Google had urged Apple for years to support RCS texting to make communication between each company’s devices more seamless.
Read more Five-year old European military drone startup Helsing is close to raising another monster round.
Read more As microdrama apps like ReelShort and DramaBox quietly rake in billions, Peacock announced on Monday that it is launching two unscripted Bravo microdramas, which will stream in the Peacock app.
Read more This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
A few months before he was awarded the Nobel Prize in economics in 2024, Daron Acemoglu published a paper that earned him few fans in Silicon Valley. Contrary to what Big Tech CEOs had been promising—an overhaul of all white-collar work—Acemoglu estimated that AI would give only a small boost to US productivity and would not obviate the need for human work. It’s okay at automating certain tasks, he wrote, but some jobs will be perfectly fine.
Two years later, Acemoglu’s measured take has not caught on. Chatter about an AI jobs apocalypse pops up everywhere from Senator Bernie Sanders’s rallies to conversations I overhear in line at the grocery store. Some previously skeptical economists have gotten more open to the idea that something seismic could be coming with AI. A California gubernatorial candidate said last week that he wants to tax corporate AI use and pay victims of “AI-driven layoffs.”
On the one hand, the data is still on Acemoglu’s side; studies repeatedly find that AI is not affecting employment rates or layoffs. But the technology has advanced quite a bit since his cautious predictions. I spoke with him to understand if any of the latest developments in AI have changed his thesis, and to find out what does worry him these days if not imminent AGI.
AI agents
One of the biggest technical leaps in AI since Acemoglu’s paper has been agentic AI, or tools that can go beyond chatbots and operate on their own to complete the goal you give them. Because they can work independently rather than just answering questions, companies are increasingly pitching agents as a one-to-many replacement for human workers.
“I think that’s just a losing proposition,” Acemoglu says. He thinks agents are better thought of as tools to augment particular pieces of someone’s work than something malleable enough to handle a person’s whole job.
One reason has to do with all the various tasks that go into a job, something Acemoglu has been researching in his work on AI since 2018. For example, an x-ray technician juggles 30 different tasks, from taking down patient histories to organizing archives of mammogram images. A worker can naturally switch between formats, databases, and working styles to do this, Acemoglu says, but how many individual tools or protocols would an AI require to do the same?
Whether or not agents will supercharge AI’s impact on jobs will come down to whether they can eventually handle the orchestration between tasks that humans do naturally. AI companies are in heated competition to prove that their AI agents can work independently for ever longer periods without making mistakes, sometimes exaggerating the results—but Acemoglu says many jobs will be spared from an AI takeover if agents can’t fluidly switch between tasks.
The new hiring spree
For years Big Tech has been offering staggering salaries to recruit AI researchers. But I asked Acemoglu about a different hiring spree I’ve noticed: AI companies are all building in-house economics teams.
OpenAI hired Ronnie Chatterji from Duke University in 2024 to be its chief economist and announced last year that Chatterji will work with Jason Furman—Harvard economist and former advisor to Barack Obama—to research AI and jobs. Anthropic has convened a group of 10 leading economists to do similar work. And just last week, Google DeepMind announced it had hired Alex Imas, an economist from the University of Chicago, to be its “director of AGI economics.”
Acemoglu has noticed colleagues getting snatched up for these roles too. “It makes sense,” he says: AI companies are well aware that public skepticism about AI, in large part due to job concerns, is growing. And they have strong incentives to shape the economic narrative around their technology (consider OpenAI’s latest proposal for a new era of industrial policy).
“What I hope we won’t get,” Acemoglu says, “is that they’re interested in economists just to further their viewpoints or further the hype.” That tension hangs over the emerging field of “AI economics”; it’s concerning that some of the most influential research about AI’s impact on work may increasingly come from the companies with the most to gain from favorable conclusions.
AI apps
I don’t think of AI as hard to use; most of us interact with it via chatbots that use plain language. But Acemoglu says we should consider how it compares with the sort of software that kicked off earlier tech transformations, like PowerPoint for slide decks and Word for documents.
“Anybody could install these on their computer and get them to do the things that they want them to do,” he says. They spread accordingly.
“We have not seen the development of apps based on AI that have the same usability,” he says. Even if anyone can chat with an AI model, it tends to take a while for the average worker to get practical and productive use out of it. That’s part of the reason why AI has not yet shown any seismic impact on the job market or the economy. One of the key signals Acemoglu is watching, then, is the creation of apps that make AI easier to use.
But he acknowledges that for a while, we’re going to see all sorts of conflicting evidence about AI: anecdotes that college grads are finding the job market worse and worse, but no noticeable effect of AI on productivity, for example. “There’s a huge amount of uncertainty,” he says. And that’s the most telling thing about the AI economy right now: the certainty of the rhetoric alongside the uncertainty of everything else.
Despite years of digitization, organizations capture less than one-third of the value expected from digital investments, according to McKinsey research. That’s because most big companies begin with technological capabilities and bolt applications onto them, rather than starting with customer needs and working backward to technology solutions. Not prioritizing the customer can create fragmented solutions; disjointed customer experiences; and ultimately, failed transformations.
Organizations that achieve outsized results from AI flip the script. They adopt a “customer-back engineering” mindset, putting customers at the heart of technology transformation.
It’s a strategy in which products and services are developed with the customer experience first in mind, including the customers’ challenges, needs, and expectations. Product development teams then work backward in a nimble and agile way to find the steps necessary to design and build solutions that achieve the desired experience.
“When you get your engineers closer to customers, you get a lot more sideways innovation,” says Ashish Agrawal, managing vice president of business cards and payments tech at Capital One. “That leads to a multiplier effect, because engineers can approach a problem from a different dimension that can be unique to the sales or product perspective.”
The case for customer-centricity in engineering
Engineers are problem-solvers by nature, says Agrawal. When they hear about challenges customers are experiencing, or how they are using products and services in the real world, they can devise ways to efficiently address customer needs, since they are naturally closer to systems and data than many other teams across the company.
“Fostering a customer-centric culture has a motivational effect on engineers when they actually start seeing how the core changes they’re making, or the features they’re adding, are having a direct impact on the lives of customers,” says Agrawal.
It also takes discipline. Agrawal explains that Capital One has set a goal for every engineer in his organization to establish several touchpoints with customers throughout the year in different forms, including:
- Digital empathy sessions to observe user journeys and identify where users hit friction
- Embedded customer support for periods of time to deepen understanding of servicing needs
- Engineering ride-alongs, in which engineers join customer success, sales, and support staff on calls or on-site visits
- Hackathon competitions to build solutions around real customer problems
The AI opportunities with customer-centricity
“The biggest challenge engineers within large companies face is a lack of direct access to customers,” says Agrawal. “This can make it harder for technologists to work with customers to identify problems and innovate solutions.”
AI has accelerated the challenges as well as the opportunities. The lifecycle of launching products has become significantly faster. But the good news is that engineers are closer to the data that feeds into AI, so they can more rapidly apply AI-informed data techniques to solve customer problems.
Agrawal outlines a recent scenario: In the customer servicing space, conversations can instantly be summarized and give a customer agent context on the member’s original request and remaining action points. Agentic AI can also be enabled to ask pointed follow-up questions about the interaction that would otherwise take human agents time to read through the entire thread.
“A solution would have been a lot harder in an ecosystem without a lot of high-quality data,” says Agrawal. “But when you combine a rich data ecosystem with agentic tools, you move from incremental fixes to high-velocity transformation.”
By investing in AI data and tools and focusing on rapid experimentation, Agrawal says the cycle of deploying solutions can be accelerated. Teams learn that if they meet customer needs and iterate on a wider range of solutions much faster, then the entire innovation cycle speeds up.
For example, Capital One used customer insights to build a state-of-the-art, multi-agent AI framework called Chat Concierge to enhance the customer experience for car buyers and dealers. In a single conversation, Chat Concierge can perform tasks like comparing vehicles to help car buyers decide on the best choice and scheduling test drives or appointments with salespeople.
Agrawal explains that car buyers can engage with Chat Concierge directly through participating dealer websites. Dealers can access and can take over the chat through Navigator Platform. The AI assistant consists of multiple logical agents that work together to mimic human reasoning, allowing it to provide information and take action based on the customer’s requests.
The elements of an AI-first mindset
According to a recent MIT Technology Review Insights survey, 70% of leaders say their firm uses agentic AI to some degree. Roughly half of executives say agentic AI systems are highly capable of improving fraud detection (56%) and security (51%), reducing cost and increasing efficiency (41%), and improving the customer experience (41%).
Looking into the future, achieving these outcomes looks even more likely. More than half of the banking executives surveyed say they expect to continue to improve fraud detection (75%), security (64%), and the customer experience (51%). Agentic AI use cases that show strong potential to transform the customer experience in financial services include responding to customer services requests, adjusting bill payments to align with regular paychecks, or extracting key terms and conditions from financial agreements.
Placing the customer at the center of a transformation requires an AI-first mindset. Companies must shift from simply augmenting an existing product to fundamentally reimagining the problem and the user’s needs through the lens of AI’s capabilities.
A few best practices that Agrawal recommends include:
Reimagine the core function of AI to solve a user’s problem: “The true value isn’t in chasing the AI hype; it’s in solving meaningful customer problems. By focusing on impact, we ensure that our innovation isn’t just fast; it’s transformative,” says Agrawal.
Start with high-quality, well-governed data as the foundation: “Data readiness and unified information across systems are the non-negotiable foundations of AI. A clean data layer is what orchestrates the agentic loop— enabling the perception, reasoning, and execution required to solve a customer’s problem before they even have to ask,” explains Agrawal.
Rebuild workflows with AI embedded from the start: “People treat models as black boxes, but agentic systems require tremendous rigor and oversight. Having a data ecosystem that is well-governed and responsible AI standards are essential pillars for building trust in these systems,” says Agrawal.
Build a cross-functional team involving data science, engineering, product, design, and other partners: Agrawal advises, “It’s important to be open and nimble to transforming how we work and create impact as AI becomes more integrated into workflows. It’s also important to take a ‘crawl, walk, run approach’ if you are new to AI, as opposed to simply jumping into it.”
In the end, achieving end-to-end transformation depends on empowering engineers and partner teams to start with customer needs and work backward to technology solutions, rather than starting with technological capabilities first and finding applications for them. When organizations make a customer-back approach second nature, they are able to not only reimagine the customer experience from the inside out, but to also place the customer front and center from the very start.
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 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.
In finance departments that have long been defined by precision and control, AI has arrived less as a neatly managed upgrade than as a quiet insurgency. Employees are already using it while leadership races to impose structure, governance, and strategy after the fact. The result is a paradox: one of the most tightly regulated functions in the enterprise is now among the most experimentally transformed.
What’s emerging is a layered shift in how work gets done. From variance commentary and fraud detection to contract review and close narrative drafting, AI is embedding itself across workflows, particularly where unstructured data once slowed down everything. Yet, as Glenn Hopper, head of AI and managing director at VAi Consulting, puts it, “the proliferation of AI happened kind of before governance and before a real plan came about.” That bottom-up adoption is forcing a recalibration at the top, where executives must now reconcile productivity gains with oversight, risk, and accountability.
Just as critical is reframing AI’s role. “AI as a means to an end, as opposed to AI being the end,” says Ranga Bodla, VP of industry and field marketing at Oracle NetSuite, underscores a growing consensus: the technology is most effective when it disappears into existing processes rather than outright replaces them. Embedded systems, seamless integrations, and tools like model context protocol (MCP) are accelerating this shift, making AI an ambient capability. Notably, ease of integration, not cost savings or new features, has become the strongest driver of adoption.
Still, the real constraint may be neither data nor technology, but people. “Talent is the actual root cause,” Hopper argues, pointing to a widening gap between domain expertise and AI fluency. Even as concerns about data security and model opacity persist, the more pressing risk may be misunderstanding the tools altogether or restricting them so tightly that employees look for workarounds beyond leadership control. “The auditability of it, I think, is critical,” Bodla notes.
Looking ahead, the trajectory is clear but variable. AI agents capable of executing complex, multi-step tasks are beginning to materialize, while expanding context windows and interoperable systems promise deeper, more persistent intelligence. But the real transformation may be a gradual shift toward systems that bolster judgement, automate routines, and allow finance teams to spend less time reconciling the past and more time shaping what comes next.
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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.
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.
—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
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.
The option would let users hide on-screen function buttons, and is already available on competitor platforms such as TikTok and Reels.
The subscription model lets users pay a monthly fee to disable data tracking for targeted promotions, in line with current regulations.