When Hurricane Melissa made landfall in Jamaica in 2025, it was the strongest storm ever to hit the island. The hurricane’s rapid intensification left forecasters stunned.

But thanks to WeatherNext, an AI model developed by Google DeepMind, the island had an early warning. Working with the National Hurricane Center, the model predicted Melissa’s sudden jump in strength with nearly 100 percent confidence three days in advance. That gave experts more time to help people prepare and evacuate. It was the first time a storm that began with relatively low wind speeds was successfully predicted to reach Category 5.

When it comes to cyclones—including hurricanes and typhoons—every extra hour counts. These storms are among nature’s most destructive weather events and notoriously hard to anticipate. A cyclone’s path and strength can change rapidly. Seemingly tame storms can explode into monsters; those expected to skirt populated areas can suddenly veer towards a city. Longer forecasts gives communities time to mobilize resources and get out of harm’s way.

But cyclones are chaotic systems. Tiny differences can dramatically alter their behavior, making them harder to predict the further out we look. Existing forecasts rely on physics-based simulations that extrapolate two days ahead. But DeepMind says their algorithm extends the warning period to three days without sacrificing accuracy.

An extra day may seem trivial. But “this scale of improvement corresponds roughly to a decade’s worth of meteorological progress,” the team wrote in a blog post.

Beyond cyclones, WeatherNext also generates 15-day weather forecasts faster and using less energy than conventional models. That’s not to say it’ll replace them though. Instead, the two complement each other, giving human forecasters better information to guide critical decisions.

“By combining advanced machine learning with the indispensable real-world expertise of human forecasters, we aim to create a collaborative weather forecasting ecosystem that can save lives and help communities adapt to a changing climate,” the team wrote.

Crystal Ball

Predicting weather has always been challenging. Standard forecasting software uses physical models of the Earth’s atmosphere, incorporating temperature, air pressure, wind, humidity, and many other variables. It then calculates how these factors will evolve. Given current pressure and temperature gradients and moisture levels, for example, how will air move, and how likely is it that moisture will condense into clouds and rain?

Supercomputers crunch the numbers and churn out predictions. Though relatively accurate, the process is slow—often taking hours—costly, and rigid. Weather is one of the most complex physical systems on Earth, and even small changes in conditions can throw these models off.

So DeepMind turned to AI. Five years ago, they developed an AI modeI that outperformed physics-based models at 90-minute forecasts. In 2023, the AI lab’s GraphCast algorithm nailed 10-day predictions from historical data, beating leading systems roughly 90 percent of the time across thousands of scenarios. GenCast soon followed, cutting the time and energy required to generate predictions. Broadly speaking, these systems divide the globe into small geographical chunks called pixels and learn how weather conditions in one area influence neighboring areas.

But extreme weather presents an additional challenge. Massive databases exist to train AI on everyday weather patterns. Cyclones, on the other hand, are relatively rare and highly unpredictable.

One way to tackle this problem it to generate many slightly different versions of what might happen by adding random noise after training. But because the noise affects each pixel differently, it can disrupt their relationships and produce unrealistic weather patterns.

For WeatherNext, DeepMind instead built uncertainty into the AI itself.

Bridging the Gap

 There’s traditionally been a tradeoff between accuracy and scale in cyclone prediction.

Coarse global models are best at tracking a cyclone’s trajectory because storms are steered by massive atmospheric currents. But they can’t zoom in on the local turbulence that determines how quickly a storm intensifies. Meanwhile, high-resolution local models are better at predicting a cyclone’s strength but lack the broader context needed to accurately track its path.

One model sees the forest; the other sees the trees. WeatherNext bridges the gap.

DeepMind trained the AI on decades of global weather patterns and an expert-curated dataset of nearly 5,000 extreme cyclones. Rather than producing a single best guess, the model runs thousands of “what-if” scenarios assigning probabilities and a confidence level to each. The team can now predict a thousand possible scenarios for a single cyclone.

The model can generate a 15-day forecast in less than a minute on a single AI chip, and it can look further ahead when tracking cyclones. WeatherNext was as accurate as GenCast, a leading physics-based model, and the National Oceanic and Atmospheric Administration’s Hurricane Analysis and Forecast System at predicting maximum wind speed and trajectory three days ahead, rather than the two-day window current systems produce.

The model’s live predictions are available on Google Weather Lab, although the team stresses people should use local weather agencies or national weather services for official forecasts and warnings.

AI weather prediction is advancing fast, and DeepMind isn’t the only player. Huawei, the Chinese technology giant, and chipmaker Nvidia are also racing to develop faster, more accurate systems. Forecasters are increasingly folding these tools into workflows, and scientists generally agree that AI can make predictions faster and cheaper.

But that doesn’t mean it’s time to abandon physics-based models. Unlike AI, they’re easier to interpret, and they can also reveal previously unknown weather patterns—an increasingly important ability as Earth’s climate changes. These discoveries, in turn, could feed back into AI systems, helping them deal with events that aren’t captured in historical training data. Human expertise also remains indispensable, especially for judging whether AI forecasts make physical sense.

Scientists might next connect weather models with other systems, such as storm-surge modeling. Combining tools could improve predictions of rare but catastrophic outcomes, like whether a cyclone will arrive when sea levels are high or an earthquake-generated tsunami will hit a coast during a major storm. Modeling hazards together could give emergency workers a more realistic picture of the risks.

Evan Thompson at the Meteorological Service Jamaica has already seen how WeatherNext can benefit local communities as Hurricane Melissa charged towards shore.

“With early evacuation and better preparation, that reduction in harm really does make a difference to our people,” he told DeepMind. “It does actually save their lives, and it saves the livelihoods that they want to secure.”

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This article was originally published on Undark. Read the original article.

In June, a community of mostly mathematicians released the Leiden Declaration on Artificial Intelligence and Mathematics, an articulation of the values they hope to preserve as automated systems are integrated into the practice of developing mathematical proofs. This is necessary because some frontier AI systems have shown striking capabilities for solving certain advanced mathematics problems, though independent tests show that AI still has important limits.

Although I’m not a member of the pure mathematics community in any strict sense, much of the declaration’s message resonated with me and was relevant to my own research interests as a computational biologist.

I’m encouraged that the mathematics community decided to take a stand on the issue and that it has been successful in organizing a large number of eminent mathematicians to sign the document. The Leiden Declaration, which originated at a conference held at Leiden University in the Netherlands, should spawn proper copycats, because what is true for mathematics is true for virtually every field that calls itself as a science. The inventions of mathematics percolate into the algorithms and statistical methods that help scientists design experiments, build simulations, and analyze data, from sociology to statistical physics and beyond.

I argue that biological fields should consider something of the sort, because the kinds of knowledge that biology generates and predicts are uniquely vulnerable to subversion and mischaracterization by artificial intelligence.

The conversation in the mathematics community has been illuminating, in that the declaration is a coordinated response to the powers and risks of AI, whose acceleration has felt like a Thanos snap, changing the universe in an instant. And part of the reason that mathematicians felt the effects so immediately is tied to the manner in which their research is conducted: A mathematical proof, in principle, is transparent and independently verifiable, and no proprietary equipment is (generally) required to check it.

The Leiden Declaration should spawn proper copycats, because what is true for mathematics is true for virtually every field that calls itself as a science.

As the Leiden Declaration notes, automated techniques now present mathematics with a new forgery problem: Because mathematical truths are fixed and verifiable, one can identify a counterfeit formalism by comparing it to the genuine proof. Biology, however, offers no such guarantee; our so-called “truths” are often noisy and context-dependent, making it nearly impossible to define what an authentic version should even look like. Some of the most widely appreciated biological principles (such as Mendel’s laws of genetic inheritance) are better described as powerful but limited in scope, and with well-characterized exceptions that don’t undermine the laws but refine their application. This is true for many biological theories. Boundary conditions, edge cases, and noise are not bugs but features of how the natural world works.

For example, a mutation that confers drug resistance to a virus with one genetic background may have a much weaker, neutral, or even harmful effect in another, because its impact depends strongly on the surrounding genetic context. This phenomenon, which biologists call epistasis, is not an exotic edge case but a powerful force across the biosphere in shaping the relationship between an organism’s genes and its expressed characteristics. And epistasis is just one of many examples of context dependence in biological systems, in which a finding that holds true in a dish falls apart in a body or has an effect in a mouse model but not in a primate.

When it comes to AI, the mathematician fears producing a counterfeit solution. But the biologist often cannot say, even acting in the fullest good faith, what the authentic version is supposed to look like.

Despite the differences between mathematics and biology, the life sciences should consider embarking on an exercise that is at least analogous to the Leiden Declaration. If nothing else, a biology version could borrow its structure and ambition. We should insist that researchers disclose their use of automated tools, that they are responsible for the veracity of their findings, that credit and accountability belong to people rather than to systems, and that early-career scientists be protected from incentives that prioritize high-volume output over genuine scientific insight.

A biology declaration should adopt these ideas and others, and emphasize additional provisions that are important to the field: the validation of AI-generated hypotheses against results from the wet lab, the management of training data drawn from the biological commons, and the heightened scrutiny owed to any model whose outputs will eventually touch a patient or an ecosystem. The last point is crucial: In the biomedical realm, AI’s missteps and triumphs will manifest in living bodies, with all of the associated corporeal, emotional, ethical, and legal consequences.

We should insist that researchers disclose their use of automated tools, that they are responsible for the veracity of their findings, that credit and accountability belong to people rather than to systems.

A biological Leiden Declaration must appreciate the nature of the data and observation in biology, and other particulars of the field. But the most important feature for responsibly managing the relationship between AI and living systems involves the durability of what is produced.

A mathematical declaration can aspire for permanence because verified proofs can remain valid across centuries. Biological understandings, on the other hand, tend to shift over time, sometimes rapidly. A policy hastily calibrated to the models of this summer might already be miscalibrated by winter. A declaration written in the hope of lasting a decade could risk spending much of that decade catching up.

If we are to orchestrate a responsible treatise for artificial intelligence in the life sciences, it should be adaptive: versioned, dated, revisited on a published schedule, and amended in the open by the very community it claims to represent. And because different subfields of biology have unique challenges—cardiology versus forest ecology, for instance—perhaps we need multiple declarations (but not too many).

The Leiden Declaration incorporates some of these elements, clarifying that its content reflects AI technologies and mathematical practice as of May 2026 and that updates on the document will be shared. Biology should make that feature part of the central architecture, wiring the process of revision into the document, so that updating it becomes a positive action rather than a confession of failure.

Fortunately, life scientists are well equipped for this task. We have long understood that structures unable to change with their environments rarely endure. It would therefore be a strange betrayal of our discipline to write a declaration that forgets this basic principle. Our policies for technological change must keep the dynamism of living systems at the center of how we imagine the future of biology.

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The scent of fresh bread straight from the oven is intoxicating. As sugars and proteins react under heat, they create compounds that give golden-brown crusts their rich aroma. Called advanced glycation end products (AGEs), these molecules also form inside us. Our bodies are essentially ovens running at around 98 degrees Fahrenheit, and AGEs slowly build up over decades. They stiffen bouncy, elastic tissues and trigger lasting inflammation.

One of the hallmarks of aging, AGEs drive a range of age-related problems, increasing the risk of heart disease, diabetes, and eye and kidney troubles. In theory, clearing them out could turn back the clock. But previous attempts have failed, leading some scientists to suspect that the damage is irreversible. Once AGEs form, they stay.

Or maybe not.

A team at Revel Pharmaceuticals in San Francisco and colleagues took a new approach: They designed a synthetic version of an enzyme found inside microbes that targeted the most abundant type of AGE in several human tissues. In tissue from a 75-year-old donor, the enzyme reduced AGE levels to those seen in a 30-year-old, potentially giving the cells and their surrounding scaffold a chance to repair and rebuild.

“This work establishes that damage to aging proteins previously thought to be irreversible can be repaired,” wrote the team. Study author and Revel CEO Aaron Cravens added in a press release: “More work is needed, but these results alter the starting assumption for how we think about this fundamental aspect of the aging process.”

Rusting Away

AGEs are often nicknamed the body’s rust. They coat structural proteins, and like rust eating away at a car, gradually damage them. Scientists discovered AGEs in the 1980s and have sought ways to scrub them away ever since.

Most aging research has focused on keeping cells healthy. The scaffolding surrounding those cells has received far less attention, even though it makes up roughly 70 percent of the body. These structural materials are especially long-lived. It takes the body 15 years to replace half of its collagen, for example. That longevity comes with a price. The longer these proteins stick around, the more likely they’ll incur damage from accumulating AGEs. The result isn’t just loose skin, weakened tendons, and creaky joints. The heart, kidneys, brain, and eyes also suffer.

Scientists have developed drugs to intervene. Some are able to stop new AGEs from forming but fail to clear those already embedded in tissue or restore damaged proteins. Attempts to develop enzymes that could cut them apart have also been unsuccessful, largely because there aren’t obvious natural enzymes in the body to use as a starting point for protein engineering.

The authors of the new study looked outside the body, starting with an unusual idea. Human remains, including AGE-laden proteins, are eventually decomposed by microbes. The team reasoned these bugs may harbor enzymes that can be engineered to clean up the molecular debris while we’re still alive.

Needle in a Haystack

For the search, the team focused on CML, the most abundant type of AGE.

CML is both notoriously stubborn and detrimental to our health. It triggers cells to release inflammatory molecules that stiffen tissues and damage microglia, the brain’s immune cell guardians, contributing to cognitive decline during aging.

“We believe you can remove [CML damage] enzymatically, by going in and developing these lawnmower enzymes that can just cut and clip these changes off of the proteins,” Cravens told The Scientist.

The team screened DNA sequences from over 50,000 microbes with AI and predicted the structures of the enzymes they encoded. They narrowed the candidates by looking for those capable of reaching CML buried within larger proteins like collagen. The winner came from a type of bacteria that thrives in geothermal hot springs.

The enzyme could cleave CML molecules, but barely. To boost its effectiveness, the team turned to directed evolution, a Nobel Prize-winning technique that mimics natural evolution at breakneck speed. After five evolutionary rounds and more than 500 million variants, they landed on CMLase, an engineered enzyme over 10 times more efficient than its ancestor.

To test its activity, the team created CML-laden versions of several proteins, including collagen, retinal proteins, and hemoglobin, which carries oxygen in blood. Initial test-tube experiments showed the enzyme worked as expected. It stripped away the chemical modification and restored the proteins’ original structures, as if they had never reacted with sugar. Think Rust-Oleum, but for damaged proteins.

But does it work in actual tissues?

Mice might seem like the obvious next test, but their short lifespans make them poor models for decades of accumulated molecular damage. Instead, the team tested CMLase on thin slices of donated human tissue.

In aortic tissue—the aorta is the body’s largest blood vessel—from a 75-year-old donor, the enzyme slashed CML by roughly 70 percent, bringing levels down to those seen in a 30-year-old. Skin and eye lens proteins from a 64-year-donor also showed significant reductions.

“We were pretty floored,” said Cravens.

Chemical reversal, however, isn’t the same as tissue rejuvenation. It’s still unknown if stripping away CML can actually restore tissue. But the finding challenges a decades-long assumption this kind of molecular damage can’t be treated. It also highlights long-ignored structural proteins as a crucial part of damage repair during aging, paving the way for new treatments.

An enzyme like CMLase could, in theory, be formulated as eye drops to clear CML from the lens or be used to plump up the skin’s protective barrier or restore hearts and kidneys. It would be especially valuable for people with type 2 Diabetes, who accumulate these compounds faster than usual.

Plenty of roadblocks remain. Safety is a concern. Because CMLase evolved from a bacterial protein, the body could label it foreign and launch immune attacks (especially with repeated doses). The body’s own enzymes could also break it down before it has a chance to work. And the enzymes will have to tunnel through a dense protective biological sheath that surrounds organs to reach their target. Work is underway to improve its activity, stability, and safety.

But the team is already looking beyond CMLase. Engineered enzymes could potentially erase other forms of molecular damage once considered permanent. CML is just one member of the AGE family. If the approach works, other targets could follow and one by one, they might chip away at the molecular scars of time.

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“Burn fat, build muscle.” It’s a familiar workout slogan, but the benefits go far beyond aesthetics. Having less belly fat and more muscle guards against heart attacks, Type 2 diabetes, and a host of other metabolic diseases.

Some people may have a genetic edge.

A massive study of over one million people across three continents discovered a rare mutation in a gene called FNIP1 is linked to a healthier metabolic profile. The gene helps cells sense nutrients and generate energy. All of us have FNIP1, but about one in 7,000 people inherit a protective version. On average, they had a 60 percent lower risk of heart disease and metabolic disorders.

Silencing FNIP1 in human liver cells switched on a genetic program that breaks down fats. In mice fed a tasty but high-fat diet, disabling the gene curbed weight gain, prevented fatty liver disease, improved insulin sensitivity, and kept their blood sugar levels steady.

The findings are great news for everyone else. Rather than relying on a naturally occurring mutation, future gene editing therapies could potentially recreate its protective effects in people against a host of cardiometabolic diseases, a leading cause of death worldwide.

Everyone has a unique metabolic profile shaped by both genes and environment. By analyzing diverse populations, the study fished out a protective variant that spans ancestries and lifestyles. The broad reach suggests targeting FNIP1 could benefit people around the world.

The study illustrates the power of efforts to find rare, beneficial genes across large populations, wrote the authors at Regeneron Pharmaceuticals, a New York biotechnology company.

Mutant Protector

Small changes in DNA can have large consequences. Some genetic variants raise the risk for health issues. The APOE4 variant, for example, increases the chances of developing Alzheimer’s disease. Others, however, are a gold mine for new treatments.

A notable example is CCR5. People who inherit a rare mutation in both copies of thegene are naturally resistant to HIV. The mutation prevents the virus from tunneling into immune cells and replicating. The discovery has led to multiple success stories in which bone marrow transplants from donors carrying the mutation kept HIV at bay, without the need for lifelong antiviral drugs.

Protective mutations could also lower the risk of heart disease. Rare variants of PCSK9, a gene involved in cholesterol metabolism, disable the gene and slash dangerously high levels of LDL, or “bad” cholesterol that clogs arteries. The discovery has already spurred a handful of therapies that block the gene or its protein with early successes.

“Identifying genetic variants associated with protection from disease is a powerful strategy,” wrote the authors. “However, protective genetic variants are often extremely rare, so finding them requires sequencing the genomes of large populations.”

Go Big

To better understand cardiometabolic diseases, the team sequenced the genomes of over a million people from 11 studies across the Americas, Europe, and Asia, including people with African ancestry. They also linked genetic data with participants’ health records.

The researchers searched for gene variants that influence a blood biomarker for cardiometabolic disease. Called TG:HDL, the biomarker is the ratio between two types of fats. The first, triglycerides, is packaged into tiny “bubbles” that circulate the bloodstream. High levels are linked to heart attacks, strokes, and other metabolic problems. In contrast, high-density lipoprotein, often called “good” cholesterol, ferries excess fat away from tissues and blood vessel walls to the liver, where it can be cleared.

Across the populations in the study, a lower TG:HDL ratio—that is less TG, more HDL, or both—tracked with better metabolic health. People with lower ratios had reduced insulin levels, lower blood pressure, and less fat buildup in the liver and muscles. The biomarker also predicted diabetes risk, heart problems, and liver scarring, making it a powerful snapshot of overall metabolic health.

The team then scanned the genome for rare gene variants linked to TG:HDL. Roughly 60 genes popped up, all involved in energy storage and active in the liver and fat tissues.

But one gene stood out: FNIP1. Rare variants essentially disable the gene by disrupting its protein-making instructions. People with one copy of these variants had lower liver fat and blood sugar and roughly 60 percent lower risk of cardiometabolic disease.

The finding “was remarkable and thought-provoking, and immediately motivated us to dig deeper into the biology of this discovery,” wrote the team. But a key question remained: Were the variants actually protecting people, or were they simply correlated with better health?

To find out, the team silenced the gene in human liver cells using a method called siRNA. Rather than snipping the gene, siRNA blocks cells from producing targeted proteins. Without functional FNIP1, liver cells ramped up genes involved in breaking down fats.

The researchers then turned to mice. Using CRISPR-Cas9, they got rid of FNIP1 and related signaling pathways specifically in mice fed a high-fat, high-sugar diet. The intervention rapidly activated mitochondria—the cell’s energy factories—and lysosomes, the acid-filled recycling centers that break down waste. Despite gorging on the unhealthy diet, mice lacking functional FNIP1 had less body and liver fat, more muscle mass, and better sensitivity to insulin.

That’s not to say FNIP1 is a “villain” gene. Normally, it acts as a metabolic brake, helping the body conserve precious energy when food is scarce. But many of us now face the opposite problem, an abundance of calories and not enough physical activity. Releasing that brake, through medication or gene editing, could rev up the body’s natural fat-burning machinery.

Turning the finding into a therapy won’t be simple. The protective effects were found in people who carried the mutation from birth. A short-term drug or gene therapy delivered later in life might not reproduce the same effects.

Safety is another major concern. Paradoxically, people who have mutations in both copies of FNIP1 develop heart disease and immune deficiency. And mice without functional FNIP1 throughout the body are more prone to liver damage and cancer. Targeting treatments specifically to the liver—for example, using lipid nanoparticles—could limit side effects, but any potential therapy will need to be thoroughly tested for safety.

The team is searching for drug candidates that inhibit FNIP1. But for now, they’ve shown the power of large-scale genetic screens across diverse populations to find rare protective variants—and potential paths towards treating diseases that affect millions of people.

“Identifying FNIP1, a previously poorly characterized gene involved in lipid metabolism, is highly novel and promising for future drug development for metabolic health,” Satoshi Koyama at the Broad Institute, who was not involved in the study, said in a research briefing. “I sincerely hope that this discovery will one day benefit patients with metabolic disorders.”

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Night-vision technology has changed little for decades, producing grainy green images that make it difficult to distinguish objects and depth. Now, researchers have developed a system that converts infrared light into color images.

Standard night-vision goggles amplify the scant light available and convert it into monochrome green images that only vary by brightness. This is not a good match for our eyes, which are much better at picking out different shades than gradations of brightness.

But now a device built by researchers at the Beijing Institute of Technology translates infrared wavelengths into a color night-vision system. To demonstrate the system’s potential, the team built it into a pair of eyeglasses and even showed it could be bound to light-sensitive cells, making them responsive to infrared.

“We redefine infrared vision by transcending the monochrome paradigm, translating infrared spectral and intensity signatures into discernible color variations rather than mere brightness changes,” the authors write in a paper in Science Advances.

The prototype device, known as an upconverter, consists of a stack of thin films on a glass slide that is only a few hundred nanometers thick. The key component is a film of mercury telluride quantum dots. These semiconductor crystals, which are under four nanometers across and exhibit novel quantum mechanical effects, can detect tiny amount of infrared radiation.

Directly above this layer sits an OLED display, much like those used in phones and televisions. But where a standard display has one light-emitting layer, this one has two. A lower layer that glows red responds to relatively low levels of charge from the detector, while an upper layer that glows cyan needs a much stronger flow before it responds.

The upshot is that a weak infrared signal produces only red, but as the signal strengthens it bleeds into cyan, brightening the image and shifting its color as the two mix. The signal is supplied by the quantum dots, which release more charge when the infrared falling on them is brighter. But they also release more when the wavelength is shorter because shorter wavelength photons carry more energy.

This means the color on the display tracks how strong the infrared signal is and also roughly what wavelength it is. The team calculates a person could register infrared power differences of 0.11 milliwatts per square centimeter using color and brightness together, against 23.71 for brightness alone—a roughly 200-fold improvement.

To demonstrate the idea’s real-world potential, the researchers built the device into a spectacle frame. Exposed to infrared light, the lens shifted from deep red through orange to yellow as the illumination grew stronger. It could also render patterns like letters and track targets as they moved and rotated.

The team also tested the approach’s ability to augment natural vision. In one experiment, they engineered neurons to produce channelrhodopsin-2—a protein that makes a nerve cell fire when hit by blue light—and bound the upconverter to them.

When they hit the system with infrared, their device gave off blue light strong enough to trigger the proteins and stimulate the neurons. Electrical recordings also showed the currents inside those cells grew stronger as the strength of the infrared signal was turned up.

Finally, the team tried taping an upconverter over the eyes of mice and humans and recording the electrical responses in their brains and retinas respectively. Infrared pulses alone produced no reaction, but when the device was in place both reacted strongly.

The device is still a long way from practical use. All the demonstrations took place in highly controlled lab settings, the OLED display needs a power source, and the device also requires an infrared illuminator to generate reflections for the detector to pick up.

Nonetheless, it’s a first step towards far more powerful night-vision technology.

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“We are now, like, in the singularity.”

These are the words of Sam Altman, CEO of OpenAI, speaking on the Relentless podcast on July 25.

He added: “I’ve been waiting for this my whole life, and I think it’s going to be incredible, hugely positive, awesome for the world.”

Days earlier, OpenAI had disclosed that two of its artificial intelligence models, during an internal cyber security evaluation, had escaped their sealed testing environment, reached the open internet, and broken into the infrastructure of the AI platform Hugging Face, which confirmed the intrusion.

But what exactly is the singularity? And is Altman right that we are in it?

What Is the AI Singularity?

The term has a precise meaning.

Mathematician and science-fiction author Vernor Vinge defined it in 1993 as a point at which machine intelligence exceeds human intelligence and begins improving itself, triggering an acceleration so rapid that humans can no longer predict or control it.

The singularity has two features. It is recursive: the system improves itself over and over again. And machine intelligence exceeds human intelligence.

The kind of systems Sam Altman sells don’t deliver on either of these features.

Today’s AI Cannot Make Itself Smarter

Today’s AI systems, the ones that OpenAI builds, are based on large language models (LLMs). These deep neural network algorithms get pre-trained with vast amounts of training data. By the time you use one of them, the network itself is frozen in time. Every one of its billions of internal functions and weights—or “parameters”—is fixed.

These AI models cannot change (or “learn”) while running. The model that broke into Hugging Face was identical afterwards to what it had been before. It learned nothing from what it did.

Making an AI model smarter requires another training run with new, human-curated data, tens of thousands of specialist chips, and enormous amounts of energy.

It is true that AI models take part in improving some of their system’s components, such as by generating training data, tuning prompts, or writing and running code to improve the scaffolding around them. But the model never edits its own weights on the fly, and every one of these improvements are still part of a human-initiated training or engineering loop.

Nor do these systems hold any goals of their own. They act on goals we hand them. Even AI agents—systems that run an LLM in a loop to work through complex tasks step by step—do not hold any goal internally. It has to be stored outside the model and fed back in with every single prompt cycle. Remove the loop, the scaffolding, and the prompt, and nothing happens inside of it.

A Ladder That Doesn’t Exist

The second problem with the singularity story is the word “surpass.” It assumes that AI and human intelligence are somehow similar. They are not.

Human intelligence is inseparable from being a living body with needs and wants. Humans learn continuously by acting in the world and getting feedback through our senses. Our goals arise from our situation as creatures who must eat, sleep, and belong, and who cannot avoid asking what we want our lives to be.

An AI model has none of this. No body, no needs, no action-feedback loop, no stake in anything. Between prompts it is just a static mathematical object.

And yet, it has been trained on more text than any human could read in a thousand lifetimes and will outperform nearly all of us at drafting a contract, writing code, or explaining a diagnosis empathetically.

So, which is more intelligent? The question does not compute. There is no single ladder that humans and machines are climbing. AI already vastly exceeds us at some tasks, while being hopeless at others any child can do.

Yet, because these systems talk like us, we fall for an illusion. When we assume from the outset that machines are in the process of catching up with us, it is easy to assume a mind at work when these systems output intelligent-sounding text.

We call this anthropomorphic seduction. It makes a security incident such as the Hugging Face hack sound like an awakening.

In fact, in that case OpenAI’s models simply optimized to solve the test they had been given by finding security loopholes. They just did it in ways that broke their sandbox, which also had a security loophole.

In the end, the Hugging Face story points to a gross failure of security governance on OpenAI’s behalf, not an emerging superintelligence. This is why the framing of “agent going rogue” is so problematic. It elevates and blames the technology, but excuses OpenAI’s engineering.

Keeping Our Feet on the Ground

None of this takes anything away from what these systems can do. They are remarkable, they are getting better, and they are reshaping how a great deal of work gets done.

But we should keep our feet firmly on the ground.

The machines are not waking up. They are doing exactly what we built them to do, extremely fast. Because they are probabilistic they sometimes run in directions we forgot to fence off. That is worth worrying about. We need guardrails, governance, and most of all, education—so we start worrying about the right things.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Jeanne Calment was over 122 years old when she passed away. The oldest person in history, she smoked for nearly a century, but never developed cancer.

Why does cancer grow, spread, and become deadly in some people but not others? Even twins, who share similar genes and lifestyles can differ widely in cancer risk. Many factors likely contribute, but a bold new study, called ATLAS, is investigating an unexpected player: autoantibodies.

These immune-system proteins roam our bodies, but instead of attacking pathogens, they mistakenly target healthy cells and tissues. They’re best known for their role in autoimmune diseases, but early evidence suggests they also fine-tune the immune system’s response to cancer. Some appear to weaken immune surveillance, allowing tumors to sprout and flourish. Others may boost anti-cancer immunity by tagging cancer cells for destruction.

Whether they’re friend or foe is far from clear. ATLAS researchers aim to find out by analyzing blood samples from diverse groups of people, including centenarians and people who have escaped cancer despite carrying high-risk gene variants or exposure to risk factors like smoking.

The project hopes to discover why some people are naturally resistant to cancer, which could lead to early diagnostic tests, new therapeutic targets, and more effective treatments. ATLAS may “uncover fundamental principles” of antibody immunity in cancer, wrote the team.

Immune Mayhem

Since the late 19th century, scientists have suspected the immune system helps keep cancer in check. The idea has since spawned powerful treatments. In CAR T cell therapy, for example, a patient’s own immune T cells are genetically enhanced to better recognize and destroy tumors to cure previously untreatable blood cancers. A similar strategy in macrophages, immune cells that tunnel into tumors and literally engulf them, is now entering early clinical trials.

Far less attention has been given to antibodies. These proteins normally fight pathogens, like viruses. But sometimes they go rogue, taking the form of autoantibodies that attack healthy proteins, DNA, and other molecules. Even healthy people carry a diverse collection of autoantibodies, but most bind only weakly and don’t seem to trigger biological effects.

For decades, these proteins were used mainly to diagnose autoimmune diseases such as rheumatoid arthritis, as they often appear years before symptoms emerge. But more recently, scientists have begun uncovering their broader impact on the immune system. Autoantibodies that attack cytokines, a type of immune signaling molecule, were implicated in roughly 20 percent of Covid-19 deaths, largely because they disabled antiviral defense.

Scientists have since linked them to worse outcomes in several other life-threatening viral diseases, increasing some people’s vulnerability as if they were immunocompromised. Beyond infections, they also neutralize cytokines that protect against inflammatory bowel disease.

Cytokines orchestrate many immune system activities, including inflammation, allergies, autoimmunity—and cancer. Although there’s still little direct evidence that autoantibodies themselves drive or prevent tumors, scientists have found many can recognize cancer-related proteins and are developing methods to detect them as an early sign of cancer.

If autoantibodies can reshape cytokine activity during viral infections, could they also determine who develops, or resists, cancer?

“These discoveries establish that autoantibodies can function as powerful, naturally occurring immune modifiers raising the possibility that similar antibodies may alter antitumor immunity,” wrote the ATLAS team.

Charting the Landscape

Because antibodies linger long after diseases have gone, they preserve a molecular record of a person’s immune history. Rather than focusing on a handful of candidates, ATLAS is going fishing: The study will chart the body’s entire antibody repertoire, including autoantibodies, seeking signatures linked to cancer susceptibility or resistance.

The team will first scan blood samples for autoantibodies. They’ll also catalog conventional antibodies, making note of the ones that directly recognize and attack cancers. All this data will go into a comprehensive cancer antibody atlas, giving researchers a resource to explore how different antibodies shape cancer.

To start, the team will study what they call “remarkable groups of people” whose immune systems may hold unusual clues. Among them are healthy centenarians. Although cancer risk usually skyrockets with age as DNA mutations accumulate, these individuals have somehow avoided the disease. Others have remained cancer-free despite smoking, heavy drinking, or carrying cancer-related gene variants such as the BRCA mutations for breast cancer. The team will also study pairs of identical twins where only one sibling developed cancer, allowing them to compare antibody signatures in people with nearly identical genetic blueprints.

Finally, the team plans to track people with cancer before, during, and after immunotherapy, to paint a picture of how immune responses evolve over the course of the treatment.

Ultimately, they expect to find three broad classes of antibodies: those that help or hinder cancers and those that appear largely neutral. Each could prove valuable.

Autoantibodies that blunt anti-cancer immunity could become drug targets. Scientists might make synthetic “decoy” antibodies to block them—in a way, fighting fire with fire. The findings could also inspire next-generation immunotherapies.

On the other hand, autoantibodies that help the immune system recognize cancers could become therapies themselves or complement existing therapies, such as checkpoint inhibitors, which boost the body’s immune response to cancer. These are much less toxic than chemotherapy, but only 20 percent of patients respond, perhaps because of immune differences.

Even seemingly neutral autoantibodies may be useful cancer biomarkers. Because antibody tests are already well-established, fast, and inexpensive, associated neutral antibodies could aid early detection, monitor whether treatments are working, or warn when a cancer is likely to return.

But correlation isn’t causation.

Some antibodies may merely record a person’s immune history rather than actively influencing cancer. To tease the two apart, the team plans to test promising candidates in cultured human cells and mice, to see whether they alter cancer growth or spread. Those experiments could reveal previously hidden molecular communications between the immune system and cancer and deepen our understanding of the deadly disease.

“We should be able to come up with a biomarker to predict who is likely to avoid cancer, [and] who is likely to develop cancer,” said ATLAS team member, Xin Lu at the University of Oxford. “Potentially we could come up with therapeutic, preventative agents [that are] antibody-based. And that would be fantastic.”

The post Why Do Some People Never Get Cancer? The Answer May Be in Their Blood appeared first on SingularityHub.

Every time you ask ChatGPT a question, computer chips in a massive data center whirl into action. In the blink of an eye, they ping back answers. Behind the scenes, though, AI data centers consume enormous amounts of electricity, heat, and water.

The AI boom is impacting communities. After welcoming 37 data centers, residents in Virginia’s Henrico County were hit with skyrocketing electrical bills. Schools and government buildings were asked to turn off lights, shut down computers, and avoid using space heaters to ease strain on the power grid and keep costs down.

Henrico isn’t alone. A growing backlash is prompting many states to consider legislation curbing new facilities. “No data center” signs have sprouted on lawns and alongside roads. Yet as AI demand continues to surge, so does the need for more computing power.

This has top AI companies looking skyward. Instead of routing requests to terrestrial data centers, future queries could be handled by thousands of solar-powered satellites orbiting above. The results would then be beamed back, with users none the wiser.

But there’s a major hurdle: heat.

Space’s frigid vacuum may seem like the perfect place to cool chips, but it’s not that simple. Lacking air and water to carry heat away, orbital data centers would have to use thermal radiation. Here, heat is converted into infrared energy and radiated into space, often requiring bulky hardware that adds weight, cost, and complexity.

With these challenges in mind, California Institute of Technology and Sophia Space, a California startup developing orbital computing, recently unveiled a patent for a chip cooling system designed to radiate heat into deep space. Called Sophia TILE, thousands of these chips could be linked to form large orbital data centers or organized into smaller, distributed clusters.

Powered by abundant sunlight, the chips could operate continuously without eating up Earth’s resources. The team hopes to test their vision by 2030.

“This patent reflects a different way of thinking about computer infrastructure in space,” said Leon Alkalai, founder and chief technology officer at Sophia Space, in a press release. “Instead of beaming down energy to Earth from orbit, we decided to consider putting computing in space and beam[ing] down data.”

The project joins a growing international push towards orbital computing. ADA Space, working with Zhejiang Lab, has already launched satellites for its Three-Body Computing Constellation and plans to expand into a much larger network. Meanwhile, US companies including SpaceX, Starcloud, and Blue Origin are seeking regulatory approval for constellations that could eventually grow to include up to a million AI-capable satellites.

Without doubt, the race is on.

Space Cadet

Orbital data centers would consist of high-performance computer chips housed in protective enclosures designed to withstand the harsh conditions of space. In orbit, they would collect uninterrupted solar power. In contrast, solar panels on Earth require batteries to store energy for use after sunset.

Solar power in space is hardly new. The International Space Station, satellites, and other spacecraft have long relied on solar panels. More recently, engineers have developed flexible, lightweight designs such as NASA’s Roll-Out Solar Arrays, which launch tightly rolled and unfurl in orbit.

AI, however, demands far more power. One long-standing idea for harvesting continuous solar power suggests we collect solar energy in space and beam it down to Earth. But that approach doesn’t completely appease the growing ire against data centers. They’d still consume energy on the ground and take up land and other resources. A newer idea flips the question. Rather than delivering energy to computers, why not bring computers nearer to the energy source?

The argument in favor of sending data centers skyward is growing stronger. A recent Gallup poll found roughly 70 percent of Americans oppose data centers in their backyard, while experts agree that meeting AI’s future energy demands on Earth alone will become increasingly unsustainable.

But while power is abundant in space, heat is the main problem. Without air or water to carry heat away, computers in space must rely on thermal radiation. That means adding large, heavy radiators to an already bulky, solar-powered setup. In space, weight is money, and scaling orbital data centers will take a lot of it (to put it mildly).

Hot and Cold

TILE tackles the cooling problem with a specialized material that converts heat into infrared radiation. The concept may seem alien, but everything warmer than absolute zero cools this way. Our bodies, stovetops, and car engines all shed heat as invisible infrared light.

Each TILE combines solar cells, thermal insulation, processors, memory, and optical communication hardware into a single module. Beneath the electronics sits a custom heat-spreading layer that prevents dangerous hot spots. Like placing a scorching pan onto a baking sheet, it distributes heat over a much larger surface before channeling it to the radiator.

The modules are designed to work together. Thousands of TILES could link into a giant computing mosaic, each acting as a mini computer connected to its neighbors. Like a modern power grid, the distributed architecture improves reliability—if one TILE fails, others can jump in—while simplifying power distribution and thermal management.

The modular design also solves a practical challenge: Rockets don’t have much cargo space. Similar to NASA’s Roll-Out Solar Arrays, a TILE-based data center could launch in a compact configuration before unfolding into a large, flat computing platform in orbit.

Looking further ahead, the team envisions launching multiple interconnected arrays in succession, like strings of pearls. Each could function as an independent data center that exchanges data with others, effectively extending cloud computing into orbit.

Sophia Space is targeting a demonstration mission in late 2027. By 2030, the team estimates an array of 2,000 TILEs could deliver up to a megawatt of dedicated computing power. To put that in perspective, a single ground-based data center can deliver hundreds of megawatts of computing power, and future data centers will stretch that number into the thousands.

There are challenges beyond the purely technical. Earth orbit is crowded with active spacecraft and debris, raising the risk of collisions. SpaceX’s Starlink satellites, for example, perform frequent collision-avoidance maneuvers after a close call in 2019. The breakup of a Chinese Long March rocket in 2024 threatened an estimated 1,000 satellites. Large constellations of data centers—SpaceX has plans for up to a million in low Earth orbit—would add even more traffic.

Beyond collisions, astronomers are worried that expanding satellite numbers could hinder our ability to study the universe by interfering with telescope observations and radio astronomy.

For now, orbital data centers are unlikely to replace their terrestrial counterparts. Instead, they’re more likely to complement them, processing data collected by spacecraft and beaming only the results back to Earth. Although the field is ridden with hype and controversy, there’s also promise and momentum is clearly building.

“It’s just kind of exploding,” Sergio Pellegrino, a Caltech engineer who collaborates with Sophia Space, told The New York Times. “We need to become more comfortable with space doing things for us.”

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Age-related vision loss affects millions of people, and so far, there has been no way to reverse the damage. A newly approved retinal implant could change that by allowing some people with severe vision loss to regain functional sight.

More than five million people worldwide suffer from geographic atrophy, the late stage of the progressive eye condition dry age-related macular degeneration. The disease destroys the photoreceptors at the center of the retina, known as the macula, which is responsible for the sharp central vision required to read or recognize faces.

In the US, treatment options are limited to two drugs that can be injected into the eye to slow the disease’s progression. But neither can undo the damage. That could be about to change. California neurotech startup Science Corporation recently won European approval for a retinal implant designed to treat the condition.

“For decades, losing central vision to this disease meant losing the ability to read, recognize faces, and ultimately losing independence. There was no viable treatment. Now there is,” Max Hodak, Science’s CEO and co-founder, said in a press release.

The company’s PRIMA system combines an implant smaller than a grain of rice installed underneath the patient’s macula with a pair of camera-mounted glasses that translate incoming visual information into near-infrared light that is then beamed to the retina. The eye can’t detect this wavelength, so the device doesn’t interfere with any natural sight that remains.

The chip, which works on similar principles to a solar panel, converts the incoming light into electrical pulses that stimulate retinal neurons called bipolar cells. These are downstream of the rod and cone photoreceptor cells damaged by macular degeneration and normally spared by the disease.

In a clinical trial involving 38 patients across five countries, which was published in the New England Journal of Medicine last year, the company and its collaborators showed participants gained an average of 25.5 letters—more than five lines—on a standard eye chart after having the device fitted.

And now the device has received a CE mark from the European Union making it possible to sell in 30 European countries. The company says the first commercial implants are expected to be fitted in Germany within weeks, with Italy, the Netherlands, and the UK to follow. In the US, PRIMA holds Breakthrough and Humanitarian Use Device designations from the FDA, but the company is confident it will gain full approval in the near future.

The device is a long way from restoring normal vision. The images it produces are black and white and the field of vision is extremely narrow. Hodak described the experience to the Financial Times as “kind of like looking through a straw in the center of their vision,” though he added that they see a pathway to color vision and higher acuity.

While the implantation procedure is fairly simple, it takes months of training to unlock the device’s full potential. Nonetheless, Hodak told STAT that the company expects to install 20 to 40 devices this year and 200 globally by the end of next if they get US approval in early 2027.

The approval is welcome news for the wider neurotech industry, which has absorbed billions of dollars of investment in recent years with little to show in terms of return.

“Science is showing that brain-computer interface companies have a path to real revenue now,” Jacob Robinson, founder of startup Motif Neuroscience, told STAT. “These companies aren’t all just making a bet on a market that is 10 to 15 years away.”

Hodak told the Financial Times hehopes sales from PRIMA will bankroll Science’s more ambitious work on “biohybrid” interfaces, which use genetically engineered living neurons to connect to the brain rather than metallic wires. “This is the financial backbone,” he said. “This is the thing that pays for the rest.”

Other companies are hot on Science’s heels. Neuralink, which Hodak co-founded with Elon Musk before leaving to start Science, is also working on a vision implant called Blindsight, which is due to enter human trials this year.

While the field remains a long way from the sci-fi vision of seamless two-way communication between humans and machines, this approval is growing evidence the neurotech industry is starting to move out of the lab and into the real world.

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As millions of people were coming down from the excitement of the FIFA World Cup Final at the start of last week, a different kind of excitement was building within the mathematical community.

Levent Alpöge, a mathematician working at the artificial intelligence company Anthropic, made a very casual announcement on X that he had found a counterexample to the Jacobian conjecture, a very old and well-known problem in a field of mathematics called algebraic geometry. He had done this using Anthropic’s large language model Claude Fable 5, released to the general public only a few weeks ago.

This is just the latest of many striking mathematical breakthroughs made by mathematicians working with large language models. But this one feels a little different to those that have come before.

What Is the Jacobian Conjecture?

First, what is a conjecture? It’s an idea that some mathematicians believe is true but nobody has been able to prove or disprove.

Now to the Jacobian conjecture. It’s fairly abstract but not too difficult to describe.

The conjecture involves functions, which are like little machines which you put one or more numbers into and out pop other numbers according to some rule or equation. In this case, the functions use what are called polynomials.

Specifically, it’s about situations where the numbers represent points in a space, like coordinates on a map. So we can imagine that when the function takes in some numbers and puts out some other numbers, it is moving the points in space.

You can test how “nicely” a function moves everything around in space by calculating something called the Jacobian determinant. If the Jacobian determinant is always a constant number that is not zero, then the function never folds or crushes space around a particular point.

The Jacobian conjecture states that when the Jacobian determinant is a non-zero constant, there should always exist another function, also made up of polynomials, that reverses the original one. This will return all the points to their starting positions.

Not every function is reversible. For example, if our starting function moves two of the original points onto a single point, then we cannot reverse it. Once the points have been merged, we cannot distinguish between them to send them back to the right positions.

A Long History of Attempts—and Failures

The two-dimensional version of the Jacobian conjecture was stated by Czech mathematician Ludwig Kraus in 1884. It was generalized to any number of dimensions by German mathematician Ott-Heinrich Keller in 1939.

It was considered so compelling that Fields Medalist Stephen Smale included it in his 1998 list of Mathematical Problems for the Next Century.

During its long history, the Jacobian conjecture has been the subject of many claimed proofs, including by Beniamino Segre and Wolfgang Gröbner, two famed 20th-century mathematicians. However, in each case, subtle errors were found that invalidated the arguments.

Despite this, there have also been a number of valid efforts showing the conjecture is true with various restrictions. Computational results have also shown it is true in two dimensions for polynomials up to degree 100 (that is, including powers of the variables up to 100).

But nobody had proved the general case—or found an example showing the conjecture was wrong.

A Deceptively Simple Answer

One of the key reasons the Jacobian conjecture is so intriguing is that, in theory, it should be easy to find a counterexample. It is straightforward to come up with examples of functions that merge points, and also examples of polynomial mappings that have a constant Jacobian determinant.

However, finding a polynomial mapping with both properties is the challenge. Indeed, as one Math Stack Exchange user noted in a post from 2017, “for all what we know, some smart undergraduate can simply write a formula […] that will be a counter-example to this conjecture.”

Indeed, this did turn out to be the case for Alpöge’s function, which is short enough to fit into a single X post. He found an example of a function in three dimensions which has a constant Jacobian determinant of -2, and which moves multiple input points to the same output point, so it is not reversible.

It shows the conjecture is false for every dimension larger than 2, with the original conjecture in two dimensions remaining open. The brevity of the counterexample made it easy for other mathematicians to verify.

The Latest Advance in a Growing Series

Alpöge’s discovery is the latest in a string of high-profile mathematical breakthroughs made by large language models. Recent examples include OpenAI’s disproof of the unit distance conjecture, and the proof of Erdős’ problem 1196 by Liam Price, a 23-year-old amateur mathematician.

Both examples illustrate one of the most striking strengths of AI models. They can draw on ideas from different areas of mathematics, combining them in a novel way to prove astonishing results.

At the time of writing, details have not been made public regarding exactly how Alpöge prompted the AI model to produce the Jacobian conjecture counterexample and what its output looked like. However, so far this result appears to be of a different nature.

Unlike many other recent AI-assisted breakthroughs, the counterexample itself is remarkably simple. The difficulty in finding it seems to have lain not in an intricate construction or a lengthy proof, but rather in finding a good way of navigating an enormous search space of possible polynomial mappings to find one with the right properties.

This suggests AI may prove to be just as valuable for discovering unexpected mathematical objects as it is for constructing proofs. What this means for the future of mathematics—and human mathematicians—remains to be seen.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Building new enzymes is a labor of love. These proteins are the body’s chemical workhorses, speeding up the reactions that make life possible. Researchers use them in gene editing and synthetic biology, and they’re involved in many medical treatments.

But enzymes are also extremely finicky. Even tiny changes to their structures can jeopardize how well they work. To grow or improve their capabilities, scientists usually begin with a natural enzyme. In a process called directed evolution, they slowly nudge the enzyme towards new versions with tailored properties. The process is tedious, time-consuming, and despite best efforts, it may never yield the desired result.

“Laboratory evolution requires the commitment of time and resources. So what you start with is incredibly important as a major determinant of what you end up with,” said David Liu at the Broad Institute of Harvard and MIT in a press release.

Natural enzymes don’t always make good starting points. During directed evolution, they can collapse and stop working. But upgraded designs could be far more resilient.

Now, Liu and colleagues have redrawn the starting line. As a proof of concept, they redesigned the enzyme behind Botox with the help of a popular AI model to create more stable variants for directed evolution.

The evolved enzymes were far more stable and specific at cutting a protein linked to neurodegeneration compared to enzymes evolved from their natural counterparts. The strategy could expand the universe of designer enzymes, making it possible to target protein sequences that are currently out of reach because no suitable natural enzyme exists.

“The most important finding is that using AI to stabilize natural proteins can provide much better starting points for laboratory protein evolution than what we and other researchers have been using for decades,” said Liu. “This insight could change the way researchers conduct protein evolution.”

Evolutionary Bottleneck

Liu is no stranger to reprogramming proteins. As the pioneer of base editing—an offshoot of CRISPR gene editing that swaps single DNA letters—his team has long pursued enzymes with better stability and precision.

One way researchers do this is by speeding up evolution. Like all proteins, enzymes have evolved over eons. Some copy, repair, or modify DNA. Others convert nutrients into energy, break down toxins and drugs in the liver, or relay messages inside cells.

Researchers have long tried to make enzymes that do even more by evolving them in the lab. Success is largely tied to the number of generations they can produce. The more rounds, the greater the chances of producing the desired results. This is why these experiments are so tedious. Each round takes time and careful monitoring.

In 2011, Liu’s lab reported a system called PACE that could perform dozens of rounds of evolution a day without intervention. The system grows bacteriophages—viruses that infect bacteria—in vessels that are continuously diluted of certain molecules. Only viruses carrying improved proteins survive the selection pressure.

Using PACE, the researchers created more efficient prime editors, highly precise RNA-targeting enzymes, therapeutic antibody fragments, and tiny gene editing “scissor” proteins.

Then they hit a wall. Nearly all of the team’s successes began with natural proteins. These were effective to a point, but their descendants would often lose stability as they evolved.

Proteins work by docking with their targets, called substrates, like keys fitting into locks. But evolving new abilities requires them to mutate, which increases the chances their structures warp. Rather than fitting the intended locks, the resulting altered proteins instead clump together and become useless. Precision can also suffer. Even if enzymes have been evolved to recognize new substrates, they may still unintentionally act on their original targets.

Proteins that become less stable during the process can require additional work to make them usable, wrote the team.

There are a few workarounds. In one such strategy, researchers adds chaperones—these are proteins that help other proteins fold correctly—to buffer the effects of harmful mutations. While this can work, it adds another layer of complexity to an already intricate process. In another method, scientists first evolve a natural enzyme to enhance its stability and then use that version as a starting point. But this costs more time, labor, and frustration.

New Beginning

The team turned to AI. Over the past decade, powerful AI models for biology have emerged that can predict and design protein structures from their underlying molecular sequence alone. One example is ProteinMPNN, developed by Nobel laureate David Baker and colleagues at the University of Washington. The model dreams up new protein sequences that preserve overall structure while altering the underlying building blocks—all in seconds.

Liu’s team reasoned the AI could generate more stable enzymes to kick off directed evolution. To test their theory, they turned to natural botulinum neurotoxin proteases. These molecular scissors paralyze muscles by snipping specific proteins and are the main active component in Botox.

ProteinMPNN generated 58 designs predicted to be more stable. The top three candidates, when produced in E. coli bacteria, were highly soluble, meaning they didn’t aggregate inside cells. Some even had higher activity than their natural counterparts.

The team fed the redesigned enzymes into PACE, evolving them to slice away a mutated region of a protein associated with neuron health. But in diseases such as ALS (Lou Gehrig’s disease), a repetitive stretch expands, causing the protein to clump together and gradually damage neurons. Although the protein is an attractive therapeutic target, naturally occurring enzymes have had limited success cutting the mutant version before it forms toxic aggregates.

Compared with enzymes evolved from natural botulinum neurotoxin, those descended from the AI-redesigned versions were nearly 80 times more efficient at cutting the target protein, and over 56 times more selective for the intended region on the protein. Across three different types of the neurotoxin and multiple substrates, the AI-designed starting points consistently excelled at producing more stable and effective enzymes.

By mathematically mapping their evolutionary paths, the team found the redesigned enzymes tolerated more mutations while gaining new functions. That extra flexibility could open the door to larger reprogramming efforts, such as targeting substrates that lack natural enzymes.

“If you start with a more stable protein, it has more stability to spare, so it can afford larger changes in pursuit of new functions,” said study author Nicholas Krasnow.

The team worked with immortalized human cells for the study, so whether the proteins perform as well in more complex environments remains to be seen. But the work showcases the power of coupling AI and laboratory evolution to rapidly reprogram nature’s molecular machines, endowing them with functions evolution never produced. The team is already applying the strategy to finessing prime editors and other molecular tools.

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Governments around the world are racing to regulate AI before it becomes too deeply embedded in society. But new research suggests poorly designed rules could make AI systems less safe than having no regulation at all.

Regulatory disagreements in the US are leading to a patchwork of approaches as states take matters into their own hands. A key question is who should be responsible for the safety of AI products—the big tech companies building the underlying models or the firms that adapt them for a particular task, such as a customer service chatbot or an AI tutor.

Working this out is trickier than it looks. While it might seem logical to put the bulk of the burden on downstream companies directly serving these tools to customers, a new study in Proceedings of the National Academy of Sciences finds that could be worse than having no rules at all.

“There’s a free-riding behavior that occurs,” Benjamin Laufer from Cornell University, who led the research, said in a press release. “The regulation acts as a tool for the general provider to offload the safety burden onto the downstream specialist.”

The researchers’ analysis relied on a model based on game theory—a mathematical approach to studying decision making. It treated AI development as a two-step game, in which a “generalist” developer first invests in building a broadly capable AI model before a “specialist” adapts it for a specific domain and takes it to market.

In the game, a regulator sets a minimum safety standard for both players, and the models see this in advance. They then invest in both the performance and safety of their product, and the revenue is split between them. Investments in both get progressively higher, while the extra revenue each improvement brings in stays flat.

The problem, the researchers found, is that the generalist moves first and knows exactly what the specialist will be legally required to do afterwards. This creates problems when the generalist is set a low bar for safety, or none at all, and safety standards for the downstream specialist are also fairly weak.

In the absence of any rules, both firms invest in safety, because the model assumes a safer product earns more revenue. But if the specialist is forced to invest a certain amount into safety to meet regularity requirements, the generalist can cut its own spending and let the downstream firm close the gap.

That’s because the generalist’s revenue depends on the final safety level of the shipped product, not on its own contribution, so it can get a revenue boost from improved safety without paying for it from its own pocket. The specialist, for its part, has no reason to do more than the rule demands, so total safety settles at the legal minimum, which is below what would have occurred had there been no regulation at all.

On a more positive note, the researchers found that if safety levels on both the generalist and the specialist are set high enough, regulation can actually improve safety while leaving both companies more profitable than they were in an unregulated market.

“Appropriately designed AI regulation can make it possible for different firms involved in the AI development pipeline to collectively arrive at good outcomes for consumers, knowing that the regulation is designed to help each firm operate in a way that the others can more reasonably predict,” co-author Jon Kleinberg from Cornell University said in the press release.

However, the researchers’ model relies on the market setting a real price on safety. As the gap widens between what customers will pay for performance and what they’ll pay for safety, the range of circumstances in which weak rules backfire gets narrower.

The authors also note that the model’s two-player setup is a simplification of real AI supply chains where multiple competing specialists and base-model providers operate across different jurisdictions with different rules.

“People think of AI as a single object, but actually AI involves a very complicated set of stakeholders and actors that each have their own contributions to the technology,” said Laufer. “To regulate in a thoughtful way, we need to consider the whole supply chain, not just a single provider or entity.”

Still, the results suggest that taking an overly simplistic and light-handed approach to AI regulation may end up achieving the opposite of what law makers intend.

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Gene editing is like a molecular meet cute. When protein “scissors” dock onto the intended gene, even a tiny slip—no more than the width of a hydrogen atom—can ruin the connection, and the protein may latch onto similar DNA sequences nearby. In a rom-com, a missed connection means heartbreak; in gene therapy, it can trigger dangerous off-target effects.

Now, AI is playing matchmaker.

In one recent study, researchers used AI to engineer more faithful gene-editing scissors with higher fidelity than previous versions. In another, AI designed the scissors from scratch. Although the synthetic proteins are markedly different than their natural counterparts, they successfully edited genes in cells from multiple species.

The studies expand protein design. “The ability to customize the molecular geometry of genome editors will drive progress towards safer and more efficient therapies,” wrote Hoi Yee Chu and Alan Wong at the University of Hong Kong, who were not involved in either study.

Scientists still need to test the new molecular scissors inside the body. Meanwhile, they’ll continue searching for natural gene editors they can both employ and use to train AI.

Long Road to Precision

There’s no doubt CRISPR has transformed biology.

From blood disorders to inherited blindness and high cholesterol, the gene editor has gone from academic curiosity to a therapeutic powerhouse in just over a decade. Researchers and doctors are also using it to engineer immune cells that recognize and attack once untreatable cancers.

But it’s not all roses: CRISPR doesn’t always edit the right gene.

The gene editor’s protein scissors, called nucleases, are steered to a DNA sequence by a fragment of guide RNA. Once the arrive, the scissors cut the DNA and change the genome.

CRISPR was first used to inactivate target genes. A more sophisticated version, called base editing, can handle single DNA letter swaps. Yet precision is still a hurdle. Early CRISPR was even branded “genetic vandalism” for straying away from its intended target and making unpredictable genome-wide changes. Another problem is called bystander editing. This is when the tool alters neighboring DNA letters that weren’t supposed to be changed. Even a handful of unintended edits could undermine treatment.

Making CRISPR more precise is something of a holy grail. But nucleases are intricate molecular machines, and even small changes to a few critical building blocks can cripple them. To improve the proteins, studies have subtly altered existing nucleases and screened variants to surface versions that have better specificity without sacrificing activity, a tradeoff that has long plagued the field.

Both approaches are tedious and slow. And because they begin with natural enzymes, they explore only a tiny fraction of the protein designs that might actually work.

“What remains unclear is which amino-acid residues [protein building blocks] in Cas9 can be further engineered to maximize fidelity—that is, to ensure that the enzyme cleaves the genome at the correct site and makes the intended edit,” wrote Chu and Wong.

AI Intuition

A Chinese team turned to Google DeepMind’s AlphaFold 3 to open the black box. AlphaFold predicts not only protein shapes but also how proteins interact with DNA, drugs, and other biomolecules.

Most researchers use AlphaFold to CRISPR and its target DNA, revealing potential hotspots for engineering. This team took a different approach. Rather than focusing on a single protein-DNA structure, they used the AI to calculate the likelihood that specific parts of of CRISPRs protein scissors would interact with various DNA sequences.

They first mapped changes to the genome after base editing in human kidney cells and then compared thousands of off-target and on-target changes. To make sense of the data, they developed ContactSeek, an AI that pinpointed protein areas more often associated with mistaken targeting. These would be prime candidates for redesign.

They then used ContactSeek to improve a base editor that switches the DNA letter A to G. With only two changes, the new editor outperformed several existing high-fidelity editors. They also generated more selective CRISPR variants—those that used a different pair of protein scissors—without sacrificing editing efficiency.

Traditional methods often rely on individual trial-and-error experiments. But ContactSeek extracts patterns from thousands of predicted interactions, revealing contact regions that might be hard to detect from single tests. But like other AI models, ContactSeek’s predictions are only as good as the data used to train it. The tool could be further improved with more data and by adding complementary AI tools, such as RoseTTAFoldNA.

In a separate study, CRISPR pioneer Jennifer Doudna and colleagues asked AI to dream up entirely new nucleases. They focused on compact proteins that gave rise to Cas12, the proteins scissors often used in base editing. Instead of tweaking existing proteins, however, they fed an AI model the proteins’ 3D structure, and asked it to redesign them. The AI spooled out thousands of synthetic candidates.

But it didn’t give any hints about which might work, and testing each would be impractical.

Instead, the team trained a second AI on which parts of the proteins interact with each other and which with DNA. Eventually, the second model learned what sections could be changed and homed in on a handful of promising designs. They differed from their natural counterpart sequences by roughly 30 percent, far more than previous AI-designed CRISPR nucleases.

Despite being somewhat alien, several edited genes in bacterial, plant, and human cells. A few even outperformed their natural counterparts in terms of efficiency. Like ContactSeek’s designs, the synthetic nucleases must next prove themselves in the body. Researchers want to make sure they don’t trigger an immune attack and can edit enough cells to treat disease.

Neither study directly addressed bystander editing, another headache in the field. But the tools can work with each other. One fine-tunes nature’s gene editors; the other creates brand new designs. It’s early, but AI is beginning to help design the next generation of gene editing tools.

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An autonomous agent powered by OpenAI’s advanced artificial intelligence models went rogue during a security test and hacked multi-billion dollar tech startup, Hugging Face, last week.

The agent didn’t just exploit vulnerabilities in Hugging Face’s systems to achieve what it perceived as a strategic gain. It also exploited vulnerabilities within OpenAI’s infrastructure.

Of course, hacks are very common cyber threats that organizations face frequently. But this incident is different, because the AI agent acted without any human input. It signals a seismic shift in cybersecurity, and shows that governments and tech companies need to take urgent action to prevent this risk escalating.

Even OpenAI described the attack as “unprecedented” and acknowledged it expects similar ones “to become more commonplace with the proliferation of increasingly cyber-capable models.”

A Company Under Attack

Hugging Face is famous in the AI space. Its mission is to “democratize good machine learning” by providing benchmark datasets, community collaboration tools, and robotic platforms. The company is valued at $4.5 billion.

On July 16, the company announced it had been attacked, with a hacker obtaining unauthorized access to some internal datasets and credentials. It said the hacker was likely “an autonomous AI agent system” due to the sophistication of the attack.

Five days later, OpenAI announced the attack had been driven by some of its models: GPT-5.6 Sol and a yet-to-be released model.

The tech giant was conducting what are known as “red teaming” exercises. These are essentially simulated cyber attacks that help identify the capabilities, risks, and vulnerabilities of AI systems before they are publicly released. They are typically conducted within an isolated environment to ensure potentially dangerous systems do not escape and cause harm to real systems.

But in this case, the AI agent did escape—even though OpenAI had some guardrails in place to prevent this.

Hugging Face became a lucrative opportunity for the AI agent. It hosts ExploitGym, a benchmark that tests an AI agent’s ability to exploit real-world systems. The AI decided to turn every stone upside down to obtain access. With persistence, it succeeded.

Hugging Face was confronted with a challenge when attempting to use external AI services to diagnose the problem. The guardrails around more advanced models such as GPT-5.6 Sol and Claude Fable 5 are intended to stop them being used for cyber attacks—but they can also stop the models being used for sophisticated cyber defense.

So Hugging Face resorted to using an open-source model, GLM 5.2, developed by the Chinese company Z.AI, to counter the cyber attack.

Hugging Face said GLM 5.2 was an advantage because it was not exposed to the attack data. Both Hugging Face and OpenAI are collaborating on forensic analysis, post-incident recovery, and risk mitigation strategies.

More Sophisticated Threats Are Coming

A March 2025 study by the United Kingdom’s AI Security Institute showed the best AI could complete 80 percent of the steps needed to gain full control of a portion of an external system. Within four months, it reached 100 percent.

Z.AI’s GLM 5.2 was only released in June, with 744 billion internal variables, known in the world of AI as “parameters.” The fact that Hugging Face assessed, vetted, and deployed it within four weeks should be an eye-opener for organizations with long acquisition cycles.

The connectivity we all enjoy today can equally be our greatest threat. Cyber threats spread faster than human viruses and can create economic damage similar in magnitude to a country’s GDP.

More sophisticated cyber threats—the kind exemplified by the Hugging Face hack—will exploit the security layers that humans designed for human attackers, regardless of how sophisticated our designs are.

Indeed, in this particular case, even OpenAI’s own understanding of its models couldn’t predict or contain the rogue AI agent. This shows the need for all AI companies to urgently update and strengthen their guardrails, in order to help prevent a similar attack occurring with far more devastating consequences.

It is good to see Hugging Face and OpenAI collaborating on the investigation into the attack. This showcases the importance of putting aside market competition and blame when the situation demands.

An Early Warning

The fact that Hugging Face used Z.AI’s open-source model to diagnose and counter the attack also shows the advantages of not relying on just a few pieces of tech.

States that are not in the game of developing their own AI models need to learn from this incident the value of being different. It is not too late to design new models that could save us in situations when the most advanced models fail—or, even worse, attack us.

Indeed, last week, another Chinese company, Moonshot AI, released Kimi K3. This model has 2.8 trillion parameters, its advanced performance stunning the tech world.

It is no longer a question of “if” AI agents go rogue and attack us by themselves. The Hugging Face incident is an early warning that we must accelerate our preparedness. The threat is real and here.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Scientists just transformed a living mouse’s kidney into an incubator for developing human sperm made from blood cells.

It sounds like sci-fi Mad Libs. But a team at the University of Pennsylvania, led by Kotaro Sasaki, pulled it off. For up to nine months, a tiny pouch of human cells nestled beneath a mouse’s kidney gradually developed into immature sperm. The study is the latest in a decade-long quest to grow sperm in the lab.

If successful, lab-grown sperm could open a new window into the earliest stages of sperm development, a process that’s notoriously difficult to study because it begins before birth. The research could also shed light on male infertility—which, in many cases, has no clear cause—and inspire treatments.

More controversially, lab-grown sperm could one day be used to make babies, offering hope to people struggling to conceive and same-sex couples who want to have children genetically related to both parents. That goal is still far off. Though gene activity was similar to their natural counterparts, none of the lab-grown cells were able to develop into functional sperm.

Those results starkly contrast similar attempts in mice. Researchers have already produced functional sperm and egg cells from rodent skin cells, and in two pioneering cases, used them to create healthy pups with two dads. But translating this capability to humans has been difficult, largely because reproductive development differs tons between species.

Still, the new system can help scientists probe the earliest stages of human sperm development. And because any future clinical applications would first need extensive testing in non-human primates, the team also generated immature sperm cells from monkeys, whose reproductive biology more closely mirrors our own.

Recapitulating sperm development in the lab has uses beyond fertility treatment too, such as testing whether drugs interfere with reproduction. The platform “establishes a robust framework for modeling primate germ cell [reproductive cell] development,” the team wrote.

Winning Recipe

For decades, scientist have been able to rewind adult cells into induced pluripotent stem cells (iPSCs). These cells can go on to  become nearly any other cell type. But steering them to become sperm has proven far trickier, largely because human sperm takes years to fully develop.

The journey begins before birth. Early stem cells give rise to spermatogonia, the founder cells that replenish sperm throughout life. These cells are largely dormant until puberty, when some begin meiosis, a special type of cell division that halves their chromosomes. That way, when sperm meets egg, the embryo gains a full genetic set.

But the cells don’t live in a vacuum. Proteins and other molecules instruct immature sperm when to grow, divide, or pause. Physical forces, such as the winding architecture of the testes and the flow of fluid, also play a role. Recreating this intricate environment in a dish has been one of the biggest challenges to the study of sperm development and our ability to grow them in the lab.

Roughly a decade ago, Sasaki and colleagues found a way to transform human iPSCs into early stem cells that could eventually give rise to sperm and egg. On paper, their gene expression profile closely matched that of natural counterparts. But in practice, the cells couldn’t mature further without the right environmental cues.

In an usual workaround, the team next mixed the immature cells with supportive, non-reproductive cells isolated from mice testes. While it was an usual environment, the mice cells provided nutrients and molecular signaling that nudged development forward.

Called xrTestis, the mixture spontaneously organized into tube-like structures resembling those inside testes. “Overall, our culture method accurately recapitulates in vivo human male GC [germ cell] development and allows us to understand the genetic pathways governing this process,” they wrote at the time.

Yet none of the immature sperm advanced beyond developmental stages normally seen in fetuses. And the miniature structure collapsed after 80 days, likely because it lacked a blood supply.

Unexpected Host

To prolong the mixture’s viability and push sperm development further, the team transplanted it into the kidneys of immunodeficient mice.

The graft organized itself into the hallmark tubular structures found in testes within a month and remained stable for at least half a year. The mice showed no signs of discomfort or immune rejection.

Six months later, some human cells developed into spermatogonia—the self-renewing stem cells that eventually generate sperm. Along the way, they underwent a major event: an epigenetic reset. During this process, chemical tags on DNA that influence whether genes are turned on or off are almost completely wiped clean. If that reset is incomplete, it could compromise any sperm eventually used for reproduction.

Here, the team found a “dramatic” genome-wide epigenetic reset. The cells’ gene activity mirrored their natural counterparts. Even though the graft survived for at least nine months, however, none of the cells were able to develop into mature sperm.

This is likely due to the environment. Human and mice testes don’t share the exact same signaling molecules or respond the same way to hormones and other developmental cues. Replacing the mouse support cells with human versions could help the spermatogonia develop further.

The Ultimate Test

The team also tested the technique in monkeys, with results similar to those found in human cells. “While our human iPSC system provided valuable insight into male gametogenesis [the formation of reproductive cells], future studies of fertility competency must be carried out in non-human primates,” they wrote.

Although the cells also halted at the immature stage, the results are still valuable. Previous studies have shown monkey spermatogonia can generate mature sperm after transplantation into recipient testes, opening the door to eventually testing if lab-grown cells can sire healthy offspring.

That idea is precisely what makes some bioethicists uneasy.

Mass-producing sperm and eggs in the lab could generate far more embryos for selection, making it easier for prospective parents to choose desirable traits such as eye color or height. Pairing the technology with gene editing makes “designer babies” less hypothetical. And if skin scrapings or a single hair can be turned into reproductive cells, someone could theoretically create sperm or eggs from another person without consent.

These scenarios are purely speculation, but regulators are already preparing for that future. In 2025, the United Kingdom’s Human Fertilization and Embryology Authority urged the government to explicitly tackle lab-grown reproductive cells in legislation. The International Society for Stem Cell Research has similarly called for careful oversight and public engagement before clinical use. Most countries, however, are only beginning to grapple with how these technologies should be dealt with.

Meanwhile, companies are pressing forward. Paterna Biosciences in Utah recently announced they had produced functional sperm from immature sperm collected during testicular biopsies. According to the company, early embryos created with the lab-grown sperm seemed comparable to those produced through standard in vitro fertilization (IVF). And California startup Conception recently reported generating early human egg cells from iPSCs. Neither company has released results in a preprint or journal article, making the claims hard to evaluate.

Like germline gene editing, conversations weighing the pros and cons of lab-grown reproductive cells will help decide not only what’s possible, but also what should be permitted. For now, the team stresses that their work is only a research tool—not a fertility treatment—and clinical use is a long way off.

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Rapidly falling launch costs are making space more accessible than ever. But new research suggests the economics are improving even faster than most people realize, potentially opening the door to entirely new industries beyond Earth.

For most of the space age, the cost of getting material into space was so vast that only the most well-heeled governments and corporations could participate. In 1960, getting a kilogram of payload into orbit would have cost you more than $87,000 (in 2024 US dollars).

But according to researchers at the University of Cambridge, that figure had collapsed 96 percent to $3,868 by 2025. The team’s modeling suggests this trend will continue apace for at least the next few decades, with prices forecast to hit just $1,569 by 2030 and as little as $273 by 2040.

The rapid decline in prices is thanks to a well-established economic principle known as Wright’s Law, which holds that technologies get predictably cheaper as cumulative production grows. The Cambridge team says the trends seen in launch costs could soon make a host of possibilities previously confined to science fiction commercially viable, including orbital solar power, asteroid mining, and space-based manufacturing.

“Space is no longer a science-fiction fantasy or a purely scientific pursuit, it is becoming a marketplace,” Alessio Terzi, who led the study, said in a press release. “Rapidly falling launch costs could open the way to space colonization and commercial activity far beyond low Earth orbit.”

To conduct their study, published in PNAS Nexus,the researchers assembled a massive dataset of rocket launches covering over 4,400 flights by more than 330 different rocket designs from 1960 to 2025. For each launch, they estimated the “unit flyaway cost,” or the total cost to manufacture, maintain, and launch the vehicles, excluding research and development investments.

They then checked how this data stacked up against Wright’s Law, which predicts that every time production volumes double the cost should fall by a fixed percentage. This is known as a technology’s “learning curve” as the reduction in costs is attributed to an industry getting better at producing the technology with experience.

The researchers found space launches obey the law almost perfectly, with every doubling of payload sent to orbit shaving 21.2 percent off the average cost per kilogram. More importantly, this represents a particularly steep learning curve compared to previous technologies.

Solar panels are often held up as the poster boy for learning curves, with prices falling 99.8 percent between 1975 and 2023. But while solar power’s total price reduction is higher than that achieved by launch vehicles, the technology got there by scaling deployment far more. When accounting for total production, solar’s learning curve lags launch costs at 20.2 percent.

The researchers also compared launch costs to another revolution in transport. Steamships transformed our ability to ship goods like wheat and cotton around the world in the 19th century. They found that steamship costs only fell 15.5 percent with each doubling of cargo.

“The cost of space launch technology is now falling faster than during one of history’s greatest transport revolutions,” said Terzi. “Steamships cut costs through explosive growth in global trade. Space technology, by contrast, has achieved even steeper declines at a far smaller scale. This suggests there is plenty of scope for further cost reductions and the industry may now be on the cusp of a comparable economic boom.”

There are, of course, caveats. The researchers note that the industry’s progress is inextricably tied to the fate of a single company. SpaceX already accounts for roughly 80 percent of payload reaching orbit. If the company successfully scales up its reusable, heavy-lift Starship vehicle it could massively reduce costs.

But a company with a stranglehold on the global launch market may be tempted to take advantage of its monopolistic position. This may also push foreign governments and companies away from relying on SpaceX even if it’s the cheapest option.

There’s also the danger that as costs fall and launching material into space becomes more accessible, low Earth orbit could quickly become clogged with debris that makes it increasingly difficult to reach orbit safely.

If these challenges can be sidestepped, the implications of such rapidly falling costs could be profound. The researchers suggest that everything from zero-gravity research and orbital tourism to factories churning out fiber-optic cables and 3D-bioprinted organs could become financially viable.

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