Scientific research often depends on complex laboratory equipment that only specialists know how to use. But Anthropic is now rolling out a system that allows AI agents to control lab devices and autonomously carry out experiments.

Laboratory automation technology has been around for decades but getting different bits of equipment to talk to each other has traditionally been a major headache. Most instruments use their own proprietary interfaces, so connecting a microscope to a robotic arm or a liquid handler typically requires bespoke software that takes specialists weeks or even months to build.

Anthropic says its new Model Hardware Standard can reduce this process to minutes by giving devices a common language. It relies on a standardized “driver” that lets any programmable device describe itself to an AI agent, allowing the AI to handle the integration. The company announced it’s opening the system up as a research preview to an initial group of labs and manufacturers.

“Our hope is that the standard can be of use to researchers, engineers, and other practitioners in speeding up the process of discovery and experimentation in any domain that uses devices with a programmable interface,” Anthropic said in a press release.

The standard is similar to Anthropic’s Model Context Protocol, which makes it easier for AI to interact with third-party software, but the new system is aimed at hardware instead. The driver at its heart is essentially a piece of software that sits between a computer and a piece of hardware, translating instructions from one into signals the other can act on.

Most laboratory instruments already run some form of driver, but each has traditionally spoken its own dialect, which is why connecting them has required custom code that can translate between devices. Anthropic’s new driver standardizes that dialect using deliberately simple commands such as “read” or “write,” which can refer to anything from checking a temperature to setting the length of an operation.

Because every device speaks in these same basic terms, machines can find each other on a network and exchange data without a custom program to translate between them. The driver also makes it easier for the company’s Claude agents to learn how to use a device they’ve never seen before.

The standard lets users encode key details, like the weight of a robotic arm, using natural language.  They can either write out their hardware setup themselves or have an agent interview them about it. The system then turns that information into a reference file covering what a device can measure, what can be adjusted, and what safety limits apply.

Anthropic says this lets its agents orchestrate complex experimental processes across multiple instruments in often highly complicated and interactive ways. “We’ve found that Claude interacts with experiments and hardware in an exploratory manner, much as a scientist would,” the company writes. “We observed Claude make an adjustment to a laser, observe the results through a camera to assess how its adjustment moved the laser beam, and repeat the process, seeking to understand the sequence of events.”

Speaking to the Financial Times, Anthropic scientist Alek Kemeny described watching Claude locate a specific, unfamiliar structure in a live brain tissue sample during a neuroscience experiment by manipulating a microscope’s mirrors and lasers on its own. “The neuroscientist sitting there said: ‘Yep, that’s right,’” said Kemeny.

The new standard could be key to the company’s ambition to move beyond its key markets of software development and knowledge work and allow its AI to start having an impact in the physical world. But allowing AI, which is still not immune to hallucinations, to control real-world hardware carries considerable risks.

“It is an impressive proof of concept, but how do we ensure safety in the physical world? Because small errors can matter here,” Kaoutar El Maghraoui, principal research scientist at IBM, said on the company’s Mixture of Experts podcast.

That’s probably why Anthropic is only releasing the standard to a small number of partners initially, and it has committed to working with them to build safety evaluations for AI systems that are operating physical hardware. If the early launch goes well though, AI agents could soon make an impact in far greater swathes of the economy.

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There’s growing excitement in the AI industry about the idea that today’s leading models could build the next generation of the technology. But a new study recently found top AI agents struggle on the kind of genuinely open-ended research problems required to push the field forward.

Large language models have made rapid progress in many of the day-to-day jobs involved in machine learning research, such as writing code, generating and curating data, and running experiments. Last year, startup Sakana AI’s AI Scientist-v2 even managed to write a paper that cleared peer review for the prestigious International Conference on Learning Representations.

These advances have led to speculation that models are close to being able to build better versions of themselves with little human oversight—a process called recursive self-improvement. The idea underpins predictions that we may be on the verge of an intelligence explosion that could quickly lead to AI superintelligence.

In a recent paper, researchers put the idea to the test using a new approach they call shadow evaluations. This involves taking the research question from a high-quality, unpublished machine learning paper and asking AI agents to solve the problem. The original paper’s authors then grade the results. When the team tested Claude Opus 4.8 on two papers submitted to the prestigious machine-learning conference NeurIPS 2026, the authors rejected both.

“The papers were nowhere close to the mark when it came to being at the quality of a top AI conference,” Sayash Kapoor from Princton University, who co-led the study, told MIT Technology Review.

Previous efforts to get AI agents to do machine learning research have often targeted problems focused on engineering, such as reproducing previous research or training smaller models against a benchmark.

In the new experiments, the researchers challenged models with more open-ended tasks that required them to devise hypotheses, decide what evidence is needed to validate them, judge when a research direction was fruitless, and go back to the drawing board.

One research question was whether the personality traits a language model displays can be measured and adjusted by observing and editing its weights; the other attempted to detect when a model that works with tabular data has quietly stopped being reliable.

In each case, the AI researchers were given $3,000 of API credits, a budget for time on GPUs to run machine learning experiments, a dedicated Linux virtual machine, and unrestricted internet access. They were then given six days to produce a paper that could pass NeurIPS’ stringent peer-review criteria.

In both cases, the models got a good start. The agents surveyed the literature effectively, came up with opening hypotheses that mirrored those of the authors, and successfully ran hundreds of experiments.

But they quickly went off the rails. Although they could monitor their own use of time and their API and GPU budgets, they rushed through the process. One left 110 hours of unused time on the clock, and both failed to spend even 50 percent of their API budget.

Both agents also settled on a research direction within just 10 hours and failed to change approaches despite repeated negative feedback from another AI designed to review drafts of their papers. The reviewer identified problems the human authors would also flag in the final paper, but the models simply added caveats to their findings and ploughed on. Ultimately the papers received a “strong reject” and a “reject” decision from the human reviewers based on NeurIPS grading protocol.

The authors admit their approach has limitations. The reviewers knew AI had written the submissions, and some of the team are on record as doubting an imminent intelligence explosion. The original human-authored papers also took far longer than six days to produce and used many more GPU hours to reach their conclusions (though, as the researchers note, the models did not use their allocated budget in any case).

Nonetheless, the results suggest that today’s models still have some way to go before they can tackle the most challenging problems in machine learning research. Until that happens, the dream of recursive self-improvement is likely to remain a distant prospect.

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Increasingly, companies are building humanoid robots that perform impressive athletic feats to mark the field’s progress. Now, Chinese robotics company Unitree says its new “Superman” robot can run 12.66 meters per second, faster than Usain Bolt’s top recorded speed.

Getting a humanoid robot to run at all requires split-second control and has been a significant engineering challenge occupying roboticists for decades. That’s why sprinting, as well as jumping, have become popular targets for robotics companies keen to demonstrate their technology’s prowess.

Unitree’s latest demonstration pushes the boundaries by not only outrunning the fastest human ever, but also jumping around 6 feet 7 inches into the air from a standing start, a full foot more than the human record.

“This new machine has only been in development for a little over three months, with significant room for further improvement in the coming months,” Unitree said in an X post that accompanied a video of the accomplishments.

The records have not been externally verified, and the sprinting speed was a peak reading taken over a shorter stretch rather than a full 100 meters like Bolt’s record. The robot’s legs are also only 2 feet 9 inches long, according to Unitree, which results in an ungainly, arm-waving gait while running.

The effort is nonetheless impressive and adds to Unitree’s growing reputation as the company leading the pack of humanoid robot developers. And the timing of the announcement was no accident, coming just days before Unitree’s stock market debut and shortly before the World Humanoid Robot Games, which opened on August 22.

The company’s Shanghai IPO was a blockbuster, recording an initial 629 percent gain on the company’s first day of trading. It was briefly valued at around $66 billion before closing at a more modest $51 billion. However, some analysts have cautioned the excitement around the company’s technology may be getting ahead of market realities.

“The IPO is expensive, and the investment ​risk is already ​quite high,” ⁠Wang Zhuo, partner of Shanghai Zhuozhu Investment Management, told Reuters. “Unitree generates much of its sales from research and demonstrations, but ​wider application is still far away.”

But the company holds a dominant grip on the emerging humanoid market that may justify some of the hype. Chinese firms control roughly 90 percent of the global humanoid robot market, with Unitree alone shipping 5,500 of the 13,000 to 18,000 humanoids sold worldwide in 2025, the most of any manufacturer. In contrast, US humanoid champions Figure AI, Agility Robotics, and Tesla each shipped around 150 units.

China’s success is down to “a combination of policy support, public investment, mature supply chain, and advancements made in AI software and hardware,” Lian Jye Su, a tech analyst at consultancy firm Omdia, told Rest of World.

This is leading to an increasingly combative response from the US. On July 29 the Federal Communications Commission banned new imports of foreign-made humanoid and quadruped robots. The move was framed as a matter of national security, though it has also been seen as an attempt to give domestic developers a leg up.

Beijing predictably objected, with foreign ministry spokesperson Mao Ning telling a press conference that “protectionism does not make the US more competitive, and it will only hurt the interests of US companies and consumers.”

Given the rapid progress made by companies like Unitree, it seems likely it’s going to take more than trade barriers for the US to catch up. In the meantime, we might see more human athletic records fall to China’s leading humanoid developers.

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You might assume scrapping a brand new car would be terrible for the environment, but it depends on what you replace it with. New research suggests replacing a gas car with an electric vehicle can cut overall emissions even when the gas car is only a year or two old.

Transportation is the second biggest source of carbon dioxide emissions globally, and passenger vehicles contribute nearly half of them, according to Our World in Data. That means the speed at which drivers switch to electric vehicles is a critical factor in efforts to fight climate change.

But while electric vehicles may not directly emit carbon dioxide on the road, they’re only as green as the grid used to charge them. And manufacturing EVs still produces significant emissions, often more than it takes to build a gas car. That makes comparing the green credentials of electric and gas vehicles more complicated than it appears.

However, new research in Science aims to simplify the debate for cars in the US. The paper models how scrapping a gas car at various ages and replacing it with an electric vehicle affects lifetime emissions. The authors found this led to lower emissions across most of the scenarios they investigated, including cases where the gas car was barely a year old.

“I think this is really a definitive study about the carbon emissions benefits of electric vehicles, because it shows that even in such an extreme scenario, the electric vehicle is still the obvious winner,” lead author Elliott Campbell, a professor of environmental studies at the University of California, Santa Cruz, said in a press release.

“So if you’re someone who’s trying to decide whether or not to put money into keeping your gas car going, switching to an electric vehicle as soon as a financially viable opportunity comes up is absolutely the right thing to do for the environment.”

Previous research had already established that the lifetime emissions of electric vehicles are substantially less than those of gas cars, making them the obvious climate-friendly choice when buying a new car. But it was less clear when to switch if you already have a gas car.

To answer this question, the researchers worked out lifetime carbon emissions for more than 400 gas and electric vehicle models with varying efficiencies and battery sizes, while also considering things like mileage, manufacturing emissions, and the energy mix of the grid used to charge the vehicles.

A key point the researchers made is that the emissions used to build a gas car are sunk costs, identical in every scenario. That means the only figures that matter are how much fuel the gas car burns over its liftetime set against the manufacturing and charging emissions of the new one.

For an average-selling SUV on the average US grid over a 16-year lifespan—the researchers’ baseline case—scrapping the car just two years after purchase and switching to an electric vehicle cut cumulative emissions by 44 percent. The carbon emissions required to build the replacement were paid back within three years.

Across the full range of US vehicle efficiencies in the study, scrapping a gas car after just a year cut lifetime emissions in 92 percent of cases, with the average vehicle saving 58 percent. The benefit only disappears in the most extreme cases—when an electric vehicle is using more than 30 kilowatt-hours per 100 kilometers (62 miles) on a grid that emits more than 500 kilograms of carbon dioxide per megawatt-hour.

To make that more concrete, this equates to one of the most power-hungry electric vehicles on the market—for instance, GMC’s Hummer electric SUV electric pickup—charging on a coal-heavy grid that emits nearly 50 percent more carbon than the US average.

The advantage also narrows or vanishes when scrapping gas vehicles driven far below the national average mileage and hybrid vehicles driven in regions with high-emission grids, which still account for around a third of US electricity generation.

And plug-in hybrids—which have larger batteries than regular hybrids and can be charged from the wall rather than only generating electricity from the engine and regenerative braking—are almost never worth replacing. For SUVs, the benefit is roughly zero, and for cars, lifetime emissions actually end up 11 percent higher.

But Gregory Keoleian at the University of Michigan told New Scientist that scrapping a one-year-old car is an “extreme case.” In reality, those cars would be resold rather than scrapped, which could lead to cheaper second-hand vehicles that pull people off lower-emission options like buses and trains and get them back behind the wheel.

Campbell admitted to New Scientist that more research is needed to model those kinds of scenarios. But it also backs up the authors’ call for more generous subsidies for scrapping gas vehicles, so that it becomes financially viable to replace relatively new gas cars without just redirecting them to the used-car market.

Until that happens, even the most eco-conscious among us are unlikely to scrap a brand new vehicle. Still, the study weakens the argument for holding on to an aging gas car.

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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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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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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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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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Synthetic biologists have long dreamed of constructing artificial cells from the bottom up. Researchers have now taken a major step in this direction by demonstrating that non-living components can be assembled into a system that grows, copies its DNA, and divides.

The genomic revolution transformed our ability to understand and manipulate cellular machinery, allowing scientists to rewire cells’ genetic circuitry to fight disease, produce valuable chemicals, and make crops more resilient. The holy grail for the field, however, has been to use these tools to create entirely synthetic cells—a milestone that would signal humanity’s mastery of life’s key ingredients.

How best to do this has long been an open question. Genomics pioneer Craig Venter made significant progress by stripping living bacteria back to their bare essentials, culminating in the 2016 unveiling of a minimal cell with just 473 genes. The Synthetic Yeast Genome Project has taken the opposite approach, building artificial versions of all 16 yeast chromosomes from scratch, though they’ve yet to get them working together in a single cell.

Now, researchers from the University of Minnesota, have assembled a synthetic cell out of engineered, non-living components housed inside an artificial, cell-like membrane. Their creation was capable of the four hallmarks of a living entity—the ability to feed, grow, copy genetic material, and produce offspring.

“We’ve replicated in chemistry what only used to be possible in biology: the complete set of behaviors of a cell,” Kate Adamala, who led the project, said in a press release. “It proves that the most fundamental functions of life, like growth and replication, do not need a mysterious magical spark.”

The researchers outline the design for their synthetic organism—nicknamed SpudCell for its potato-like shape under the microscope—in a non-peer reviewed paper uploaded to bioRxiv. SpudCell features a genome 90,000 base pairs long, which is considerably smaller than the 113,000 base pairs researchers had previously predicted would be the bare minimum needed to support a viable cell.

Rather than housing all the genes in a single chromosome, the team split them across several small, circular DNA molecules called plasmids, each specialized to fulfill specific functions. The researchers say this makes it possible to modify different aspects of the organism more easily.

To read the genome and build proteins, SpudCell uses a pre-defined kit of 36 purified enzymes drawn largely from E. coli. The whole assembly sits inside a liposome, a hollow bubble of the same fatty molecules that form natural cell membranes.

The artificial cell feeds in two distinct ways. Small molecules pass directly into the cell through protein pores implanted across the membrane. Molecules too large to squeeze through—like ribosomes and enzymes—are packaged inside tiny lipid bubbles that fuse with the membrane and empty their contents inside.

While the cell can feed, it’s entirely reliant on the researchers providing it with specially prepared meals. This means it’s a long way from surviving in the wild, which is both a major limitation and a key safety mechanism. “It’s a bed-ridden Frankenstein’s monster that has to be spoon-fed,” Adamala told New Scientist. “There’s no danger of it running amok.”

After ingesting “food,” SpudCell’s genes use the material to churn out proteins, while folding the incoming lipids into its membrane. This causes the whole cell structure to swell. Within a few hours, it’s bulked up enough to reproduce by dividing into two smaller cells.

Replicating cell division has been a longstanding challenge in the field. Natural cells split using an intricate protein scaffold called a cytoskeleton that’s fiendishly difficult to recreate. Adamala’s team sidestepped this problem by using a completely different mechanism, in which proteins bunch up on the membrane’s surface, putting it under mechanical strain. Eventually this squeezes two parts of the membrane together to pinch off a new cell.

The cells even manage a crude form of evolution. When the researchers introduced a genetic tweak boosting the cells’ ability to feed, those with the variant outcompeted the original lineage within five generations, and their edge widened when the researchers exposed the population to nutrient scarcity.

However, no one is claiming SpudCell is alive. Crucially, the cells cannot make their own ribosomes—the machines that build proteins from genetic instructions—and the ribosomes provided by the researchers degrade over time, limiting the cells to five to ten divisions.

The University of Chicago’s Jack Szostak told Quanta the work is an “impressive step” but the inability to produce ribosomes seriously limits potential for sustained growth. “If their system was able to generate its own ribosomes and other proteins and RNAs, it would be much closer to existing biological cells such as bacteria,” he said.

Nonetheless, the researchers think these artificial cells are a promising way to manufacture drugs, fuels, and materials without the toxic, energy-hungry industrial chemistry we rely on today. And they’ve created a new nonprofit called Biotic to share the tools they’ve developed with researchers.

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Scientists have long dreamed of developing nanoscale machines, but building reliable mechanical components at the molecular scale has proved challenging. Researchers have now developed a DNA-based switch that can rapidly and repeatedly snap between two stable states, much like the components that underpin everyday electronics.

Ever since Richard Feynman’s visionary lecture “There’s Plenty of Room at the Bottom,” researchers have been enamored with the idea of engineering at the scale of atoms and molecules. But manipulating matter at the nanoscale is easier said than done.

Individual molecules are in constant motion and continuously jostled about by the thermal energy of their surroundings. This makes it extremely difficult to position and assemble larger structures and undermines control of the mechanical motion of components.

This is particularly true for switches—key components in many mechanical and electronic devices you might want to build. Getting a tiny structure to hold one position, flip cleanly to another, and then stay there has so far been an unsolved problem.

But now, a team at the Technical University of Munich has created a switch made from folded strands of DNA that remains stable for up to an hour and flips in milliseconds on the application of a brief electric field. Crucially, the device was able to switch back and forth repeatedly with no degradation in performance.

“Individual devices sustain hundreds of thousands of switching cycles over several hours and remain functional for actuation over several days,” the researchers write in a paper in Science Robotics. “As a nanoscale electromechanical interface, our device enables applications in molecular information processing, optical nanodevices, and the dynamic control of chemical reactions.”

The device borrows a principle from standard engineering known as a snap-through mechanism, which rests in either of two states and only flips when pushed hard enough, a bit like a light switch.

Scaling the idea down to a few tens of nanometers meant designing rigid arms linked by flexible molecular hinges, so the structure settles into one of two configurations and does not flick between them on its own. The team relied on DNA origami to accomplish this, where a long strand of DNA is folded into custom 2D and 3D shapes using hundreds of shorter “staple” strands.

One of the two arms features a longer “extension arm” that acts as a lever to push the switch between configurations. DNA carries negative charge, so when an electric field is applied to the device, it pushes the arm hard enough to flip the switch. Left alone, the team estimates that the structure stays in its resting state for roughly six hours, and they observed no spontaneous flips while monitoring 70 switches for an hour.

One of the device’s main strengths is its endurance. One switch survived more than 200,000 flips over five and a half hours, and a simplified version withstood a million switching cycles in three hours while still working about 85 percent of the time. Performance varied considerably from one device to the next, however, with some failing after a few thousand cycles and others continuing for days.

The researchers say failures likely stem from a combination of contaminants, surface wear, and chemical changes in the surrounding fluid. However, some inactive switches later started working again, which the team says suggests they are capable of self-repairing.

To test whether the switch could do anything useful, the researchers attached a gold nanorod to the moving arm, turning it into a microscopic light switch that changed how light scattered off the particle. In a second test, they used the switch to expose or hide a molecular binding site, allowing it to control whether DNA strands could attach.

That second capability could be particularly useful as it could make it possible to control chemical reactions—for instance by turning enzymes on and off. The authors suggest that this could be used to create “control knobs” for chip-based bio-factories that run sequences of reactions.

Considerable obstacles remain before the device can become genuinely useful. A single switch encodes just one bit of information, and the team acknowledges that wiring arrays of switches together to create something resembling a circuit remains a distant prospect.

But a workable switch is a fundamental component that can be used to create all manner of devices. While we’re still a long way from Feynman’s dream of molecular machines, this is a meaningful step in that direction.

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AI’s hacking skills are big news at the moment, but finding vulnerabilities in code may be the least of our worries. A new study suggests AI models can discover potentially damaging loopholes in the rules and regulations underpinning society.

Modern AI systems are powerful optimizers. Give them a goal, and they’ll pursue it relentlessly, quickly discovering solutions that would take a human years to find. But they are also incredibly literal in the way they approach a problem. They will do exactly what you tell them and are incapable of reading between the lines in the ways a human would.

This tendency leads to a recurring problem known as “reward hacking,” where an AI finds some loophole to maximize its performance on the metric used to measure success without actually achieving what its designers intended. The classic example is the AI that discovered it could win a boat racing videogame by looping around in circles collecting power-ups rather than completing the course.

The problem is partly due to humans being bad at specifying their goals. And unfortunately, it seems this weakness exists in the rules and regulations used to run society. When researchers let popular large language models loose in 72 simulated regulatory environments, the models found 60 percent of known loopholes and even identified some entirely new exploits.

“Within these environments, reward hacking naturally emerges and leads to regulatory loophole discovery,” the authors write in a non-peer-reviewed paper published on arXiv. “Models learn to hack the social rules and generate strategies that remain technically compliant while defeating regulatory intent.”

The regulatory environments the researchers created were primarily based on rules governing things like pharmaceutical patents, NBA salary caps, and deep-sea mining. In each case, Alibaba’s Qwen3 model was given the relevant rules, an explanation of its task, a predefined set of actions it could take, and the system used to score different outcomes.

A more powerful model, Google’s Gemini-3-flash, then simulated the consequences of different actions Qwen3 took and judged if and when it had found a way to exploit the rules of the game. When that occurred, the larger model patched the loophole by adding new rules, and the smaller model was set loose again. Over many iterations, the models to discover increasingly subtle workarounds.

When building their regulatory environments, the researchers omitted real-world fixes that regulators had used to close known loopholes. Over many trials, Qwen3 rediscovered more than 60 percent of these exploits. In a simulation of pharmaceutical patent regulations, the two models ended up replaying the same sequence of loophole discovery and regulatory reform that occurred in the real world.

Crucially, their behavior emerged spontaneously without the researchers asking the algorithms to cheat the system. This is a byproduct of the popular reinforcement learning approach the researchers used, where a model is rewarded for getting closer to a specific, numerically-defined goal.

Worryingly, the team found that existing safety measures offered little protection. Both models are designed to refuse prompts featuring harmful language, but loophole-seeking behavior slipped under the radar. When asked to self-critique their own behavior, the models identified fewer than 40 percent of their own exploits.

The researchers note that the same capabilities could be used more proactively to scour proposed regulations for loopholes before enactment. But lead author Wei Liu, a PhD student at King’s College London, says there are always likely to be gaps. “In the real world,” he told Science, “society is a huge, complicated reward function that can’t ever be patched to a perfect status.”

Adding to the concern, the models used in this study were far from the frontier, suggesting that more powerful AI could be even more adept at regulatory hacking. Whether our existing institutions can adapt quickly enough to this emerging threat is an open question.

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