The human cell is a miserable thing to study. Tens of trillions of them exist in the body, forming an enormous and intricate network that governs every disease and metabolic process. Each cell in that circuit is itself the product of an equally dense and complex interplay among genes, proteins, and other bits of profoundly small biological machinery.

Our understanding of this world is hazy and constantly in flux. As recently as a few years ago, scientists thought there were only a few hundred distinct cell types, but new technologies have revealed thousands (and that’s just the start). Experimenting in this microscopic realm can be a kind of guesswork; even success is frequently confounding. Ozempic-style drugs were thought to act on the gut, for example, but might turn out to be brain drugs, and Viagra was initially developed to treat cardiovascular disease.

Speeding up cellular research could yield tremendous things for humanity—new medicines and vaccines, cancer treatments, even just a deeper understanding of the elemental processes that shape our lives. And it’s beginning to happen. Scientists are now designing computer programs that may unlock the ability to simulate human cells, giving researchers the ability to predict the effect of a drug, mutation, virus, or any other change in the body, and in turn making physical experiments more targeted and likelier to succeed. Inspired by large language models such as ChatGPT, the hope is that generative AI can “decode the language of biology and then speak the language of biology,” Eric Xing, a computer scientist at Carnegie Mellon University and the president of Mohamed bin Zayed University of Artificial Intelligence, in the United Arab Emirates, told me.

Much as a chatbot can discern style and perhaps even meaning from huge volumes of written language, which it then uses to construct humanlike prose, AI could in theory be trained on huge quantities of biological data to extract key information about cells or even entire organisms. This would allow researchers to create virtual models of the many, many cells within the body—and act upon them. “It’s the holy grail of biology,” Emma Lundberg, a cell biologist at Stanford, told me. “People have been dreaming about it for years and years and years.”

These grandiose claims—about so ambiguous and controversial a technology as generative AI, no less—may sound awfully similar to self-serving prophesies from tech executives: OpenAI’s Sam Altman, Google DeepMind’s Demis Hassabis, and Anthropic’s Dario Amodei have all declared that their AI products will soon revolutionize medicine.

If generative AI does make good on such visions, however, the result may look something like the virtual cell that Xing, Lundberg, and others have been working toward. (Last month, they published a perspective in Cell on the subject. Xing has taken the idea a step further, co-authoring several papers about the possibility that such virtual cells could be combined into an “AI-driven digital organism”—a simulation of an entire being.) Even in these early days—scientists told me that this approach, if it proves workable, may take 10 or 100 years to fully realize—it’s a demonstration that the technology’s ultimate good may come not from chatbots, but from something much more ambitious.

Efforts to create a virtual cell did not begin with the arrival of large language models. The first modern attempts, back in the 1990s, involved writing equations and code to describe every molecule and interaction. This approach yielded some success, and the first whole-cell model, of a bacteria species, was eventually published in 2012. But it hasn’t worked for human cells, which are more complicated—scientists lack a deep enough understanding to imagine or write all of the necessary equations, Lundberg said.

The issue is not that there isn’t any relevant information. Over the past 20 years, new technologies have produced a trove of genetic-sequence and microscope data related to human cells. The problem is that the corpus is so large and complex that no human could possibly make total sense of it. But generative AI, which works by extracting patterns from huge amounts of data with minimal human instructions, just might. “We’re at this tipping point” for AI in biology, Eran Segal, a computational biologist at the Weizmann Institute of Science and a collaborator of Xing’s, told me. “All the stars aligned, and we have all the different components: the data, the compute, the modeling.”

Scientists have already begun using generative AI in a growing number of disciplines. For instance, by analyzing years of meteorological records or quantum-physics measurements, an AI model might reliably predict the approach of major storms or how subatomic particles behave, even if scientists can’t say why the predictions are accurate. The ability to explain is being replaced by the ability to predict, human discovery supplanted by algorithmic faith. This may seem counterintuitive (if scientists can’t explain something, do they really understand it?) and even terrifying (what if a black-box algorithm trusted to predict floods misses one?). But so far, the approach has yielded significant results.

[Read: Science is becoming less human]

“The big turning point in the space was six years ago,” Ziv Bar-Joseph, a computational biologist at Carnegie Mellon University and the head of research and development and computational sciences at Sanofi, told me. In 2018—before the generative-AI boom—Google DeepMind released AlphaFold, an AI algorithm that functionally “solved” a long-standing problem in molecular biology: how to discern the three-dimensional structure of a protein from the list of amino acids it is made of. Doing so for a single protein used to take a human years of experimenting, but in 2022, just four years after its initial release, AlphaFold predicted the structure of 200 million of them, nearly every protein known to science. The program is already advancing drug discovery and fundamental biological research, which won its creators a Nobel Prize this past fall.

The program’s success inspired researchers to design so-called foundation models for other building blocks of biology, such as DNA and RNA. Inspired by how chatbots predict the next word in a sentence, many of these foundation models are trained to predict what comes next in a biological sequence, such as the next set of As, Ts, Gs, and Cs that make up a strand of DNA, or the next amino acid in a protein. Generative AI’s value extends beyond straightforward prediction, however. As they analyze text, chatbots develop abstract mathematical maps of language based on the relationships between words. They assign words and sentences coordinates on those maps, known as “embeddings”: In one famous example, the distance between the embeddings of queen and king is the same as that between woman and man, suggesting that the program developed some internal notion of gender roles and royalty. Basic, if flawed, capacities for mathematics, logical reasoning, and persuasion seem to emerge from this word prediction.

Many AI researchers believe that the basic understanding reflected in these embeddings is what allows chatbots to effectively predict words in a sentence. This same idea could be of use in biological foundation models as well. For instance, to accurately predict a sequence of nucleotides or amino acids, an algorithm might need to develop internal, statistical approximations of how those nucleotides or amino acids interact with one another, and even how they function in a cell or an organism.

Although these biological embeddings—essentially a long list of numbers—are on their own meaningless to people, the numbers can be fed into other, simpler algorithms that extract latent “meaning” from them. The embeddings from a model designed to understand the structure of DNA, for instance, could be fed into another program that predicts DNA function, cell type, or the effect of genetic mutations. Instead of having a separate program for every DNA- or protein-related task, a foundation model can address many at once, and several such programs have been published over the past two years.

Take scGPT, for example. This program was designed to predict bits of RNA in a cell, but it has succeeded in predicting cell type, the effects of genetic alterations, and more. “It turns out by just predicting next gene tokens, scGPT is able to really understand the basic concept of what is a cell,” Bo Wang, one of the programs’ creators and a biologist at the University of Toronto, told me. The latest version of AlphaFold, published last year, has exhibited far more general capabilities—it can predict the structure of biological molecules other than proteins as well as how they interact. Ideally, the technology will make experiments more efficient and targeted by systematically exploring hypotheses, allowing scientists to physically test only the most promising or curiosity-inducing. Wang, a co-author on the Cell perspective, hopes to build even more general foundation models for cellular biology.

The language of biology, if such a thing exists, is far more complicated than any human tongue. All the components and layers of a cell affect one another, and scientists hope that composing various foundation models creates something greater than the sum of their parts—like combining an engine, a hull, landing gear, and other parts into an airplane. “Eventually it’s going to all come together into one big model,” Stephen Quake, the head of science at the Chan Zuckerberg Initiative (CZI) and a lead author of the virtual-cell perspective, told me. (CZI—a philanthropic organization focused on scientific advancement that was co-founded by Priscilla Chan and her husband, Mark Zuckerberg—has been central in many of these recent efforts; in March, it held a workshop focused on AI in cellular biology that led to the publication of the perspective in Cell, and last month, the group announced a new set of resources dedicated to virtual-cell research, which includes several AI models focused on cell biology.)

In other words, the idea is that algorithms designed for DNA, RNA, gene expression, protein interactions, cellular organization, and so on might constitute a virtual cell if put together in the right way. “How we get there is a little unclear right now, but I’m confident it will,” Quake said. But not everyone shares his enthusiasm.

Across contexts, generative AI has a persistent problem: Researchers and enthusiasts see a lot of potential that may not always work out in practice. The LLM-inspired approach of predicting genes, amino acids, or other such biological elements in a sequence, as if human cells and bodies were sentences and libraries, is in its “very early days,” Quake said. Xing likened his and similar virtual-cell research to having a “GPT-1” moment, referencing an early proof-of-concept program that eventually led to ChatGPT.

Although using deep-learning algorithms to analyze huge amounts of data is promising, the quest for more and more universal solutions struck some researchers I spoke with as well-intentioned but unrealistic. The foundation-model approach in Xing’s AI-driven digital organisms, for instance, suggests “a little too much faith in the AI methods,” Steven Salzberg, a biomedical engineer at Johns Hopkins University, told me. He’s skeptical that such generalist programs will be more useful than bespoke AI models such as AlphaFold, which are tailored to concrete, well-defined biological problems such as protein folding. Predicting genes in a sequence didn’t strike Salzberg as an obviously useful biological goal. In other words, perhaps there is no unifying language of biology—in which case no embedding can capture every relevant bit of biological information.

[Read: We’re entering uncharted territory for math]

More important than AlphaFold’s approach, perhaps, was that it reliably and resoundingly beat other, state-of-the-art protein-folding algorithms. But for now, “the jury is still out on these cell-based models,” Bar-Joseph, the CMU biologist, said. Researchers have to prove how well their simulations work. “Experiment is the ultimate arbiter of truth,” Quake told me—if a foundation model predicts the shape of a protein, the degree of a gene’s expression, or the effects of a mutation, but actual experiments produce confounding results, the model needs reworking.

Even with working foundation models, the jump from individual programs to combining them into full-fledged cells is a big one. Scientists haven’t figured out all of the necessary models, let alone how to assemble them. “I haven’t seen a good application where all these different models come together,” Bar-Joseph said, though he is optimistic. And although there are a lot of data for researchers to begin with, they will need to collect far more moving forward. “The key challenge is still data,” Wang said. For example, many of today’s premier cellular data sets don’t capture change over time, which is a part of every biological process, and might not be applicable to specific scientific problems, such as predicting the effects of a new drug on a rare disease. Right now, the field isn’t entirely sure which data to collect next. “We have sequence data; we have image data,” Lundberg said. “But do we really know which data to generate to reach the virtual cell? I don’t really think we do.”

In the near term, the way forward might not be foundation models that “understand” DNA or cells in the abstract, but instead programs tailored to specific queries. Just as there isn’t one human language, there may not be a unified language of biology, either. “More than a universal system, the first step will be in developing a large number of AI systems that solve specific problems,” Andrea Califano, a computational biologist at Columbia and the president of the Chan Zuckerberg Biohub New York, and another co-author of the Cell perspective, told me. Even if such a language of biology exists, aiming for something so universal could also be so difficult as to waste resources when simpler, targeted programs would more immediately advance research and improve patients’ lives.

Scientists are trying anyway. Every level of ambition in the quest to bring the AI revolution to cell biology—whether modeling of entire organisms, single cells, or single processes within a cell—emerges from the same hope: to let virtual simulations, rather than physical experiments, lead the way. Experiments may always be the arbiters of truth, but computer programs will determine which experiments to carry out, and inform how to set them up. At some point, humans may no longer be making discoveries so much as verifying the work of algorithms—constructing biological laboratories to confirm the prophecies of silicon.

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Frosty pine trees rim the edge of an icy lake. Snow is falling; spa music plays in the background. A gray-haired man with a pleasant face stands beside the lake. He begins to undress. He is going swimming, he explains, to demonstrate his faith, and his opposition to science, to technology, to modernity. “I don’t need Facebook; I don’t need the internet; I don’t need anybody. I just need my heart,” he says. As he swims across the lake, seemingly unbothered by the cold, he continues: “I trust my immune system because I have complete trust and faith in its creator, in God. My immunity is part of the sovereignty of my being.”

This is Călin Georgescu, the man who shocked his countrymen when he won the first round of the Romanian presidential election on November 24, despite hardly registering in opinion polls and conducting his campaign almost entirely on TikTok, where the platform’s rules, ostensibly designed to limit or regulate political messages, appear not to have constrained him. On the contrary, he used the tactics that many social-media influencers deploy to appeal to the TikTok algorithm. Sometimes he added soft, melancholic piano music, imploring people to “vote with your souls.” Sometimes he used pop-up subtitles, harsh lighting, fluorescent colors, and electronic music, calling for a “national renaissance” and criticizing the secret forces that have allegedly sought to harm Romanians. “The order to destroy our jobs came from the outside,” he says in one video. In another, he speaks of “subliminal messages” and thought control, his voice accompanied by images of a hand holding puppet strings. In the months leading up to the election, these videos amassed more than 1 million views.

Elsewhere, this gentle-seeming New Age mystic has praised Ion Antonescu, the Romanian wartime dictator who conspired with Hitler and was sentenced to death for war crimes, including his role in the Romanian Holocaust. He has called both Antonescu and the prewar leader of the Iron Guard, a violent anti-Semitic movement, national heroes. He twice met with Alexander Dugin, the Russian fascist ideologue, who posted on X a (subsequently deleted) statement that “Romania will be part of Russia.” And at the same time, Georgescu praises the spiritual qualities of water. “We don’t know what water is,” he has said; “H₂O means nothing.” Also, “Water has a memory, and we destroy its soul through pollution,” and “Water is alive and sends us messages, but we don’t know how to listen to them.” He believes that carbonated drinks contain nanochips that “enter into you like a laptop.” His wife, Cristela, produces YouTube videos on healing, using terms such as lymphatic acidosis and calcium metabolism to make her points.

Both of them also promote “peace,” a vague goal that seems to mean that Romania, which borders Ukraine and Moldova, should stop helping Ukraine defend itself against Russian invaders. “War cannot be won by war,” Cristela Georgescu wrote on Instagram a few weeks before voting began. “War destroys not only physically, it destroys HEARTS.” Neither she nor her husband mentions the security threats to Romania that would grow exponentially following a Russian victory in Ukraine, nor the economic costs, refugee crisis, and political instability that would follow. It is noteworthy that although Călin Georgescu claimed to have spent no money on this campaign, the Romanian government says someone illegally paid TikTok users hundreds of thousands of dollars to promote Georgescu and that unknown outsiders coordinated the activity of tens of thousands of fake accounts, including some impersonating state institutions, that supported him. Hackers, suspected to be Russian, carried out more than 85,000 cyberattacks on Romanian election infrastructure as well. On December 6, in response to the Romanian government’s findings about “aggressive” Russian attacks and violations of Romanian electoral law, Romania’s Constitutional Court canceled the election and annulled the results of the first round.

Given this strange combination—Iron Guard nostalgia and Russian trolls plus the sort of wellness gibberish more commonly associated with Gwyneth Paltrow—who exactly are the Georgescus? How to classify them? Tempting though it is to describe them as “far right,” this old-fashioned terminology doesn’t quite capture whom or what they represent. The terms right-wing and left-wing come from the French Revolution, when the nobility, who sought to preserve the status quo, sat on the right side of the National Assembly, and the revolutionaries, who wanted democratic change, sat on the left. Those definitions began to fail us a decade ago, when a part of the right, in both Europe and North America, began advocating not caution and conservatism but the destruction of existing democratic institutions. In its new incarnation, the far right began to resemble the old far left. In some places, the two began to merge.

When I first wrote about the need for new political terminology, in 2017, I struggled to come up with better terms. But now the outlines of a popular political movement are becoming clearer, and this movement has no relation at all to the right or the left as we know them. The philosophers of the Enlightenment, whose belief in the possibility of law-based democratic states gave us both the American and French Revolutions, railed against what they called obscurantism: darkness, obfuscation, irrationality. But the prophets of what we might now call the New Obscurantism offer exactly those things: magical solutions, an aura of spirituality, superstition, and the cultivation of fear. Among their number are health quacks and influencers who have developed political ambitions; fans of the quasi-religious QAnon movement and its Pizzagate-esque spin-offs; and members of various political parties, all over Europe, that are pro-Russia and anti-vaccine and, in some cases, promoters of mystical nationalism as well. Strange overlaps are everywhere. Both the left-wing German politician Sahra Wagenknecht and the right-wing Alternative for Germany party promote vaccine and climate-change skepticism, blood-and-soil nationalism, and withdrawal of German support for Ukraine. All across Central Europe, a fascination with runes and folk magic aligns with both right-wing xenophobia and left-wing paganism. Spiritual leaders are becoming political, and political actors have veered into the occult. Tucker Carlson, the former Fox News host who has become an apologist for Russian aggression, has claimed that he was attacked by a demon that left “claw marks” on his body.

This New Obscurantism has now affected the highest levels of U.S. politics. Foreigners and Americans alike have been hard-pressed to explain the ideology represented by some of Donald Trump’s initial Cabinet nominations, and for good reason. Although Trump won reelection as a Republican, there was nothing traditionally “Republican” about proposing Tulsi Gabbard as director of national intelligence. Gabbard is a former progressive Democrat with lifelong ties to the Science of Identity Foundation, a Hare Krishna breakaway sect. Like Carlson, she is also an apologist for the brutal Russian dictator Vladimir Putin and for the recently deposed dictator of Syria, Bashar al‑Assad, both of whose fantastical lies she has sometimes repeated. Nor is there anything “conservative” about Kash Patel, Trump’s nominee for FBI director, who has suggested that he intends to target a long list of current and former government officials, including many who served in the first Trump administration. In keeping with the spirit of the New Obscurantists, Patel has also promoted Warrior Essentials, a business selling antidotes both to COVID and to COVID vaccines. But then, no one who took seriously the philosophy of Edmund Burke or William F. Buckley Jr. would put a conspiracy theorist like Robert F. Kennedy Jr.—another Putin apologist, former Democrat (indeed, from the most famous Democratic family in America), and enemy of vaccines, as well as fluoride—in charge of American health care. No “conservative” defender of traditional family values would propose, as ambassador to France, a convicted felon who sent a prostitute to seduce his sister’s husband in order to create a compromising tape—especially if that convicted felon happened to be the father of the president’s son-in-law.

[From the October 2024 issue: Kash Patel will do anything for Trump]

Rather than conservatism as conventionally understood, this crowd and its international counterparts represent the fusion of several trends that have been coalescing for some time. The hawkers of vitamin supplements and unproven COVID cures now mingle—not by accident—with open admirers of Putin’s Russia, especially those who mistakenly believe that Putin leads a “white Christian nation.” (In reality, Russia is multicultural, multiracial, and generally irreligious; its trolls promote vaccine skepticism as well as lies about Ukraine.) Fans of Hungarian Prime Minister Viktor Orbán—a small-time autocrat who has impoverished his country, now one of the poorest in Europe, while enriching his family and friends—make common cause with Americans who have broken the law, gone to jail, stolen from their own charities, or harassed women. And no wonder: In a world where conspiracy theories and nonsense cures are widely accepted, the evidence-based concepts of guilt and criminality vanish quickly too.

Among the followers of this new political movement are some of the least wealthy Americans. Among its backers are some of the most wealthy. George O’Neill Jr., a Rockefeller heir who is a board member of The American Conservative magazine, turned up at Mar-a-Lago after the election; O’Neill, who was a close contact of Maria Butina, the Russian agent deported in 2019, has promoted Gabbard since at least 2017, donating to her presidential campaign in 2020, as well as to Kennedy’s in 2024. Elon Musk, the billionaire inventor who has used his social-media platform, X, to give an algorithmic boost to stories he surely knows are false, has managed to carve out a government role for himself. Are O’Neill, Musk, and the cryptocurrency dealers who have flocked to Trump in this for the money? Or do they actually believe the conspiratorial and sometimes anti-American ideas they’re promulgating? Maybe one, maybe the other, possibly both. Whether their motivations are cynical or sincere matters less than their impact, not just in the U.S. but around the world. For better or for worse, America sets examples that others follow. Merely by announcing his intention to nominate Kennedy to his Cabinet, Trump has ensured that skepticism of childhood vaccines will spread around the world, possibly followed by the diseases themselves. And epidemics, as we’ve recently learned, tend to make people frightened, and more willing to embrace magical solutions.

Other civilizations have experienced moments like this one. As their empire began to decline in the 16th century, the Venetians began turning to magic and looking for fast ways to get rich. Mysticism and occultism spread rapidly in the dying days of the Russian empire. Peasant sects promoted exotic beliefs and practices, including anti-materialism, self-flagellation, and self-castration. Aristocrats in Moscow and St. Petersburg turned to theosophy, a mishmash of world religions whose Russian-born inventor, Helena Blavatsky, brought her Hindu-Buddhist-Christian-Neoplatonic creed to the United States. The same feverish, emotional atmosphere that produced these movements eventually propelled Rasputin, a peasant holy man who claimed that he had magical healing powers, into the imperial palace. After convincing Empress Alexandra that he could cure her son’s hemophilia, he eventually became a political adviser to the czar.

Rasputin’s influence produced, in turn, a kind of broader hysteria. By the time the First World War broke out, many Russians were convinced that dark forces—tyomnye sily—were secretly in control of the country. “They could be different things to different people—Jews, Germans, Freemasons, Alexandra, Rasputin, and the court camarilla,” writes Douglas Smith, one of Rasputin’s biographers. “But it was taken on faith that they were the true masters of Russia.” As one Russian theosophist put it, “Enemies really do exist who are poisoning Russia with negative emanations.”

Replace dark forces with the deep state, and how different is that story from ours? Like the Russians in 1917, we live in an era of rapid, sometimes unacknowledged, change: economic, political, demographic, educational, social, and, above all, informational. We, too, exist in a permanent cacophony, where conflicting messages, right and left, true and false, flash across our screens all the time. Traditional religions are in long-term decline. Trusted institutions seem to be failing. Techno-optimism has given way to techno-pessimism, a fear that technology now controls us in ways we can’t understand. And in the hands of the New Obscurantists—who actively promote fear of illness, fear of nuclear war, fear of death—dread and anxiety are powerful weapons.

[Autocracy in America: The end of democracy has already begun]

For Americans, the merging of pseudo-spirituality with politics represents a departure from some of our deepest principles: that logic and reason lead to good government; that fact-based debate leads to good policy; that governance prospers in sunlight; and that the political order inheres in rules and laws and processes, not mystical charisma. The supporters of the New Obscurantism have also broken with the ideals of America’s Founders, all of whom considered themselves to be men of the Enlightenment. Benjamin Franklin was not only a political thinker but a scientist and a brave advocate of smallpox inoculation. George Washington was fastidious about rejecting monarchy, restricting the power of the executive, and establishing the rule of law. Later American leaders—Lincoln, Roosevelt, King—quoted the Constitution and its authors to bolster their own arguments.

By contrast, this rising international elite is creating something very different: a society in which superstition defeats reason and logic, transparency vanishes, and the nefarious actions of political leaders are obscured behind a cloud of nonsense and distraction. There are no checks and balances in a world where only charisma matters, no rule of law in a world where emotion defeats reason—only a void that anyone with a shocking and compelling story can fill.

This article appears in the February 2025 print edition with the headline “The New Rasputins.”