2026-09-01 05:33:36
Delivered by injection, the drug transforms astrocytes into neurons. In an early study, mice modeling Alzheimer’s showed marked improvement compared to untreated peers.
The Alzheimer’s brain faces a double whammy. Toxic protein clumps build up inside and outside neurons to torpedo normal function and destroy delicate structures. Eventually, the cells die. The adult brain has an extremely limited ability to grow new neurons. Once gone, they’re rarely replaced. Over time, the brain withers, taking learning, memory, and cognition with it.
But there might be a sneaky workaround. The brain is packed with star-shaped cells called astrocytes that keep neurons healthy. They’re also shape-shifters. Under certain conditions, astrocytes can shed their identity and transform directly into mature neurons. In other words, they could be an abundant, untapped source of replacement neurons.
A team at the University of South Carolina has now taken advantage of this quirk. They engineered a tiny molecular cage and filled it with antibodies. Once inside astrocytes, the antibodies released a protein “brake” that normally keeps the cells’ identity stable. Free from this constraint, astrocytes in lab dishes and human brain organoids adopted the molecular signatures of neurons and eventually sparked with electrical activity.
In mice modeling Alzheimer’s disease, the treatment increased the number of neurons in the hippocampus, a brain region crucial for learning and memory and one of the first to falter in the disease. Treated mice resumed normal behavior and performed similarly to healthy mice on tests of learning and memory in a maze.
The approach fundamentally differs from existing methods and could “unlock previously inaccessible regenerative mechanisms,” wrote the team. If it proves safe and effective in clinical trials—and that’s a big if—the approach could one day tackle diseases beyond Alzheimer’s, such as Parkinson’s or amyotrophic lateral sclerosis (ALS).
The quest to treat Alzheimer’s has often been called the “graveyard of dreams.” The most common form of dementia, the disease affects roughly 24 million people worldwide and slowly eats away at thinking, memory, learning, and emotional regulation. Experts still debate Alzheimer’s root cause, but they largely agree that clumps of misshapen proteins called amyloid beta and tau exacerbate the disease.
Current FDA-approved treatments have had limited success. Antibodies that clear clumps offer only modest benefits to cognition and carry the risk of serious side effects. Other drugs, such as memantine, alter brain chemicals to protect damaged cells, rev up faltering brain circuits, and ease symptoms. But they don’t halt degeneration. As the disease progresses, benefits fade.
The central problem is frustratingly clear. Neurons die faster in Alzheimer’s than the brain can replace them. That’s why a landmark study nearly two decades ago made waves. Scientists once thought mature astrocytes were set in their fate. But the study showed the cells could be reprogrammed into neurons that generated electrical activity and formed connections with neighboring neurons in lab dishes to form working circuits.
Scientists later found a protein called PTBP1 that prevented this conversion. In 2020, a team injected an RNA-targeting form of CRISPR into the brains of mice modeling Parkinson’s disease. This reduced PTBP1 levels, which in turn, triggered the production of new neurons. The treatment restored the mice’s balance and motor skills, although some experts were skeptical.
While promising, CRISPR-based approaches can have unintended effects, and brain surgery is a tall order for any treatment. So, the team developed another way to release the PTBP1 brake.
They turned to a duo of technologies that transport antibodies inside nanoparticle cages to degrade specific proteins inside cells. In this case, they used antibodies targeting PTBP1 and packaged the concoction in a biocompatible gel injected into the bloodstream.
Because of their large size, antibodies can’t usually cross the blood-brain barrier, a tightly sealed wall that keeps many molecules out of the brain. But the nanoparticle system helped ferry the antibodies across the blockade, nixing the need for brain surgery.
The team first tested the drug, called TN-PTBP1, on astrocytes grown in lab dishes. Within days, the cells lost their star shapes and began growing long, willowy branches characteristic of neurons. Their molecular profile also shifted, and the cells eventually burst with electrical signals.
The team recorded similar results in brain organoids, or “mini brains,” grown from human stem cells. Given a small electrical zap, the converted neurons responded in synchrony with neighboring neurons, suggesting they had integrated into existing neural circuits.
“The new neurons can become mature and survive,” said study author Peisheng Xu in a press release.
Next, they tested the drug in a mouse model of Alzheimer’s disease. By eight months, the mice showed clear signs of the disease. Their brains were highly inflamed and littered with toxic protein clumps. Neurons in the hippocampus had also substantially died off, similar to the loss seen in moderate to severe Alzheimer’s in humans.
The mice struggled with everyday behaviors, such as foraging for material to build nests. And they consistently performed poorly on a classic memory test where they had to find a location using visual cues (a bit like remembering where you parked your car).
Half the mice received TN-PTBP1 for two weeks; the others received saline. As expected, the drug reliably slashed PTBP1 levels in the brain. Over the course of the trial, treated mice increasingly improved on tests of cognition and memory, eventually performing at levels similar to healthy peers. Mice treated with saline showed no improvement.
“After just two injections, these mice became smarter,” said Xu. “Even after one injection, we already saw these mice’s behavior differ from that of the nontreated ones.”
The team found broader benefits too. The drug reduced inflammation and, surprisingly, the number of toxic protein clumps, suggesting it may have helped restore some of the brain’s ability to rid itself of waste. Neuron density also increased throughout the brain, and the treatment boosted production of proteins involved in maintaining the blood-brain barrier, which is often damaged in Alzheimer’s.
One unexpected, and welcome, effect was neurogenesis, the birth of new neurons in the hippocampus and another brain region. Neurogenesis declines with age, and whether it exists at all in adult humans is hotly debated. How TN-PTBP1 triggered it in mice remains a mystery. It’s also unknown how much the new neurons contributed to the animals’ recovery versus the direct conversion of astrocytes into neurons.
Still, it’s clear the drug boosted neuron numbers and “successfully reversed Alzheimer’s disease progression” in the mice, wrote the team.
The approach has a long road ahead. Many promising treatments in mice have failed in clinical trials. In the next few years, the team hopes to test the approach in monkeys, dial in the dose, and assess long-term safety. Astrocytes perform many tasks that keep the brain humming, and forcing them to abandon their identity could have unexpected consequences. There’s also the possibility newly converted neurons could scramble existing brain circuits rather than integrating safely, causing more harm than good.
But with rigorous testing, the drug could offer new hope.
The post This Drug Makes New Neurons in the Brain. Scientists Say It Reversed Alzheimer’s Symptoms in Mice. appeared first on SingularityHub.
2026-08-29 22:00:00
OpenAI Is Developing a ‘Persistent’ AI AgentMaxwell Zeff | Wired ($)
“In recently aired podcasts, interviews, and private investor meetings, OpenAI CEO Sam Altman has described his desire to turn ChatGPT into a proactive, always-on AI agent. …’There’s like a single product which is: I need to ask the AI something,’ Altman said on a recent episode of David Senra’s podcast. ‘Eventually, maybe the AI should proactively offer me things.'”
I Saw the Future of AI in a Robot That Can Learn on the SpotWill Knight | Wired ($)
“I visited the Cambridge, Massachusetts, offices of a startup called Generalist AI, where I watched robot arms perform simple chores like stacking cups, putting blocks into bowls, and the like. I was astonished by how quickly they figured things out—it was reminiscent of a flesh-and-blood person. The arms mastered a range of tasks after ingesting a short, instructional video and, most impressively, no specific training for a given task.”
Fully Autonomous Russian Drone Kills Three UkrainiansBrendan Ruberry | Semafor
“Though AI has often been used in the final stages of human-planned strikes, the reported incident crosses a dangerous threshold, analysts said, leaving machines to interpret the laws of war and to determine what constitutes a legitimate target. ‘This is a risk for the whole world,’ a Ukrainian commander said. ‘In a few years, we will be living in a Terminator movie.'”
Researchers Get Two Genetic Codes to Work at the Same TimeJohn Timmer | Ars Technica
“Now, researchers have found a way to operate two separate genetic codes simultaneously, avoiding the need to do any work to compensate for altering the code that every protein in a cell relies on. They didn’t test it in an actual cell, and it might cause some problems there. But it’s a creative solution that should accelerate some synthetic biology work.”
An Experimental Single-Time Treatment Slashed Cholesterol for a YearCarolyn Y. Johnson | The New York Times ($)
“The results highlight the potential to treat even common diseases by altering people’s genes. In the small study, which followed only 15 patients, participants who got the highest dose saw their cholesterol levels plunge by half—and stay that way for a year. “
The Floodgates Are Open After Another Chinese Company Lands a Reusable RocketStephen Clark | Ars Technica
“It has been a little more than a month since China recovered an orbital-class rocket booster for the first time. A second launch operator accomplished a similar feat Tuesday in another sign of China’s growing launch capability. It took 10 years for a second US launch company, Blue Origin, to propulsively land an orbital-class booster after SpaceX did it with the Falcon 9 rocket in 2015.”
A Startup Claims It’s Found a Drug to Make Your Blood YoungAntonio Regalado | MIT Technology Review ($)
“The quest has been to find practical ways to mimic [the benefits of replacing old blood with young blood observed in lab mice]. And that is something [Irina] Conboy says she’s now achieved by hitting on a combination of two existing drugs that produce youthful effects—but without the need for any bodily fluid exchange.”
What We Still Don’t Know About OpenAI’s Hugging Face HackMaxwell Zeff | Wired ($)
“The public postmortem leaves some basic details unresolved…[which] makes it harder to know how much of what happened reflects the growing capabilities of AI agents and how much was specific to the way OpenAI designed and monitored its own systems.”
SpaceX Plans to Build the World’s Biggest SpaceportEditorial Staff | The Economist ($)
“Mr. Musk’s ambition is for the site to host more than 30 rocket launches a day, to support both Starlink—the firm’s existing broadband-from-space service—and its plans to fly data centers into orbit, where they would benefit from both free solar power and an absence of NIMBYs. …If Mr. Musk hits his 30-launches-a-day target, his Louisiana purchase would allow SpaceX to fly about 2m tons of payload into orbit every year, up from about 3,800 tonnes in 2025.”
Walmart Is 3D Printing the Future of Big Box StoresPatrick Sisson | Fast Company
“For the last two years, contractors working for Walmart have used 3D-printed construction at a handful of sites across the US. …It’s the country’s largest deployment of 3D-printed architecture in the commercial sector. The retailer’s scale is catalyzing the growth and adoption of the technology, which promises to construct buildings faster and more affordably.”
Robotaxis Are Real Now—So Is the PushbackRani Molla | The Verge
“The consequences could look very different as autonomous fleets grow from thousands of vehicles to hundreds of thousands. That’s why the battles taking shape across the country are increasingly about the terms of expansion: what companies have to prove before they grow, what they owe cities and workers, and how much control cities and states should have over their operations.”
Kids Outlearn AI—and We Still Don’t Know WhyElise Cutts | MIT Technology Review ($)
“‘The progress recently has been amazing,’ Michael C. Frank, a cognitive scientist at Stanford University, says of LLMs. ‘But we still have to burn down a forest and scrape the entire sum of all human knowledge to re-create this milestone that happens in our living rooms over the course of a year.’ [This yawning divide] raises a tantalizing question for cognitive scientists and a challenge for the architects of AI models: How is it that kids can still outperform the most linguistically sophisticated machines ever built?”
The post This Week’s Awesome Tech Stories From Around the Web (Through August 29) appeared first on SingularityHub.
2026-08-29 00:45:11
Recursive self-improvement, where AI continuously builds better versions of itself, might be harder than some hope.
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.
The post Are We on the Verge of an Intelligence Explosion? Maybe Not. appeared first on SingularityHub.
2026-08-28 05:13:16
Can AI replace lawyers—at least in some circumstances?
Last week, Australia’s Fair Work Commission ruled Gregory Baker, a computing academic at Macquarie University, should be treated as an ongoing, part-time employee, after the university had earlier declined his request to convert from a casual role.
It was immediately described as a “landmark” decision, the first test of Labor’s “employee choice pathway” reforms passed in 2024.
But the ruling also made headlines for other reasons. Baker represented himself at the tribunal and has said he won with the help of trained artificial intelligence agents. His success again has us asking: Can AI replace lawyers?
On closer scrutiny, Baker’s case looks less like evidence of AI replacing lawyers and more like a powerful illustration of how a highly capable user can employ AI tools to terrific effect.
Speaking to the Australian Financial Review following the ruling, Baker said it was actually an AI tool that alerted him to the possibility of converting his role from casual to permanent part-time in the first place.
He had been teaching computer science at Macquarie University over consecutive semesters from 2023 to 2025, and in November 2025, he gave the university the required notice that he believed his work no longer met the requirements of casual employment.
The university did not accept this notification and Baker lodged a dispute at the Fair Work Commission—without a lawyer—in December 2025. The parties could not reach agreement, and the case went to arbitration on May 12. A decision was handed down last Wednesday.
Baker has said he won by using multiple paid AI agents, such as OpenAI’s paid offering, ChatGPT Pro. This “team” helped assemble his case, follow up references, and anticipate his employer’s counterarguments.
His victory has been celebrated as historic, with the Australian Financial Review describing it as “the first known successful use of technology by a self-represented person in the legal arena.”
However, a few things set this particular case apart. Baker’s IT background, expertise managing AI agents, and ability to optimize their use for his case represent a rare level of expertise in using AI in a legal context.
Details included in the Fair Work Commission’s decision also suggest he kept his legal argument narrowly focused on teaching he’d done in one particular unit.
Less expert use of AI in court often sees those bringing claims produce “kitchen sink”-style arguments, which include weak, exaggerated, and nonsense claims.
Baker’s dispute was also narrow, limited to the application of a casual conversion law that had not yet been tested. Importantly, the Fair Work Commission (a tribunal, not a court) is designed to be user-friendly, to enable workers to bring claims without a lawyer.
Elsewhere, the use of generative AI in legal proceedings is attracting a lot of attention for less positive reasons.
Most of this attention centers on the damage caused by inaccuracies, hallucinations and “AI slop”, and how courts and tribunals should best respond.
By making it easier to put a case together, AI has removed traditional access barriers for some litigants. But while case numbers are going up, case precision and quality is going down, making it harder to manage disputes to resolution.
Courts and tribunals are struggling with the volume. At the Fair Work Commission alone, workload has reportedly increased by 70 percent over three years.
New challenges are emerging as time goes on. Reports suggest litigants and lawyers in some overseas jurisdictions are embedding prompts in digital documents (something called “prompt injection”) to overcome or manipulate AI-based review systems some courts use to process documents.
Baker’s example shows us something significant. Used well, AI tools can empower people with narrow legal disputes and digital skills to achieve successful resolutions at low cost.
This is an important development in access to justice. In Australia, there is a huge gap between the number of people with legal problems and the very limited funding available for legal assistance.
Most of the community is in the “missing middle,” unable to afford private legal assistance but on incomes too high to qualify for free legal aid.
AI tools stand a good chance of helping people with sufficient legal capability with problems and cases—like Gregory Baker’s—that are a good fit for the solutions AI can offer. These are few and far between, however.
We should expect case numbers and self-representation in courts and tribunals will continue to grow and expand beyond Fair Work.
While there will be some baseless cases, the growth also represents the natural consequence of removing one traditional access barrier to our formal justice institutions—getting in the front door to start proceedings.
The bigger picture challenges are persistent and raise important questions. Who will most benefit from the capacity of AI tools to enhance access to justice, and who will continue to struggle to get basic legal problems resolved?
For courts and tribunals, the challenge will be striking a balance between managing caseloads and delivering justice, while not wasting the opportunity to expand access to justice.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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2026-08-26 04:35:27
These lab-grown balls of brain tissue could help researchers study a host of disorders that emerge as the brain ages.
Five years is an eternity for brain organoids. Also called mini brains, these blobs of tissue have taken neuroscience by storm for their ability to capture the intricacies of developing brains.
Organoids begin life as a collection of stem cells. Within weeks, they spontaneously produce a range of brain cells. Neurons form circuits that spark with electrical activity. Gene expression resembles that of early fetal brains. Some organoids learn to control small, isolated muscles. Others link to spinal cord organoids and process pain signals.
Over time, they grow more sophisticated in both structure and function—eerily similar to near-term fetuses—prompting bioethicists to ask if they could one day become conscious.
But time isn’t on their side. Most mini brains survive only a few months before their sensitive neurons start to wither. Circuits break down, structures collapse, and eventually the organoids die. As a result, they can model only the early stages of human brain development, leaving what happens during the later months of pregnancy and after birth largely mysterious.
These periods are especially relevant to schizophrenia, epilepsy, severe autism, and a host of other disorders. Scientists have studied late-stage development using donated tissue, but samples are scarce and raise ethical concerns.
A team led by Harvard’s Paola Arlotta is now pushing the boundaries with organoids. Last week, they described a method that kept mini brains alive for over five years—the longest yet—and tracked their development throughout. Despite growing outside the body, the organoids matured on a timetable similar to normal brains. Genetic activity in the oldest ones resembled that of a typical 4-year-old.
The findings were originally reported in a preprint and have now been peer-reviewed and published in Nature.
The developmental lockstep surprised the team. Cells from older organoids, when mixed with younger ones, continued maturing on schedule, suggesting they carried an internal developmental clock that keeps track of their progress.
“The brain doesn’t develop in a vacuum. It’s an organ of incredible complexity that interacts with so many other systems,” study author Irene Faravelli said in a press release. “It was not a given at all that our simplified model would match natural development in this many ways.”
Because mini brains generate nearly the full range of human brain cells, they’re promising models for the study of early brain development. But early versions survived only a few weeks. Without blood supply, cells at their centers starved and died.
Through trial and error, researchers learned to coax them into increasingly sophisticated structures that included layers resembling the cortex and had integrated blood vessels. This vastly extended their lifespan.
In 2021, a study kept mini brains alive for up to two years, capturing cortical development from pregnancy to roughly a year after birth. Four years later, Arlotta’s team announced a way to extend organoid lives to a staggering seven years. Roughly the size of a pea, each nugget was packed with some two million healthy neurons and other brain cells.
Following these organoids for years offers an unprecedented window into how the brain grows and wires itself—and how genetic changes early on might contribute to diseases later in life.
Our brains take roughly two decades to mature. Throughout this period, neurons constantly rewire their connections. Scientists have long known that conditions such as schizophrenia and some forms of epilepsy first emerge during adolescence. Because mini brains can be grown from a person’s skin cells and retain genetic mutations associated with neurodevelopmental disorders, they offer a way to probe how, and when, neural wiring goes awry.
But timing matters. The question is, how faithfully does a growing blob in a dish follow the developmental journey of a human brain?
To answer that question, the team grew 34 organoids and tracked them at regular intervals. They collected data every three to six months for the first 18 months, then annually until the organoids were over five years old.
Crucial to the brain blobs’ longevity was switching the growth medium—a nutrient- and protein-rich slurry—halfway through development. The new recipe kept neurons alive longer, giving them time to support increasingly complex activity.
The team then tracked changes in gene activity and epigenetic markers (chemical tags that control which genes are turned on or off). They then compared the findings with data from younger organoids—ranging from 15 days to six months old—and donated human tissue.
The developmental timeline was surprisingly similar to that of a human brain. Young organoids showed gene activity resembling the first trimester; by three to six months, they looked more like second-trimester brains. After a year, their gene activity profiles resembled those of newborns. By the end of the experiment, they most closely matched a typical 4-year-old.
The team also tested them with epigenetic methods used to gauge biological age as opposed to calendar years. The organoids gained and shed epigenetic markers in patterns that broadly tracked those seen in natural brain development.
The organoids seemed to retain a “sense” of time. The team mixed cells from year-old organoids with those from 15-day-old organoids. Both followed their usual trajectory: The younger cells developed into early-stage neurons. But the older ones skipped those stages and rapidly produced more mature neurons often requiring months to grow.
“I like to think of this as a sort of ‘warping of developmental time’ indicating that the organoid cells record and recall the time they have already spent in culture,” said Arlotta.
In other words, the cells seem to carry an internal developmental clock, which could be especially useful for studying disorders with symptoms emerging long after the early stages of development.
To be clear, though, a mini brain resembling a 4-year-old’s brain at the molecular level doesn’t mean it has the same wiring or computational capabilities. Gene activity only captures part of a brain’s development; real brains are shaped by experiences and interactions with the rest of the body. Without input, mini brains can only offer a molecular blueprint of brain development, not its entire rich tapestry.
Still, long-living organoids are a breakthrough. Researchers could freeze cells from organoids at different developmental stages and later thaw them for experiments. This could speed up discoveries because scientists wouldn’t have to grow new organoids from scratch for each new study. Think of it as a save point in video games.
The team plans to grow long-lived organoids from people with schizophrenia or epilepsy and use them to study disease progression and screen drugs. Keeping ethics in mind, they’re also considering exposing mini brains to sensory stimuli such as sight, sound, or touch.
“There is still much to learn about how the embryo naturally builds a progressively more complex and mature brain,” Arlotta said. “Applying these lessons to organoids will allow us to model unexplored events of human brain maturation that occur after birth.”
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2026-08-25 06:52:36
The flashy company, which recently completed a blockbuster IPO, appears to be leading the pack of humanoid robot makers.
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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