Innovation abounds in device charging

The changes may be less perceptible than in smartphones, tablets, or wearables, but chargers have also been quietly reinvented over the last decade. At one time a bulky mix of tangled cables and connectors, slow to perform and prone to overheating, they’re now smaller, safer, and faster, thanks to a slew of technological advances.

These advances include a switch to gallium nitride (GaN), which has now usurped silicon as the preferred semiconductor, capable of handling higher voltages, faster switches, and more efficient conduction. Multi-port chargers, coupled with an industry-wide shift toward USB-C standardization, mean a single charger can handle multiple devices. And early smart chargers are also trickling onto the market, able to dynamically distribute power and carry out autonomous safety checks.

Combined, these have repositioned chargers as differentiated standalone devices, rather than peripheral accessories.

But, manufacturers say there is much further to go if chargers are to accommodate the demands of a connected ecosystem now made up of an estimated 20 billion devices, according to IoT Analytics.

“Charging products are undergoing a fundamental identity shift—from accessory to primary component,” says Mario Wu, general manager for North America at Anker Innovations. “This is not simply a functional upgrade; It is a repositioning of charging’s role within the broader digital lifestyle ecosystem. As charging becomes normalized, the charger is no longer an appendage to your devices—it is the infrastructure underlying every digital experience.”

Pillars of performance

If this vision for the future of charging sounds ambitious, there are concrete advancements to back it up. Newly refined semiconductors are already bolstering power and performance, building on the gains delivered by GaN with some sweeping changes to systems architecture.

To take advantage of the fast-moving technology, Anker launched GaNPrime 2.0, which combines GaN materials with higher-frequency controllers and other power devices, achieving higher power output and lower heat generation, explains Wu. For example, the addition of a multi-level buck converter converts voltage from a binary on/off pattern, to multiple, smaller steps that create smoother transitions and reduce stress on components. Combined with Anker’s proprietary control algorithm, this simultaneously achieves a more compact product design and reduced energy loss.

Changes such as this mean secondary-stage power conversion now reaches over 99.5%, says Wu, and some products can maintain 140 watts on a single port without falling below optimal levels. “In traditional setups, you might use three separate chargers—adding up to roughly 210 watts combined,” says Wu. “But Anker’s Prime 160W Charger with PowerIQ 5.0 can charge those same three devices in roughly the same time because it dynamically reallocates unused capacity instead of locking it in place.”

But if GaNPrime 2.0 represents where the architecture stands today, it’s by no means the end point. Says Wu, “The next phase of GaN development focuses on higher frequency switching: When paired with breakthroughs in materials and control technology, higher switching frequency enables lower energy loss, improved conversion efficiency, and even more compact designs.”

Other third-generation semiconductors like silicon carbide (SiC) will also have a role to play. Already deployed at scale in EV inverters and industrial power systems, Wu explains that SiC can deliver “exceptional, high-temperature stability and reliable support for high-voltage, high-power applications.” Improving circuit design using SiC to make it compact and cost-effective for smaller devices has proven a stumbling block until now, but Wu is hopeful that as manufacturing scales up, the material will become “an increasingly credible direction.”

Without constraints

Consumers also demand portability in their device charger. They want chargers without the spatial constraints of wires or surface-to-surface connection—or what’s known as imperceptible charging.

Wireless charging innovations today go part of the way, but they’re based on the principle of magnetic coupling—i.e., only when transmitter and receiver coils are aligned is energy transfer efficient and stable. That means devices must be in contact with the charging pad surface.

But research into technologies that use magnetic resonance and infrared are moving the dial. Best known for creating non-invasive imaging in health care via MRIs, magnetic resonance uses magnetic fields to allow energy transfer over greater distances by tuning transmitter and receiver coils to the same resonant frequency. Transmitters emit an oscillating magnetic field from which the receiver can extract energy even if coils are not perfectly aligned. This “significantly relaxes placement requirements for users, [but currently] the trade-off is reduced transmission efficiency,” says Wu.

Infrared wireless charging also represents a meaningful area ripe for exploration, Wu adds. This sees infrared beams deliver energy to photovoltaic receivers on devices, with transmitters installable at any location so long as there is clear line-of-sight to the device. This enables wireless power delivery across meters rather than centimetres. He explains, “The core challenge it currently faces is further increasing power levels, and related research is ongoing.”

Wu says Anker is engaged in technical exchanges with both universities and industry associations to find workarounds for these trade-offs. “Our strategy is to remain at the forefront: continuously tracking, conducting in-depth evaluations, and delivering the next generation of wireless charging technology to users the moment it matures and becomes viable.”

Levelling up intelligence

If the power, performance, and portability of chargers have made incremental gains in the last decade, though, then imbuing devices with smart capabilities is arguably more of a step change in what users might expect.

Wu defines smart charging as “the shift from passive power delivery to active, adaptive energy management.” In short, if conventional chargers supply fixed current, then smart chargers can read device signals, monitor conditions, and adjust their output accordingly to optimize speed, safety, and efficiency.

Some products on the market already hint at these possibilities.

Next-gen chargers already deliver dynamic power allocation, for example, recognizing individual device IDs to adapt the distribution of power to multiple devices simultaneously. But in 10 years’ time, the goal is to create chargers that go much further, says Wu, capable of autonomously managing energy across multiple connected devices, communicating with users, and adaptively optimizing performance.

“Smart charging will feel less like a feature and more like an invisible service—one where the system knows your devices better than you do: anticipating needs, intervening before battery degradation sets in, and managing the full energy picture across everything you own,” he summarizes.

These future charging systems will understand each device’s specific needs and deliver the right charge, at the right moment, balancing longevity with performance, without the current trade-offs. A single device will serve an entire household, Wu believes, working imperceptibly in the background to balance multiple devices without spatial restraints. And they’ll proactively engage with users, too, providing feedback and updates via personable interfaces.

That may sound highly conceptual, but it’s a far closer technological reality than you’d think, Wu insists. “The transition [to smart charging] is actively underway” and chargers will soon join the ranks of devices deemed indispensable for day-to-day life, albeit as understated as ever.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

The Download: the hantavirus outbreak and Musk v. Altman week 2

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Here’s what you need to know about the cruise ship hantavirus outbreak

Last week, eight passengers aboard a Dutch-flagged cruise ship contracted a type of hantavirus transmitted by rats. Three have since died. But health experts stress that this situation is nothing like the coronavirus outbreak in 2020.

The Andes virus is known to spread between people, and there are no specific antiviral treatments or vaccines. Yet transmission appears to require a specific form of contact that the cruise ship fostered.

Here’s what you need to know about the outbreak—and why experts believe it can be contained.

—Jessica Hamzelou

This story is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here

Musk v. Altman week 2: OpenAI fires back, and Shivon Zilis reveals that Musk tried to poach Sam Altman

In the second week of the landmark trial between Elon Musk and OpenAI, Musk’s motivations for bringing the suit came under intense scrutiny.

OpenAI president Greg Brockman testified that Musk had pushed for the company to create a for-profit entity, while Shivon Zilis, a former board member, revealed that the Tesla tycoon had sought to lure Sam Altman to a new AI venture.

The courtroom also heard about Brockman’s private journals, Musk’s abandoned plans for a rival AI lab, and the moment he stormed out of a pivotal meeting carrying a painting of a Tesla.

Here’s what happened in the second week of the trial—and what’s coming next

—Michelle Kim

Michelle Kim, who’s also a lawyer, has been in court on each day of the Musk v. Altman trial. To keep up with her ongoing coverage of their legal showdown, follow @techreview or @michelletomkim on X. 

How LLMs could supercharge mass surveillance in the US: 10 Things That Matter in AI Right Now

There are pieces of your life scattered all over the internet, and some of them are for sale. Data brokers collect web searches, financial records, and location data from millions of people and sell them to various clients, including the US government.

While gathering that data has become easier in the smartphone era, making use of it at scale has remained difficult. But researchers are beginning to show that LLM agents can connect anonymized data to real people quickly, cheaply, and at a massive scale.

Find out why privacy experts fear AI could remove the friction that has long protected the public from mass surveillance.

—Grace Huckin

“How LLMs could supercharge mass surveillance in the US” is a feature accompanying MIT Technology Review’s 10 Things That Matter in AI Right Now, our guide to what’s really worth your attention in the busy, buzzy world of AI. Check out the full list of the big ideas, trends, and advances in the field here.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Meta’s embrace of AI is making employees miserable
Workers feel pressured to use the tech while fearing AI-driven layoffs. (NYT $)
+ They’re also unhappy about Meta tracking them to train AI. (The Verge)
+ AI’s rise has been described as “the most joyless tech revolution ever.” (WSJ $)
+ Gen-Z is particularly fed up with it. (NYT $)
+ We’ve entered the era of AI malaise. (MIT Technology Review)
 
2 South Korea’s military wants robots to fill gaps in troop numbers
It’s in talks with Hyundai to bring robotics to the front lines. (Bloomberg $)
+ They could include Boston Dynamics’ Spot and a new exoskeleton. (SCMP)
+ South Korea’s military has shrunk by 20% over six years. (BBC)
 
3 OpenAI is being sued over ChatGPT’s alleged role in guiding a mass shooter
A lawsuit claims the bot said targeting children would bring more attention. (NBC)
+ Florida’s AG has opened a criminal investigation into the case. (NPR)
+ Does AI cause or amplify delusions? (MIT Technology Review)

4 The Canvas hack was the biggest-ever student data privacy disaster
It exposes the risks of centralizing the data of millions of students. (404 Media)
+ While the platform is back online, the hack disrupted university exams. (NPR)
+ The breach is part of a trend of edtech vulnerabilities. (WP $)
 
5 Alibaba has joined China’s “chat to buy” shopping craze
By integrating AI assistant Qwen into its e-commerce platforms. (Reuters $)
+ Companies are betting that chat is the future of online shopping. (SCMP)
+ OpenClaw is a driving force behind the trend. (MIT Technology Review)
 
6 Cybercrime increasingly comes with threats of physical violence
In the US, the physical threats rose more than twofold last year. (BBC)
 
7 AI’s next phase plays into TSMC’s hands
Taiwan’s chip-making giant stands to gain from the supply squeeze. (WSJ $)

8 Europe is confronting life without American tech
Dependence on Silicon Valley is a growing geopolitical concern. (FT $)
 
9 The US, UK, and China top new rankings for AI in life sciences
Switzerland and Germany follow in the AI Competitiveness Index. (SCMP)

10 The Pentagon has released a massive trove of declassified UFO files
Including newly declassified documents, images and footage. (New Scientist)
+ The files contain reports of “orbs,” “saucers,” and lunar “flashes.”  (Wired $)
+ Here’s how to spot an alien. (MIT Technology Review)

Quote of the day

“There’s a real sense where ‘safety’ isn’t a bad word anymore.”

—Nathan Calvin, general counsel at Encode, a nonprofit AI advocacy group, tells the Washington Post that Anthropic’s Mythos has forced a White House reset on AI safety.

One More Thing

detail from an image of Mars' surface
This computer-generated image of Mars was built with laser altimeter data from NASA’s Mars Global Surveyor, which operated for nine years in orbit around the planet.
NASA/JPL-CALTECH


Inside NASA’s bid to make spacecraft as small as possible

As NASA’s InSight lander descended to Mars in November 2018, two tiny spacecraft tracked its progress. InSight had touched down, they reported, and survived its treacherous journey.

The mission offered a pathway to cheaper space exploration, with small, low-cost probes launching far more often than multibillion-dollar flagship missions. But there’s a catch: miniaturization can only go so far before it collides with the hard limits of physics.

NASA still hopes small sats could transform planetary exploration. But first, scientists and engineers have to figure out what these tiny spacecraft can realistically do.

Discover how small spacecraft could pave the way for giant leaps into the cosmos.

—David W. Brown

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ In a grand tribute to a four-legged hero, Cambodia has erected a statue to honor a rat.
+ A 1957 comedy accidentally made a seriously prescient prediction about office automation.
+ Explore the physics of the train that wouldn’t fall over—a monorail that promised to revolutionize travel.
+ Are you a true cinephile? Prove it with these daily movie quote challenges.

Haemodynamic correlates of bilateral 6 Hz transcranial alternating current stimulation during working memory enhancement revealed by fNIRS

IntroductionTheta-band transcranial alternating current stimulation (tACS) has been proposed to enhance working memory (WM) by entraining endogenous oscillations, yet the haemodynamic signatures accompanying theta-tACS–related WM gains remain unclear. We investigated whether 6 Hz tACS modulates prefrontal task-evoked haemodynamic responses during WM and whether such changes relate to behavioral improvement.MethodsIn a randomized, single-blind, sham-controlled design, healthy adults (18–30 years) were allocated to 6 Hz tACS or sham (initially n = 25 per group). Stimulation (1mA peak-to-peak, 20min, 30-sramp-up/down) was delivered bilaterally over the dorsolateral prefrontal cortex (DLPFC; F3/F4). Participants completed the Digit Span Test and N-back tasks (1-, 2-, 3-back) before and after stimulation. During N-back performance, fNIRS recorded bilateral DLPFC signals. Task-evoked oxygenated hemoglobin (HbO) β coefficients were estimated using a general linear model. Group differences in pre–post changes were tested using within-/between-group analyses and ANCOVA, with brain–behavior coupling assessed via Spearman correlation.ResultsAfter fNIRS quality control, 44 participants were included in the final analysis; six participants were excluded because more than 30% of channels were identified as low quality. Relative to sham, 6 Hz tACS produced greater improvements in backward Digit Span and total Digit Span. In the N-back task, accuracy improved selectively under the high-load 3-back condition (between-group change: [F(1.40) = 12.29, p_adj = 0.0034, η2p = 0.24)], whereas reaction time showed no significant between-group differences. fNIRS revealed post-stimulation increases in left DLPFC HbO-β during 3-back, with a significant between-group β-change at one channel [(F(1.40) = 10.69, p_adj = 0.035)]. Within the 6 Hz group, 3-back accuracy gains correlated positively with β-change in the left DLPFC (ρ = 0.44, p_adj = 0.039).ConclusionBilateral 6 Hz tACS selectively enhances high-load WM accuracy and is accompanied by increased task-evoked haemodynamic activation in the left DLPFC. The observed brain–behavior coupling suggests that theta-frequency neuromodulation may facilitate executive control under high cognitive demand via strengthened prefrontal neurovascular responses.

Steroid receptor coactivator-1: integrating steroid hormone signals to regulate brain function and disease

Steroid receptor coactivator-1 (SRC-1), also known as nuclear receptor coactivator-1 (NCOA1), represents the first identified member of the p160 nuclear receptor coactivator family and plays a pivotal role in integrating steroid hormone signals, regulating gene transcription, and maintaining neural homeostasis in the central nervous system (CNS). SRC-1 exhibits region-specific, cell-type-specific, and sexually dimorphic expression patterns in the brain, with prominent distribution in key regions including the hippocampus, cerebral cortex, hypothalamus, and amygdala. Functional studies demonstrate that SRC-1 participates in diverse neural functions such as learning and memory, energy metabolism, emotional regulation, and reproductive behavior through modulation of synaptic plasticity-related genes, neurotrophic factors, and metabolic pathways. Aberrant SRC-1 expression is closely associated with neurodegenerative diseases, autism spectrum disorders, and glioblastoma. This review systematically summarizes the molecular structure, expression characteristics, physiological functions of SRC-1, and its roles in neurological disorders, while discussing its potential applications as a diagnostic biomarker and therapeutic target.

Exploring the neuroprotective potential of ligustrazine: a preclinical meta-analysis and machine learning perspective on cerebral ischemia-reperfusion injury

ObjectiveThis study aimed to assess the efficacy of ligustrazine in treating cerebral ischemia-reperfusion (I/R) injury and construct a preclinical evidence framework by meta-analysis and machine learning.MethodsA systematic search was conducted for preclinical studies published in PubMed, Embase, Web of Science, and the Cochrane Library up to June 25, 2024. The inclusion criteria encompassed preclinical animal studies pertinent to the topic. Data extraction was performed independently by two individuals, Stata 17.0 software was used for quantitative analysis, R (version 4.3.3) and Python (version 3.11.4) were used for machine learning with neurological function score as the dependent variable.ResultsA total of 23 articles were included, involving 381 animals in the meta-analysis and 321 animals in the machine learning component. Ligustrazine significantly improved neurofunctional scores (NFS) [Longa criteria, SMD = −1.59, 95%CI (−2.16, −1.01), P < 0.001; mNSS criteria, SMD = −1.67, 95%CI (−2.36, −0.97), P < 0.001], cerebral infarct volume (%) [SMD = −2.56, 95%CI (−3.03, −2.09), P < 0.001], and BBB [SMD = −3.06, 95%CI (−4.53, −1.59), P < 0.001]. Furthermore, machine learning analyses, with NFS as the dependent variable, identified the time of first dose, duration, and dose as key determinants of neurofunctional improvement with ligustrazine. Notably, model interpretation suggested that greater improvements were more likely to occur when the initial administration of ligustrazine occurred within 24 h prior to (or 2.21 h post) the ischemic event, at a dosage of 23.53–34.69 mg/kg/day (or 45.71 to 75.65 mg/kg/day), and with an administration duration exceeding 71.43 h.ConclusionThe combination of meta-analysis and machine learning in this study not only confirms that ligustrazine is effective in reducing cerebral I/R injury, but also provides a framework for elucidating the preclinical intervention variables, thus offering novel insights for optimizing preclinical strategies of ligustrazine in cerebral I/R injury.

LFP-LOC: an LFP power–based method for validating the anatomical placement of high-density neural probes in rodents

High-density CMOS-based neural probes provide unprecedented spatiotemporal resolution for in-vivo electrophysiology, yet accurate validation of implant position remains challenging. Here we present LFP-LOC, a simple and interpretable method for intraoperative validation and refinement of probe anatomical location based on the spatial distribution of local field potential (LFP) power. Using spontaneous activity recordings performed in rodents, we compute power spectral densities in canonical LFP bands and apply dimensionality reduction and clustering to identify electrodes with shared spectral signatures. Across multiple implant sites, probe technologies, electrode layouts, and experimental conditions, the resulting clusters consistently align with anatomical boundaries. Applied to high-density probes with up to 1,024 electrodes/channels and sub-30 μm pitch, power features converge within approximately 20 s of recordings, allowing online intraoperative assessment. By leveraging the robust relationship between LFP power and brain structure, LFP-LOC enables rapid validation and adjustment of probe placement during surgery, complements histological validation, and may facilitate mesoscale electrophysiological studies.

Exercise as a multiscale recalibration of stress-related homeostatic balance

Chronic stress disrupts homeostasis in the brain and body, leading to anxiety, depression, and cardiovascular and metabolic dysfunction. Although exercise can counter these effects, the mechanisms are scattered across fields and not yet integrated. This review proposes a multi-scale framework. Exercise is not only stress-relieving; it is also a controllable challenge that can recalibrate the system when repeated bouts are matched by sufficient recovery and bioenergetic support. We propose that repeated exercise engages a stress response–adaptation–recovery cycle, in which peripheral signals from skeletal muscles, liver, adipose tissue and gut convey body metabolic state to the brain and are consolidated into durable plasticity only when mitochondrial capacity, substrate availability, and redox balance permit recovery. These signals pass through the blood-brain barrier and engage plasticity switches, including neurotrophic signals, epigenetic modification and metabolic coupling, thus stabilizing the neural circuits of threat appraisal, reward processing and contextual memory. By integrating these dimensions, we clarify how exercise can transform short-term physical stress into lasting resilience and provide direction for future research.

Task-aligned outcome learning in psychiatry: reducing endpoint dilution

Psychiatric research relies on well-defined outcomes for standardization, comparability, and replication, yet investigators often fix broad endpoints before knowing which symptom domains carry task-relevant signal. Even when psychometrically sound and clinically useful, composite measures can dilute predictive information and attenuate treatment effects when predictability or responsiveness concentrates in only a subset of symptoms—thus making studies appear negative despite meaningful change. This Perspective proposes a task-aligned, two-stage machine-learning framework for learning the appropriate outcome. In the first stage, constrained discovery derives a clinically interpretable outcome from a prespecified item pool. In the second, confirmatory evaluation tests the prespecified hypothesis either on a fixed learned outcome, when the aim is to assess a previously derived endpoint in a closely matched study, or on a relearned outcome generated by the same prespecified procedure, when the aim is to test whether that procedure can recover a task-aligned endpoint across different studies. The framework complements psychometrics and open-science practices, shifting focus from broad unsupervised composites to empirically supported targets, with safeguards to keep results interpretable and rigorous.