’m going to give a very concrete and simple example that I think illustrates that. 

Look, I’m Latina. I grew up speaking mostly English, but I listen to Spanish-language radio. I watch a lot of YouTube, and on YouTube I’m watching people like Karol G and Carin León. A lot of people — a lot of Latino swing voters — have the same media consumption habits that I do.

The first problem or challenge that a lot of candidates make is they just simply translate their English ad into Spanish. And that is, to me, one of the biggest mistakes because there are a lot of things that people want to be communicating in Spanish that don’t translate word for word. There are sayings in Spanish that don’t translate directly from English or more direct ways you can communicate.

I’m sure it immediately reads Google Translate rather than an original message for said community.

Exactly. So that’s mistake No. 1. Mistake No. 2 is when they’re deploying that ad digitally, as a proxy for talking to people, they’re going to use Spanish-language browser settings. So they say, “We don’t want people who speak English to see the Spanish-language ad.”

So the way they find that set of voters is just by going to people who have Spanish-language browser settings. 

Like I said, I don’t have a Spanish-language browser setting. Everything I consume online is set in English on Chrome, but I still watch a lot of content in Spanish on YouTube.

So you’re missing a voter like me who’s going to see a Spanish language ad from a James Talarico or a Manny Rutinel and be turned on by it because I’m going to say, “Hey, that candidate’s speaking to our community. That candidate has enough respect to develop a Spanish-language ad.” 

We did some testing a couple of years ago that showed that over 70 percent of Latinos who see an ad in Spanish think that party or candidate cares more about Latinos. It matters that much.

Let’s stay in 2024 for a second longer. Your research found that Project 2025 and Trump’s mass deportation plans were not top of mind for most people, and that they were thinking about the economy. I wanted to ask about that because I remember the RNC signs at the convention saying, “Mass deportations now.” One number says that 62 percent of Latinos now think that Trump wants to deport undocumented immigrants broadly. 

Why did it take till post-election to realize those threats of mass deportation were real considering they were so front and center during his campaign?

I think this is really important for us as avid news consumers to understand the kind of news that the average voter, and especially that voter who’s coming in and out of elections, is consuming.

What they were seeing on mainstream media, perhaps even Fox News at the time, was a focus on people or Trump surrogates who said he was going to focus on serious criminals, securing the border, and violent criminals.

And so that’s what people bought. Fast-forward to 2026; the people who feel most regretful who voted for Trump in 2024 are most regretful because of immigration. They feel like there was a broken promise there. 

They were told he was going to focus on the border, which everybody agreed was chaos, and he was going to focus on violent criminals and serious threats, and at the end of the day, he has not complied with that.

And then add that to the litany of broken promises.

I’m hearing a distinction between a focus on the border, which Republicans did try to prioritize rather than the interior deportations that may have been more attributed to Stephen Miller than Donald Trump. But we have certainly seen that be a focus of the administration once they came back into office.

You mentioned the shift that has happened since 2024, and that’s really important, of course. Trump’s approval rating with Latinos is now worse than with non-Latinos, a stunning reversal in 18 months. 

You mentioned the factors that have contributed there, but is this really just the economy?

It’s the economy, it’s the war, and it’s immigration. It’s those three things. 

One thing that is really interesting to note about Trump’s approval, other than that it’s at basement levels — and he’s very much pulled down the GOP with him broadly and with Latino voters — is that in Trump 1.0, his approval on the economy and the cost of living was actually better than his everyday approval on his total job.

And he has been underwater with Latinos almost this entire term so far on the cost of living and the economy. It’s almost been a complete and total reversal.

Listen, you tell people you’re going to fix egg prices and gas is going to go down, and then you do the opposite, and we’re hearing that story universally across different electorate groups. 

You’re saying that’s still the main story when it comes to Latinos also?

Yes, it’s still the main story. I think one important data point to understand about the Latino voter is Latinos make up more of the wage workforce than any other part or any other racial subgroup. What that means is when wages are down and inflation is high, Latinos feel it more. 

We have less savings. We have lower rates of home ownership. So the safety nets that other people might have, we just don’t have. 

That really has exacerbated the kind of impact that people are feeling right now as they’re trying to make ends meet.

You mentioned the exponential growth in the terms of people who now regret or are disappointed with their vote for Donald Trump. Does that mean a preference for Democrats? How should we think about the binary choice between D and R versus what is a clear story about a backlash to Donald Trump’s actions?

I think it is a huge mistake for Democrats to rest on their laurels and think that because of what people are feeling about Trump and Republicans and the sense of disappointment and promises broken that that is automatically going to translate for them as a party and as a brand.

Right now, the Democratic brand has suffered a repeated set of blows among Latino voters. Twenty years of comprehensive immigration reform hasn’t been delivered. It’s a thing that comes up in almost every focus group. 

And part of what the build-up to 2024 was, was not only 20 years of a promise undelivered; it was the chaos at the border, it was feeling like Democrats were focused on everything but Latinos and their families, or everyday Americans and their families, and that they had lost that thread.

AI will be the death of me. And you. And everyone we know.

At least, this is what a growing number of technologists and policymakers fear. 

Last week, a lead researcher at Anthropic declared that there was a greater than 10 percent chance of AI “killing all humans” within the next decade. This nerve-racking forecast, combined with recent AI-initiated cyberattacks, have fueled calls for slowing the progress of artificial intelligence — a proposal that Anthropic and OpenAI subsequently embraced.

Not all prophets of AI doom agree about precisely how this apocalypse would come about. Many focus on scenarios reminiscent of The Terminator films, in which superintelligent machines conspire to exterminate humanity — perhaps in the same not-quite-intentional way that humans laid waste to the dodo, or passenger pigeon.

But there is another, arguably more intuitive way that AI could bring about mass death, if not human extinction: by making it easier for people to develop and deploy catastrophic bioweapons. 

Importantly, this hypothetical doesn’t require AI models to become conscious or superintelligent; it merely requires them to accelerate preexisting trends in biotechnology. 

For those alarmed by this hypothetical, the threat AI poses is essentially twofold. First, when combined with technological progress in gene editing and DNA synthesis, chatbots could vastly increase the number of people capable of assembling dangerous viruses already known to medical science. And the more potential bioengineers there are in the world, the higher the odds that one will prove to be a diligent psychopath.

Second, AI could help highly trained biologists to discover — or engineer — a custom supervirus, which could then fall into the wrong hands. 

Recent headlines have lent some credence to both of these fears. 

Earlier this year, biologists warned that, with a few simple prompts, they’d gotten chatbots to dispense detailed instructions for engineering a treatment-resistant strain of an infamous pathogen, step-by-step protocols for recreating a once-pandemic virus, and advice on dispersing biological payloads via weather balloon.

In August, a team of scientists revealed that an AI model trained on DNA sequences had generated blueprints for deadly viruses unseen in nature (albeit, viruses deadly to bacteria, not animals). And last week, Anthropic announced that it had thwarted several suspected attempts by state actors to use Claude for bioweapons research.

Precisely how much AI is increasing the risk of an artificial pandemic is unclear. Even before ChatGPT, advances in synthetic biology were already making bioengineering tools more potent and accessible. And even after four years of rapid AI progress, the technical barriers to engineering novel superviruses remain formidable.

Regardless, there is a great deal more that governments can — and should — be doing to protect us from synthetic pandemics, both manmade and natural. No matter what AI’s ultimate impact on biosecurity proves to be, now is the time to both prepare for the worst-case scenario and, ideally, preempt it.

Why synthetic superviruses are scary

Before we dig into how AI could raise the risk of a man-made pandemic, it’s worth spelling out why that prospect is so unnerving. 

Put simply, few things could more credibly destroy civilization — and/or, the human species — than a synthetic supervirus.

Infectious diseases have killed more humans than any other force on Earth. The Black Death wiped out at least 30 percent of all Europeans in the 1300s, while the 1918 flu killed 50 million people worldwide. You don’t need me to tell you what things were like in 2020 (or how much worse they could have been, if vaccines hadn’t been rolled out in record time). 

And nature kicked off these catastrophes without even trying. A bioterrorist consciously seeking to maximize human death could theoretically improve on evolution’s handiwork. If biotech continues to advance — and scientists identify viral genomes that reliably yield high transmissibility and lethality — a malicious actor might one day be able to manufacture a virus that’s both as contagious as measles and as deadly as Ebola. 

Natural selection generally disfavors viruses that are both highly transmissible and profoundly fatal; bugs that kill their hosts too reliably often have difficulty spreading. Yet a virus with a long asymptomatic period could escape this tradeoff. For example, HIV can live in a person’s body for a decade before generating noticeable symptoms. In theory, a highly contagious respiratory virus could similarly lie in wait for many weeks, months, or even years.

If bioterrorists ever managed to unleash an easily transmitted, highly fatal virus, they could shake the foundations of modern existence. In that scenario, reliably showing up for work might require accepting a 50-50 chance  of getting yourself — and your family — brutally killed. Covid never required essential workers to assume risks anywhere near this high (the virus had a less than 1 percent case fatality rate among prime-age workers, before vaccines became available). Although there are many heroes among us, plenty of doctors, nurses, warehouse workers, power plant technicians, delivery drivers, and other essential laborers might not tolerate a coin flip’s chance of wiping out their loved ones. And if enough sheltered in place, systems for producing and distributing healthcare, food, water, and power could swiftly break down.

Humanity’s “Joker” problem

All this invites the question: Who would ever want to do this? 

After all, viruses are terrible weapons: They’re difficult to make, impossible to control, and jeopardize the lives of those who cultivate and distribute them. Partly for these reasons, biological weapons have hardly ever been used; the last fatal bioterror attacks were the anthrax mailings of 2001.

To this, anxious biosecurity experts might reply, to quote the former British intelligence officer Alfred Pennyworth: “Some men just want to watch the world burn.”

Mass shooters regularly forfeit their lives to the cause of killing others at random. And brilliant iconoclasts periodically develop murderous ideologies; Ted Kaczynski, the Harvard mathematician turned Unabomber, wished to bring about the collapse of industrial civilization.

And every once in a while, an aspiring supervillain gets graduate training in virology. In the 1990s, the Kyoto University-educated genetic engineer Seiichi Endo joined Japan’s Aum Shinrikyo death cult. Drawing on the group’s $1 billion in funds, Endo sought to weaponize Ebola, Q fever, botulinum, and anthrax — with the aim of killing as many nonbelievers as possible. 

Of course, Endo failed. And lone wolf psychopaths have never gotten their hands on anything more lethal than an assault weapon, bomb, or airplane. 

But some scientists believe that technological advances — including generative AI — may provide the Endos and Kaczynskis of tomorrow with catastrophic power. 

How AI and biotech could democratize bioterrorism

MIT biologist Kevin Esvelt is among his field’s most prominent Cassandras. And he has voiced two fundamental concerns. The first is that breakthroughs in synthetic biology and AI are rapidly expanding the pool of people who can engineer known viruses. 

Over the past 15 years, the cost and difficulty of editing genomes have plummeted. In 2012, to modify a specific site in a genome, you often needed access to custom-engineered proteins that cost upward of $5,000 and still tended to work poorly, according to Olivia Scharfman, a biotechnology fellow at the Institute for Progress. Thanks to the gene-editing tool CRISPR, a scientist with minimal lab training can execute such a modification for as little as $30. Meanwhile, DNA synthesis has also grown radically more affordable.

This might not be such a big deal, if dangerous biological materials were hard to legally access. But Esvelt and his colleagues recently demonstrated that this isn’t the case. As an experiment, they had a student place orders for pieces of the 1918 influenza genome using a pseudonym and fake position. Thirty-six of 38 DNA synthesis companies shipped the genomes without asking whether the applicant had government permission or sound intentions. Together, these fragments were enough to create the virus many times over.

Crucially, in Esvelt’s account, reverse engineering known viruses using modern tools does not require elite skills. Any random biology graduate student may be up to the job. 

And AI may be making such rudimentary viral engineering even easier. 

In theory, a chatbot can help you plan a bioweapons attack in much the same way that it can walk you through fixing a dishwasher: By finding and synthesizing the internet’s vast trove of information, offering step-by-step instructions for navigating unfamiliar challenges, and fielding all troubleshooting queries that might subsequently arise.

And there’s evidence that AI models will in fact provide would-be bioterrorists with precisely this kind of assistance.

In a 2023 study, MIT researchers asked students to seek guidance on engineering a pandemic from that era’s chatbots. Within an hour, the AI models had suggested four promising pathogens, offered detailed protocols for manufacturing them with with synthetic DNA, provided a list of DNA synthesis retailers with low screening standards, and advised that — if users still found themselves unable to make the virus, even with all this help — they could purchase the assistance of a contract research organization to do some of the bioengineering for them. 

And of course, frontier models have gotten a lot better since 2023. Last year, SecureBio, Esvelt’s biosecurity nonprofit, found that one OpenAI model outperformed 94 percent of expert virologists on troubleshooting complex laboratory problems. 

Anthropic’s internal research has produced similar results. In one experiment, the company tasked amateurs with drafting a bioweapons acquisition plan. It gave some participants access to basic internet resources and others the assistance of Claude models (with the safeguards turned off). Those working with AI developed far more viable blueprints, according to the biodefense experts who scrutinized the proposals for critical errors.

Thus, there’s reason to think that the number of people with the skills and resources necessary for reverse engineering a virus from genomic blueprints is sharply rising — and with it, the risk that a bloodthirsty nihilist or ideologue gets their hands on a dangerous pathogen.

AI might help scientists learn too much

What’s even worse than bringing back smallpox? Esvelt’s second concern is that these same technological forces — combined with top scientists’ often reckless ambitions — will lead to the discovery or development of a supervirus, a new or custom-designed pathogen even more dangerous than what nature has come up with in the past.

Today, some scientists are actively trying to discover — and publicly identify — novel viruses circulating in nature that could theoretically trigger the next pandemic. Others conduct so-called gain-of-function experiments, altering pathogens to discern which mutations make them more transmissible, lethal, or difficult to detect (some have alleged that Covid-19 emerged from precisely this kind of research, though that theory is widely rejected among epidemiologists). This work is generally intended to anticipate future epidemiological threats, so that we can develop vaccines before they arrive. But their work could inadvertently provide bad actors with recipes for reverse engineering mass-casualty bioweapons. 

Separately, advances in gene editing and AI may enable breakthroughs in viral engineering that yield apocalyptic pathogens. 

A recent study from researchers at Stanford and the Arc Institute fed such fears. The scientists trained an AI called “Evo” on the DNA sequences of bacteriophages, the simple viruses that infect bacteria. They then used this “genomic language model” to generate blueprints for hundreds of bacteriophages found nowhere in nature. Sixteen of these designs yielded viable pathogens, some of which were better at killing bacteria than their natural ancestors. 

What is clear, however, is that novel pathogens are among the greatest security threats our species faces.

Theoretically, scientists may one day be able to engineer novel human viruses using the same basic techniques. What’s more, even if genomic-language models fail at that task, they might still be able to help users tweak existing viruses in ways that render them more transmissible, deadly, or vaccine-resistant.

These discoveries could theoretically filter down to amateur bioterrorists or well-funded extremist groups like Aum Shinrikyo. Alternatively, a state-sponsored weapons program could develop a catastrophic supervirus, then unleash it on the world through a lab leak or (perhaps, more improbably) a deliberate attempt at genocide. 

There is some precedent for the former scenario: The Soviet Union tried to weaponize smallpox and the Marburg virus, despite the considerable hazards and dubious military utility of such bioweapons. In the process, Soviet scientists accidentally triggered a smallpox outbreak during testing, while also letting anthrax leak from a laboratory, killing at least 64 people.

Bioweapons ain’t easy

All this said, there are reasons to question whether AI is truly making a synthetic pandemic more likely. 

Chatbots may solve some of the challenges facing an amateur biologist with omnicidal aspirations. But it doesn’t necessarily clear the biggest obstacles in that person’s path. 

Viral engineering is a complex, difficult, and physical undertaking. Performing it successfully requires what scientists call “tacit knowledge” — practical skills and understandings that are difficult to explain in words: knowing how to suppress the hand tremors that could rip delicate DNA strands, or how to recognize when the DNA in your solution has reached the proper concentration, or how to smell when a sample has been contaminated.

And these are things that Claude still can’t reliably impart. In studies, amateurs with access to a large language model tend to perform better on written biological challenges — but not on actual lab work. In a 2026 randomized study, non-scientists working with frontier AI systems were no more likely to complete a multistep laboratory assignment than those relying on the internet alone. 

Of course, tacit knowledge wouldn’t necessarily be a problem for aspiring bioterrorists in the mold of Seiichi Endo — graduate-trained scientists with extensive lab experience. But at a minimum, chatbots don’t appear to have turned every malcontent who once took AP Bio into a competent virologist.

And while AI will likely help top biologists advance their field’s frontiers, the barriers to custom engineering doomsday viruses remain formidable. The “Evo” experiment is arguably a case in point. Although the model did generate working blueprints for a few novel bacteriophages, nearly all of its suggested genomes didn’t work. Researchers had to arduously test hundreds of DNA sequences to arrive at 16 viable strands. And engineering slightly modified versions of an extremely small, well-known bacteriophage capable of infecting E. coli in a lab — and creating a novel virus more fearsome than any nature has yet devised — aren’t especially similar tasks. 

To offer a strained analogy: The fact that I can dunk on Victor Wembanyama in NBA 2K26 does not imply that I’m making progress toward being able to dunk on him in real life.

Even in the age of CRISPR and ChatGPT, biology remains an exceptionally difficult discipline that resists reliable modeling. The effects of modifying a single gene can often yield unpredictable and inconsistent results, varying with host biology, immune responses, and environmental conditions that scientists only partly understand. AI may help us overcome these complexities. It also might not.

We need to be preparing for the next pandemic

Scientists disagree about how serious a risk synthetic pandemics pose — and how much AI is magnifying it.

What is clear, however, is that novel pathogens are among the greatest security threats our species faces. Even if synthetic biology and artificial intelligence did not exist, natural selection would keep churning out new viruses — and periodically hitting upon ones that befuddle human immune systems. 

For this reason, there would be an urgent need to make our societies less vulnerable to future pandemics, irrespective of AI’s implications for viral engineering. In a world where chatbots and CRISPR are plausibly making bioterrorism easier, failing to invest aggressively in public health is all the more reckless.

The good news is we already know — from hard-earned experience — many steps that we can take to reduce the risk of a new pandemic and prepare to respond to one if needed. 

Governments can limit the destructive power of novel contagions by investing in vaccines that protect against entire families of viruses (or ideally, all of them) and broad-spectrum antivirals; raise our odds of catching outbreaks early by funding meticulous wastewater and clinical surveillance; ensure that essential workers don’t have to choose between staying alive and keeping civilization going amid a pandemic by developing and then stockpiling foolproof personal protective equipment; make indoor spaces less hospitable to airborne viruses by upgrading ventilation and filtration systems; and reduce the the threat of bioterrorism by mandating rigorous screening of synthetic-DNA orders.

Fears that humanity will be done in by superintelligent machines are currently galvanizing media attention and political concern. And this may be warranted. But we should pay at least as much attention to our species’ age-old, organic adversaries as we do to our hypothetical, robot ones — and not just because the latter could start teaching the former new tricks.

This story appeared in The Logoff, a daily newsletter that helps you stay informed about the Trump administration without letting political news take over your life. Subscribe here.

Welcome to The Logoff: Donald Trump is escalating his fight with the media. 

What happened? Trump announced he was banning three media organizations — CNN, MS NOW, and Politico — from the White House, “effective immediately.” 

In a Truth Social post Friday afternoon, Trump wrote that the ban was “a result of their constant ‘reporting’ FAKE NEWS!” and that other outlets would follow. 

Why is Trump doing this now? We don’t know, and other than his blanket statement alleging “FAKE NEWS,” it seems like Trump doesn’t either. In the Oval Office on Friday, Trump said that “it’s really just cumulative stories of the last two years. You get sick of it.”

That complaint — “fake news” — is a long-running Trump attack that generally refers to unfavorable but true stories about him and his administration. 

Can he do this? Probably not, though it might take a while for journalists to get their access back. The ban sure looks like a First Amendment violation, and also like the White House’s attempt to ban the Associated Press last year as part of a dustup over the Gulf of Mexico, which a judge ultimately blocked. 

At the time, US District Court Judge Trevor McFadden, a Trump appointee, ruled that “Under the First Amendment, if the Government opens its doors to some journalists — be it to the Oval Office, the East Room, or elsewhere — it cannot then shut those doors to other journalists because of their viewpoints.” 

What’s the big picture? Trump has been flinging around “fake news” accusations for a long time now, but his attacks on the press and other media — like late-night shows — have grown more serious in his second term. 

The Pentagon, under Defense Secretary Pete Hegseth, banned press from its press office earlier this year and has tried to force reporters covering the building to sign a restrictive new press policy (that move was struck down in March).

Trump has also repeatedly threatened broadcast licenses for networks like ABC and NBC; last month, ABC sued over an FCC review of its licenses.

And with that, it’s time to log off…

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