Scientists at Columbia University have used a precise gene-editing tool, base editing, to make changes in three disease-linked genes in early-stage human embryos. The goal wasn’t to create pregnancies, but to test the safety and limits of rewriting DNA at the very early stages of life.

The paper, not yet peer reviewed, sparked immediate controversy. Some researchers hailed it as a technical milestone that could one day prevent devastating inherited diseases before birth. Others warned it edges society closer to the prospect of “designer babies”—an idea bioethicists have argued is akin to modern eugenics.

The debate is hardly hypothetical. The work has already attracted commercial interest. New York-based Nucleus Genomics, which screens in vitro fertilization (IVF) embryos for serious genetic disorders, has also developed predictive models for complex traits such as intelligence. The company plans to sponsor future research by study leader Dieter Egli and team.

Critics worry that even experimental advances could fuel demand from wealthy patients while encouraging companies to develop and market embryo-editing technologies, despite unresolved ethical and safety concerns.

Egli argues the findings should be public precisely because these debates are no longer academic curiosity. He has repeatedly called for scientists, regulators, and the public to weigh the pros and cons of editing human embryos. As for clinical use today, his position is unequivocal: “You can’t use it. It’s as clear as day and night,” he told Nature.

Conceptual Shift

Why edit embryos at all?

Cells in an early embryo eventually give rise to every tissue in the body. Correct a harmful mutation at the start of development, and the fix could, in theory, propagate throughout a child’s entire body—and even be passed on to future generations.

The strategy could help in genetic disorders that hamper fetal development or trigger diseases in newborns. For some developmental and metabolic conditions, intervention after birth may already be too late. Even when treatment is possible, gene editors must be able to target various organs, which is an ongoing challenge.

In various efforts, scientists have already repaired disease-causing mutations in mouse embryos and fetuses, including those linked to blood disorders. But mice aren’t humans. Early embryos from the two species repair DNA damage in fundamentally different ways, making it tough to gauge whether a strategy that works in mice will succeed, or prove safe, in people. That uncertainty has fueled interest in testing gene-editing tools directly in human embryos.

Not everyone is on board. International scientific groups have repeatedly called for a temporary ban on editing human embryos, and the practice is illegal in several countries.

That didn’t stop Chinese scientist He Jiankui. In 2018, he announced the birth of gene-edited babies after using a tool called CRISPR-Cas9, claiming the changes would protect them against HIV infection. Global outrage ensued.

By then, years of research had already highlighted CRISPR’s risk. The tool cuts both strands of DNA and relies on the body’s repair machinery to stitch them back together. But the process can go awry, introducing unintended mutations, deleting large chunks of DNA, or altering the wrong locations on the DNA strands altogether. He’s reckless experiment resulted in three years of imprisonment, although he still defends the work.

Subsequent studies only deepened concerns. In some cases, CRISPR editing in human embryos caused extensive genetic damage. In one study,  it completely destroyed the chromosome that housed the target gene.

An Imperfect Upgrade

The new study tested a next-generation gene editor designed to overcome some of CRISPR’s biggest shortcomings.

Egli and team used an approach called base editing, which rewrites individual DNA letters. Unlike CRISPR, base editing only nicks the DNA strands and is generally thought to be more precise. The technology hit a major milestone last year when it helped cure a baby with a potentially fatal genetic disorder, and earlier lab studies hinted it could also succeed in human embryos.

Working with early-stage embryos, the team edited three genes with the potential to cause illness. In each case, they converted the genetic letter A to G at precise locations. One of the genes, PCSK9, regulates “bad” cholesterol levels. Mutations are associated with a high risk of heart problems. The team’s edit was designed to switch off the gene, mirroring strategies already being explored in adults.

The other two targets, HBG1 and HBG2, control production of fetal hemoglobin, an oxygen-carrying protein. The edits made here reflected a natural protective variant that could lessen symptoms in blood disorders, such as sickle cell disease and beta thalassemia.

The team found no signs of widespread DNA damage, suggesting the tool is more precise than CRISPR. But it wasn’t perfect. Many embryos emerged as so-called genetic mosaics, with some cells carrying the intended edit and others retaining their original genetic blueprint.

That’s a huge problem. As an embryo develops, unedited cells could outcompete edited ones, leaving the disease-causing mutation largely intact. In some embryos, edited cells stopped dividing altogether.

And a lack of obvious chromosome damage doesn’t guarantee safety. The edits could still trigger harmful effects that aren’t noticeable until after birth—when it’s already too late to reverse them.

Calls for Scrutiny

Egli stresses that embryo editing is still far from being ready for the clinic. “These base editors—they can have damaging effects on the embryo. So why would you use it if you don’t fully understand that?” he told Nature.

His team is now working to reduce mosaicism and plans to test the technology in embryos that have developed to roughly 100 cells. This is when fertility clinics typically evaluate and freeze embryos.

Speaking to The New York Times, fertility expert Paula Amato at Oregon Health & Science University, who was not involved in the work, called the strategy “promising.” Genomics researcher Greg Neely at the University of Sydney in Australia also praised the work: “This will go down in history in a positive way—less reckless, more careful and ethical than previous attempts.”

Others remain deeply skeptical. Critics argue that embryo editing permanently alters the genetic inheritance of future generations, who have no say in the decision. The study’s ties to Nucleus Genomics also raised eyebrows. The company previously drew controversy for developing genetic predictions for traits such as intelligence and height and for its slogan “have your best baby.”

To Kian Sadeghi, CEO and cofounder of Nucleus, embryo editing extends that vision. The technology could help couples carrying mutations who struggle to produce enough unaffected embryos for selection during IVF.

Fyodor Urnov at the University of California, Berkeley, who was not involved in the study, isn’t convinced. IVF clinics already screen embryos for many inherited disorders without altering their DNA. Given the risks, selecting an unaffected embryo is often a safer option than rewriting its genome.

“In practical terms, therefore, this preprint will solely impact the rapidly growing movement of embryo editors for purposes of ‘baby improvement’,” he said.

That movement, once taboo, is gaining steam. Yet the traits most often cited by proponents—height, intelligence, emotional regulation—are shaped by hundreds or even thousands of genes, which scientists still don’t fully understand. Such enhancements are far beyond the reach of today’s technology. Every additional edit also increases the chance of unintended consequences.

For Egli, that’s precisely why the research should be discussed openly. “Research is necessary to provide information to discourage the wrong use of a technology,” he said.

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AI is becoming more powerful, and mysterious.

Despite years of work on “explainable AI,” today’s most advanced systems remain black boxes for the most part. Scientists can observe what they do but cannot fully explain how they arrive at their conclusions or predict when they’ll fail.

As large language models (LLMs), the algorithmic engines behind popular chatbots, permeate society, researchers are warning that the window for understanding AI “minds” is rapidly closing even as the technology’s influence expands.

Last week, Eric Horvitz, chief scientific officer at Microsoft, and Robert West at EPFL in Switzerland outlined the dangers of putting AI interpretability on the back burner. They call for new AI benchmarks and better tools for unpicking machine minds.

The challenge resembles efforts to understand our own minds. Some researchers have already taken a neuroscience-inspired approach, mapping AI’s internal networks to concepts, goals, and reasoning. Others borrow from psychology, treating AI as a participant of behavioral studies.

The stakes are rising. AI tools already shape how people search for information, make decisions, and form judgments. Their answers influence everyday users and the researchers who build them.

As AI capabilities grow, our understanding of them could fall behind. “Preserving human agency must therefore remain a central goal,” the authors write.

The Black Box Conundrum

LLMs are built on artificial neural networks (specifically, a design called the transformer). Inspired loosely by the brain, these networks connect vast numbers of artificial neurons into intricate architectures. The basic idea is straightforward. Data enters the network and passes through layers of computations, which transform it into an output like text or code.

At first, that output is often wrong. But with feedback and repeated training, the network adjusts the strengths of connections between neurons and gradually improves. It learns.

After initial training, engineers turn to reinforcement learning, where algorithms improve through trial and error and further hone their responses. Another method, inspired by how the brain etches memories during sleep, reduces the tendency to forget old knowledge while learning new tasks. And self-attention, the key innovation behind transformers, allows AI to selectively focus on various words, images, sounds, or video frames at different moments, boosting efficiency and performance. Today, attention underpins nearly every major AI system.

Yet the inner workings of finished algorithms remain hidden.

Early efforts to crack open AI’s black box examined how artificial neurons responded to images, revealing that neural networks build increasingly more sophisticated “ideas” of the world. Google Brain borrowed methods from cognitive psychology to study AI behavior, while others investigated whether LLMs could mimic aspects of “theory of mind”—the ability to infer what others are thinking and feeling.

These studies laid the foundation for a popular method called mechanistic interpretability. Anthropic, creator of Claude, is leading the field. Company researchers have linked patterns of algorithmic activity to specific concepts and reverse engineered parts of neural networks to expose how internal computations shape responses.

Other tech giants are joining the cause. OpenAI is training algorithms that work in more explainable steps and building reasoning models that pause, “think,” and justify their conclusions in plain language. DeepMind is building microscope-like tools for neural networks, helping researchers peer into their decision-making process. And Microsoft has released new tools aimed at responsible use of AI.

Understanding AI, the authors write, does not require tracing every line of code or every neural-network parameter. Just as neuroscience, psychology, and sociology offer different windows into human behavior, AI can be studied at multiple levels, from how individual circuits work to observing behavior in real-world scenarios.

The challenge is that AI capabilities may be advancing faster than our ability to explain them. And some researchers believe time is running out.

Race Against the Machine

Three trends are making AI more opaque.

The first is how we evaluate AI. Increasingly, LLMs we being used to train, benchmark, and improve other models. AI “judges” now score metrics like helpfulness, rank competing outputs, detect hallucinations, and assess new releases. In a system known as constitutional AI, for example, algorithms critique their own responses using reinforcement learning and generate explanations for their reasoning. Other researchers have proposed AI debate frameworks, where multiple models challenge each another’s conclusions before a human has the last say. Researchers are also exploring automated interpretability tools. Like digital neuroscientists, AI systems are used to analyze each other—describing neurons, circuits, and behavioral patterns—to explain increasingly complex models.

Using AI to solve an AI-induced problem introduces a paradox. If AI-generated explanations become too complex for humans to verify, opacity compounds.

A second trend is the rise of AI societies. Networks of interacting AI agents are becoming more common, particularly in complex tasks such as scientific research and drug discovery. Yet as they become more sophisticated, their communication could drift from human language and reasoning, making them harder to interpret.

Studying their interactions with methods adapted from sociology could unveil unexpected norms, hidden rules, and collective behavior. The authors argue that training in the future should not only reward effective collaboration among AI agents, but also ensure humans can understand their communication.

The last trend already permeates our lives. ChatGPT, Claude, Gemini, and other LLMs listen to our woes, offer recipes, and code websites. But they also learn about humanity. Through training data and interactions, they glimpse how people think, reason, and feel. In turn, they capture core aspects of life, such as fear, anxiety, happiness, and the need for social belonging.

To be clear, the systems don’t have intentions. They’re not examining us. But even as we struggle to understand them, AI systems are building more sophisticated models of who we are.

“A striking asymmetry follows: While human understanding of AI declines, AI understanding of humans deepens, producing new forms of behavioral opacity,” the authors write.

But complacency is perhaps even more insidious. AI assistants are often optimized to be agreeable, helpful, and reassuring. Studies have found that people generally prefer AI agents that support their opinions and decisions. As AI is woven into everyday life, curiosity and skepticism may gradually give way to trust. They work. Why question how?

The authors don’t have a solution for the long-standing problem. Instead, they call for better benchmarks to measure AI capabilities and stronger evaluation methods. And while open-source projects and crosstalk between commercial companies and academia are now frequent, they say we need lasting norms of responsible disclosure. Mechanistic interpretability and AI “psychology” could build on each other.

“The goal is not just more capable AI, but AI that is more intelligible, accountable, and aligned with human aims,” they write.

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For decades, a handful of molecular switches has haunted the nightmares of cancer researchers. The switches trigger runaway tumor growth and cause the disease to spread across the body in multiple cancers. In theory, this makes them perfect treatment targets. Blocking even one could lead to drugs that are effective against a variety of cancers.

But despite considerable efforts, these switches—all of which are proteins—have escaped our most advanced cancer treatments, earning them the term “undruggable.” This is largely due to a shared trait: They all have smooth surfaces, making it difficult for drugs to interact with them.

But maybe not for much longer.

Researchers recently reported promising results for a new medication targeting a family of undruggable proteins in a clinical trial for advanced pancreatic cancer. The drug, daraxonrasib, nearly doubled survival time compared to chemotherapy, with fewer side effects. It’s not a total cure. But the treatment gives patients precious time, adding roughly 13 months after diagnosis. Patients also reported less pain and better quality of life.

Daraxonrasib is the latest in a new generation of drugs aimed at undruggable proteins. And AI-based tools are now poised to further accelerate progress in the field.

RAS Attack

The RAS family was the first group of oncogenes—or genes that drive cancer—ever discovered. The genes became a major focus in 1982 when several teams independently showed the mutation of a single DNA letter could transform RAS genes into a potent cancer trigger.

The proteins RAS genes encode are like spring-loaded molecular switches that relay signals from a cell’s surroundings. When proteins called growth factors latch onto a cell, RAS switches flip on to promote cell growth and survival, while built-in safeguards quickly turn them off again.

Cancerous mutations break this cycle. The switches get stuck in the “on” position, continuously instructing cells to grow and divide. This is, of course, a hallmark of cancer.

An ideal drug would simply switch RAS off. But most drugs are like rock climbers. They need grooves, pockets, or bumps on a protein to grab onto. Similar to a smooth rock face, RAS offers few such features. Making matters worse, different mutations subtly reshape the protein, so it’s tough to build a one-size-fits-all inhibitor.

The first RAS drug wasn’t approved in the US until 2021, nearly four decades after discovering the genes’ role in cancer. Even then, the drug targeted just one family member of three, limiting its reach to a relatively small group of patients. Many eventually developed resistance.

That’s why daraxonrasib turned heads. Developed by Revolution Medicines in Redwood City, California, the drugs switches off all three RAS family members. Rather than trying to grip the slippery proteins directly, it binds to a partner molecule that helps RAS proteins fold into their final 3D shapes. In this way, the drug hitches a ride on active RAS and shuts the proteins down.

The workaround paid off. The new study enrolled 500 people worldwide with advanced pancreatic cancer. All participants had already tried cancer therapies with limited success. On average, patients receiving daraxonrasib lived 13.2 months and spent most of that time with limited pain. The most common discomfort was a rash. Those receiving chemotherapy fared worse, living roughly 6.6 months and experienced more severe side effects.

The results don’t rival the dramatic success of CAR T cell therapies in blood cancer. In CAR T, caregivers engineer a patient’s own immune cells to recognize and attack tumors, sometimes producing long-lasting remission after a single infusion.

But the findings have energized the field. If approved, a daily daraxonrasib pill would likely be far more affordable and easier to administer than a personalized cell therapy. And because RAS mutations fuel many solid cancers—which CAR T still struggles to control—the drug could offer a new defense against deadly cancers that are largely beyond cell therapy’s reach. Combining daraxonrasib with earlier-generation RAS inhibitors may further boost its effects.

The Genome Guardian

Daraxonrasib didn’t appear overnight. Scientists used a crystallized snapshot of its target protein as a molecular blueprint. Years of medicinal chemistry followed, with scientists repeatedly tweaking candidate compounds to boost potency, improve selectivity, and minimize toxicity.

AI could dramatically accelerate similar efforts against other undruggable cancer targets. Among the most coveted is p53, often called the “guardian of the genome” for its dizzying array of roles. The protein orchestrates the activity of over 300 genes involved in DNA repair, metabolism, cell death, and inflammation, making it one of the cell’s most important defense systems.

Since its discovery in 1979, p53 has been both a holy grail and a headache for cancer researchers. Mutations in the gene are common in multiple cancers. But like RAS, the protein is flat and smooth. Some mutations destabilize its structure; others turn it into misfolded clumps. A universal p53 drug has remained elusive.

Some researchers are trying to restore the protein. In a small trial earlier this year, they tested a drug that restabilizes a common mutant form of p53. Within 21 days, tumors shrank roughly 20 percent in patients with ovarian, breast, and several other solid cancers.

Other researchers aim to selectively kill cells carrying the mutation. Using AI, a team at Baylor College of Medicine screened nearly 10 million compounds that cause mutated p53 cells to self-destruct, while sparing healthy cells. The search uncovered 83 chemically distinct candidates. One called H3 dramatically suppressed tumor growth in mice.

“These results highlight the potential use of AI-powered drug screening to investigate individual p53 mutants in the future,” they wrote. Although the approach is early-stage and only focused on one mutation, the team is hopeful it can be extended to other cancerous mutations.

Most Wanted

MYC is another formerly undruggable protein that could now be vulnerable. Roughly 70 percent of cancers have abnormal MYC activity. Normally, the protein is a master regulator of growth, directing cells to manufacture proteins, replicate DNA, absorb nutrients, and divide when needed.

Cancer finds many ways to hijack the system and keep cells in a state of runaway growth. MYC gene mutations aren’t just single-letter swaps. Sometimes the gene duplicates or is rearranged across the genome, churning out excessive amounts of the protein it encodes. This genetic diversity makes approaches using gene therapy difficult. And again, like RAS, the MYC protein’s smooth, featureless surface lacks stable anchors for drugs.

An emerging strategy is to disrupt MYC’s interaction with other proteins that it needs to function. A designer protein blocking MYC activity, for example, recently showed promise in a small trial against solid cancers. Other teams are using AI to identify drugs that limit MYC’s ability to fix damaged DNA in tumors, kneecapping their ability to divide. Meanwhile, biotechnology companies are deploying AI to map out MYC’s structure and molecular interactions in search of new ways to shut the protein down.

Daraxonrasib’s success shows that undruggable proteins aren’t untouchable. There’s a lot more work ahead to prove other similar drugs can work too. But scientists are increasingly leaning into AI during all stages of drug development to speed up the process. Maybe, one day, “undruggable” will disappear from our vocabulary altogether.

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