TECH

Europol targets underground banking network linked to drug trafficking

Some of the world's leading underground bankers were targeted in an international law enforcement operation against a sophisticated financial network suspected of financing drug trafficking and laundering money derived from criminal activities worldwide.

At the heart of the investigation lies a structure known as the "Dubai Bank." Despite the name, it is not a conventional banking institution but rather a clandestine network that, according to Europol, provided financial services to criminal organizations.

The network is suspected of having provided the means to finance large drug shipments and subsequently move the proceeds generated from their sale.

This type of underground banking enables the transfer of value between countries via operators who receive and deliver funds in different jurisdictions, bypassing the need for every transaction to go through conventional banking channels. Balances between intermediaries can be settled later through other financial or commercial flows.

For criminal organizations, these operators function as financial service providers: they facilitate payments to suppliers, the movement of money across borders, and the concealment of the origin or destination of proceeds.

The case originated with the seizure of 1.8 tons of cocaine off the Spanish coast. The subsequent financial investigation allowed authorities to look beyond the drug transport chain and identify the operators suspected of moving the money linked to these criminal activities.

According to Europol, the investigation ultimately exposed a network capable of moving illicit funds on an international scale. The European agency describes some of the suspects as being among the major underground bankers identified by authorities—a classification that should be understood as Europol’s own assessment.

The operation also demonstrates a strategy that goes beyond merely arresting those directly responsible for the crimes. Authorities aim to target the services, assets, and infrastructure that allow criminal organizations to continue operating. This same logic was applied in Operation Endgame, where authorities targeted cybercrime infrastructure and identified or froze over €41 million in digital assets.

The investigation, led by the Spanish National Police (*Policía Nacional*) and supported by Europol, resulted in the arrest of 21 suspects for crimes including participation in a criminal organization, drug trafficking, and money laundering.

An operation carried out in Spain on July 22, 2026, resulted in the arrest of 15 suspects. Spanish authorities also issued 19 international arrest warrants for suspects located outside the country. Six of these warrants have already been executed, resulting in four arrests in the United Arab Emirates, one in Egypt, and one in the Netherlands.

Among those detained is a key figure from a major underground banking network known as the "Dubai Bank." Identified by Europol as a high-value target, the suspect is believed to have played a pivotal role in providing financial services to large-scale drug trafficking operations worldwide.

Authorities identified, seized, or froze assets worth approximately €20 million. These included 48 properties valued at over €14 million, luxury vehicles worth more than €1.6 million, and 121 bank accounts with a combined balance of €2.3 million.

Cocaine investigation leads to the money...Operation DRAKKAR stemmed from an investigation into a large cocaine shipment intercepted by the Spanish National Police in February 2021. Officers boarded a vessel in Spanish coastal waters and discovered 1,835 kilograms of cocaine. All nine crew members were arrested. After the crew and the drugs were removed, the vessel was towed to the port of Gijón, Spain. The ship subsequently sank after taking on water due to a leak allegedly caused by the captain.

Investigators later discovered the reason: another 1,650 kilograms of cocaine had been hidden on board. It is suspected that members of the criminal network later entered the sunken ship and retrieved the hidden cargo. It was determined that both cocaine shipments had been loaded in South America in January 2021. The plan was to transfer the drugs on the high seas to speedboats, which would then transport the cocaine to shore in Spain.

The investigation into those responsible for the shipment gradually led investigators to higher levels of the criminal chain. Links were established to other suspected drug trafficking operations worldwide, while financial tracing exposed individuals suspected of financing the shipments and laundering the proceeds of the trafficking.

This eventually led investigators to a sophisticated international financial infrastructure used to provide underground banking services to organized crime.

The “Dubai Bank”... At the heart of the investigation was an underground money transfer network known as the "Dubai Bank." The network is suspected of making large sums of money available to criminal organizations in different countries on very short notice, providing the financial infrastructure needed to fund large drug shipments and move the resulting illicit profits.

Instead of physically transferring cash across borders for each transaction, underground banking networks can move value between jurisdictions through networks of brokers. Payments can be settled via internal accounting and clearing mechanisms, including commercial transactions, companies and their bank accounts, centralized cash management, or transfers of other assets.

The network operated on a commission basis, with fees varying depending on the transaction and other factors.

Investigators also identified the use of tokens to authenticate cash transactions. A unique identifier—often the serial number of a banknote—could be passed along the chain of transfer. Presenting the corresponding identifier upon receipt of the cash allowed the parties involved to verify that the money was being handed over to the intended recipient.

The financial investigation also identified assets suspected of having been acquired with illicit funds, including a luxury property in Ibiza.

Europol support...Europol supported the investigation by facilitating the international exchange of information and providing operational and financial analysis that helped identify key financial intermediaries within the network. Since October 2024, the investigation has also been supported by an Operational Task Force established at Europol.

Europol provided expertise in underground banking systems and asset tracing and recovery. Specialists were deployed to Spain for the day of the operation, where a money laundering expert supported investigators on the ground using a mobile office.

Given the network's global reach, international cooperation was crucial to the investigation. Authorities from the Netherlands, Sweden, and the United States—including the U.S. Drug Enforcement Administration (DEA)—contributed to the investigation.

 

mundophone

 

TECH

Apple iPhone 18 Pro: Leak reveals exciting new camera features

The Apple iPhone 18 Pro (Max) and the iPhone Ultra are expected to launch with five exciting new camera features, which can already be previewed in the beta version of iOS 27. Photography enthusiasts in particular are likely to appreciate some of these new features.

The Apple iPhone 18 Pro, iPhone 18 Pro Max, and iPhone Ultra are expected to be officially unveiled on Wednesday, September 9. Apple is said to be equipping its next-generation flagship smartphones not only with hardware upgrades, such as the main camera with a variable aperture on the Pro models, but also with a range of new software features that could be made available, at least in part, on the iPhone 17 Pro with the update to iOS 27.

MacRumors has already identified five new features in the iOS 27 beta that are likely to make the Camera app much more interesting for enthusiasts. First and foremost, it will finally be possible to manually focus the camera without having to rely on a third-party app. When manual focus is selected, the focus distance can be adjusted using a slider at the bottom of the screen. In line with this, the Camera app is expected to support Focus Peaking, which allows you to highlight in-focus areas of the image with a color of your choice, making it easier to see what’s currently in focus.

Manual exposure adjustment is also expected to be simplified in two ways. First, the iPhone 18 Pro’s camera app reportedly offers a histogram that shows when details are lost in bright or dark areas of the image. And second, areas of the image that are overexposed can be highlighted with a color overlay. The self-timer will reportedly allow users to take multiple photos in succession at a preset interval. Finally, the Camera app will be able to automatically adjust lens correction based on the selected aperture – further evidence that the aperture on the iPhone 18 Pro can be adjusted.

The iPhone 18 Pro Max battery is rumored to have a capacity of 5,391 mAh in the Chinese version and could reach 5,567 mAh in the North American model, which is sold exclusively with eSIM. These figures appeared in alleged filings with China's 3C regulatory body but have not been officially released by Apple.

The difference between the versions is reportedly due to the space taken up by the physical SIM card tray. With the previous generation, Apple itself confirmed that eSIM-only models utilized the freed-up space to accommodate a larger battery.

If the leak is accurate, both the 5,391 mAh and 5,567 mAh cells would surpass the 5,000 mAh capacity confirmed by Samsung for the Galaxy S26 Ultra.

This potential upgrade would place the new iPhone in an unusual position in a direct comparison with Samsung. However, the OnePlus 15 would still lead by a wide margin in terms of rated capacity: the device's official specifications list a battery equivalent to 7,300 mAh, supported by charging speeds of up to 120 W.

Does higher mAh mean longer battery life? Not necessarily. While the iPhone 18 Pro Max's battery capacity determines how much energy can be stored, actual battery life also depends on the efficiency of the processor, display, modem, and operating system, as well as temperature and usage patterns.

For this reason, two phones with similar capacities can yield different results in tests involving web browsing, video playback, or gaming. Valid comparisons require identical testing procedures, screen brightness settings, connectivity, and temperature conditions.

The iPhone 17 Pro Max, for instance, has a lower rated capacity than the OnePlus 15, yet the integration of hardware and software allows Apple to claim up to 37 hours of video playback for the Brazilian version. This figure, however, cannot be directly compared to independent tests conducted by other outlets.

A20 Pro chip...The A20 Pro could improve the iPhone 18 Pro Max's battery efficiency, though neither the processor itself nor its manufacturing process has been officially confirmed. Rumors suggest the component will be manufactured using TSMC's 2-nanometer technology.

TSMC claims its N2 process can consume 25% to 30% less power than N3E when chips operate at the same speed. This estimate refers to the manufacturing process, not a phone's total battery life.

It would be incorrect to equate this theoretical reduction directly to a 25% or 30% increase in battery life. Processor power consumption accounts for only a portion of the device's total energy usage; the screen, cameras, modem, and apps also impact the outcome.

Will the iPhone 18 Pro Max last 20 hours in tests? There are no battery test results for the iPhone 18 Pro Max yet. The possibility of exceeding the 20-hour mark in a benchmark—as cited by Tom’s Guide—is a projection based on leaked capacity details and the potential efficiency of the A20 Pro chip.

The device has not yet undergone the test published by the outlet. Any battery life figures presented prior to a product review should be treated as estimates, not as specifications confirmed by Apple.

Confirmation of the new battery? The first opportunity will be the Apple Event on Wednesday, September 9, 2026. The presentation begins at 10:00 AM  and will be officially streamed online. Overdrive has prepared a guide with the schedule and details on where to watch the Apple Event.

The company typically releases its own estimates for video playback and streaming, but it does not always disclose the capacity in mAh in its commercial specifications. Even after the announcement, the exact figure may depend on regulatory filings or device teardowns.

mundophone

TECH

The AI ​​shock to employment

For a long time, the debate surrounding artificial intelligence and jobs seemed to belong to the distant future. That gap is closing rapidly. Companies are already automating tasks related to programming, customer service, analysis, and content creation, while workers try to figure out which skills will remain valuable. According to Mo Gawdat, a former Google executive, we are only at the beginning. His latest prediction sets a surprisingly near date for a transformation that could hit those currently entering the job market particularly hard.

Mo Gawdat, former Chief Business Officer at Google X, has been warning for some time that the advance of artificial intelligence will not be just another technological shift in the professional landscape.

In his view, the change could affect the very structure of traditional employment.

His latest estimate is particularly striking: by 2028, around 30% of jobs in certain sectors could disappear due to the accelerated adoption of AI.

This does not mean that three out of every ten jobs on the planet will inevitably cease to exist by that date.

This is Gawdat’s own prediction regarding sectors especially vulnerable to automation; other studies present considerably more complex scenarios, in which many professions are transformed rather than simply eliminated.

Even so, the former executive believes that the current pace of technological change makes it dangerous to expect a slow transition.

Tools that, just a few years ago, could only generate short texts now write code, analyze documents, research information, create presentations, and execute entire sequences of tasks.

And there is one group that may feel this shift before others.

Young people may find a much narrower entry point... Gawdat is particularly concerned about recent graduates.

For decades, many careers followed a relatively predictable structure. Entry-level professionals performed simpler tasks, learned from experienced colleagues, and gradually took on greater responsibilities.

Artificial intelligence is beginning to disrupt precisely that initial tier. Summarizing documents, producing preliminary reports, researching information, writing simple code, organizing data, and preparing materials are examples of activities that traditionally served as entry points for junior professionals.

Now, many of these tasks can be performed in seconds by generative systems.

This creates a paradox.

If companies need fewer entry-level workers, how will they develop the experienced professionals needed to fill leadership roles ten or twenty years from now?

For recent university graduates, Gawdat’s advice is not to try to compete directly with machines on speed.

He recommends learning to use these tools while simultaneously strengthening capabilities where the human element remains central.

Nurses, therapists, and human-centric roles fall into a different category... Among the examples Gawdat cites are professions such as nursing, counseling, and other activities built around human contact.

The logic is relatively simple.

An artificial intelligence system can analyze medical information, prepare a report, or answer questions. That does not mean it can fully replace the physical presence of someone caring for a patient.

The same applies to jobs that rely on trust, empathy, negotiation, responsibility, and complex interpersonal relationships.

This does not make these professions immune to AI.

They, too, will be transformed.

A healthcare professional might use intelligent systems to analyze medical records. A teacher could prepare materials using AI. A therapist might use digital tools for certain administrative tasks.

The difference lies in automating parts of the work without necessarily eliminating the human role at its core.

Meanwhile, some of the largest technology companies are already offering a glimpse of how this transformation might unfold.

Google itself expects its employees to use AI...Within Google, artificial intelligence has moved beyond being merely an experimental tool.

The company has been encouraging employees across various areas—including non-technical roles—to incorporate AI systems into their daily work routines.

Engineers use coding assistants. Sales teams can analyze conversations and prepare for meetings. Strategy professionals can accelerate the production of documents and reports.

The goal is not necessarily to remove the human from the process, but to alter what is expected of them.

If a task that once took three hours can be completed in 20 minutes with the help of AI, the benchmark for productivity inevitably begins to shift.

This effect can extend far beyond technology companies.

When an organization discovers that a specific team can produce more by using automation, competitors face pressure to do the same.

And it is precisely this dynamic that makes predictions about jobs so difficult.

Disappearing is not the only way a job can be affected... There is a vast difference between a job vanishing and having some of its tasks automated.

A lawyer can still exist even if an AI generates the first draft of a contract. A programmer remains necessary even if a system writes part of the code. A journalist does not automatically become obsolete simply because a machine can summarize documents.

What changes is the composition of the work.

Repetitive functions may decrease, while tasks involving supervision, decision-making, verification, and human interaction grow.

That is why predictions like Gawdat’s should be interpreted as scenarios rather than inevitable timelines.

The labor market also depends on economic growth, legislation, the cost of technology, business confidence, the creation of new professions, and workers' ability to adapt.

Yet, there is one part of his warning that seems increasingly less futuristic.

The discussion is no longer about whether artificial intelligence will be used in the workplace.

It is already being used.

The question that is starting to matter is different: when a machine can perform in minutes what used to take up a large part of a professional's day, what exactly will be the work left for that person to do?

Artificial intelligence is changing the workforce by boosting productivity in some sectors while displacing routine tasks in others, leading to a complex shift rather than an immediate jobs apocalypse

Current impact on employment:

-The "Apocalypse" is postponed: Broad, catastrophic unemployment has not materialized. Studies from organizations like the Stanford Institute for Economic Policy Research indicate that AI's overall effect on near-term employment numbers remains relatively small

-Productivity gains and growth: In some highly exposed professions, productivity gains have actually increased demand. For example, employment for paralegals and market-research analysts grew faster than the national average as AI tools helped them handle tasks more efficiently

-Visible reductions: Routine-heavy administrative and support roles have seen declines. Fields like customer service, secretarial work, and entry-level corporate recruiting face measurable headwinds and reduced new listings

-Entry-level pressures: New college graduates face a tougher job market, with entry-level listings dropping and application volumes surging as firms favor automation for foundational tasks

Why disruption is uneven:

-Messy jobs: Many roles are more AI-resistant than expected because they require complex human interaction, physical dexterity, or multitasking. Trades (plumbers, electricians) and specialized professionals (radiologists who also consult and comfort patients) remain largely insulated

-Double-edged career shocks: Research on Career Shocks in the Age of AI highlights that workers experience both positive shifts (optimized task processes, enhanced skills) and negative pressures (automation of specific duties)

mundophone 

DIGITAL LIFE

A new generation of digital lies worries experts

Lies have always existed, but something has changed radically. Today, a fabricated story can be accompanied by convincing images, a seemingly authentic voice, and even forged documents before reaching millions of people via social media. Once that happens, undoing the damage can be virtually impossible. Faced with this new landscape, a delicate debate is emerging in Argentina: at what point does a digital forgery cease to be mere misinformation and instead warrant a criminal response?

Artificial intelligence did not invent fake news. What it did was make its production faster, cheaper, and more sophisticated.

Manipulated information can circulate on social media in seconds, be amplified by algorithms, and reach thousands or millions of users before anyone can verify its authenticity. The problem does not end when the post stops going viral.

False content can remain available for years, reappear in search engine results, and even be incorporated into the responses of artificial intelligence systems, taking on a sort of digital "second life."

It is against this backdrop that Argentine criminal lawyer Jorge Monastersky proposes opening a legal discussion on what he terms "willful informational manipulation."

The idea is not to turn every lie into a crime. The focus would be on much more specific situations: instances where someone knowingly produces or disseminates false or manipulated information—presenting it as true—with the deliberate intent of destroying another person's reputation or credibility.

The proposal takes on even greater complexity with the advent of so-called "deep news."

This concept describes false, distorted, or artificially constructed content that manages to pass for genuine news.

Artificial intelligence has vastly expanded these possibilities. Today, it is possible to alter photographs, mimic voices, produce videos, modify documents, or create entire scenes with a level of realism that makes it difficult to immediately distinguish between what actually happened and what was fabricated.

A fake video can place a person in a situation that never occurred. Synthetic audio can make them utter words they never actually spoke. Artificially constructed news can combine true and false elements to produce a narrative completely divorced from reality.

For Monastersky, however, the mere use of artificial intelligence should not be enough to constitute a crime.

The difference lies in intent.

An image created for humor, a parody, a work of art, or even a journalistic report later proven incorrect would not automatically fall into this category. It would be necessary to demonstrate that the person responsible knew the content was false or manipulated yet decided to present it publicly as true with the specific aim of causing serious harm.

This distinction is crucial because it directly touches upon another sensitive issue: freedom of expression.

The boundary between combating fakes and protecting press freedom...Creating a new criminal offense related to disinformation carries obvious risks. Overly broad legislation could end up being used against journalists, political opponents, comedians, or anyone publishing information inconvenient to powerful individuals.

For this reason, the proposal draws a line between criticism and deliberate fabrication.

Political criticism, journalistic investigations, whistleblowing, satire, parody, opinions, and value judgments would remain protected. A news report containing an error would not, in itself, be sufficient to constitute the offense in question.

To establish liability, several elements would need to be present simultaneously: factually false or manipulated information, knowledge of that falsity, specific intent to cause harm, and the objective capacity to inflict serious damage on the victim's social, professional, or institutional reputation.

A single post can be enough to destroy a reputation...The impact need not involve celebrities or politicians.

For an ordinary person, a false accusation accompanied by a seemingly authentic video can lead to the loss of a job, clients, contracts, professional relationships, and personal ties. A career built over decades can be called into question by something produced in minutes.

When the target holds public office, the problem takes on a new dimension. A fabrication targeting a judge, prosecutor, legislator, or minister may not only harm that individual but also undermine public trust in the institution they represent.

The proposal discussed by the lawyer suggests a starting point for debate of two to four years in prison, though the final determination would rest with the legislature and depend on broader criteria regarding proportionality and criminal policy.

It would not necessarily require a massive disinformation campaign. Depending on its reach and severity, a single post, deepfake, or manipulated news story could trigger devastating consequences.

The real challenge begins once the lie goes viral...The discussion highlights a problem likely to grow as generative tools become more advanced.

For years, convincingly manipulating a video or imitating someone’s voice required technical expertise, equipment, and time. Now, similar capabilities are becoming available to millions of users.

At the same time, social media platforms provide the perfect infrastructure for distributing these creations at scale.

The result is an unprecedented combination: producing a fabrication has become easier precisely when spreading it has also become faster.

The legal issue, therefore, is not simply deciding whether lying should be a crime. It is about determining how to deal with someone who knowingly fabricates a false reality, presents that construct as true, and exploits the amplification power of platforms to try to destroy another person.

Artificial intelligence can create a fabrication in seconds. The internet can spread it even faster. The problem is that by the time the truth finally catches up with the lie, the damage may already be done.

mundophone

DIGITAL LIFE

AI could create new jobs while leaving millions of workers unable to access them

Much of the debate surrounding artificial intelligence centers on a seemingly simple question: how many jobs will disappear, and how many will be created? But there is another, far more complex possibility. What if new jobs do emerge, but are located in the wrong places for the people who need them? New projections for the U.S. labor market show that technology, an aging population, and workforce skills may move in different directions, creating a sort of labor puzzle.

The problem begins even before considering what artificial intelligence will be capable of doing.

Based on current immigration levels, projections from the Indeed Hiring Lab indicate that the U.S. workforce could shrink by about 1.2 million people by 2040.

However, the impact could be even more pronounced before then.

By 2032, the reduction could reach approximately 5.9 million workers—or 3.7% of the workforce—driven primarily by an aging population and retirements.

It is tempting to imagine that artificial intelligence would automatically fill this void.

After all, if fewer people are available to work and machines can take over an increasing number of tasks, the two phenomena might simply offset each other.

The problem is that they are not necessarily occurring in the same places.

Sectors most exposed to the impact of AI include information, financial activities, and professional and business services.

Meanwhile, the greatest need for workers is expected to arise in very different areas.

And this mismatch completely changes the conversation.

AI is advancing in areas where the labor shortage may not be most acute... Construction, healthcare, and public administration are among the sectors projected to have a significant need for labor.

These are precisely the areas where artificial intelligence offers less potential for direct replacement.

An AI can analyze medical records, for example, but that does not mean it can take over all the physical and human tasks performed by a nurse. Similarly, generative systems can assist architects and engineers, but they do not automatically replace the workers needed to build a house.

A curious scenario emerges.

While certain administrative and professional roles may see an increasing number of tasks automated, other areas continue to seek workers.

In theory, simply shifting workers from one sector to another should suffice.

In practice, changing professions is far more complex than moving a piece across a game board.

The key question becomes: how much of a person's existing skillset can be leveraged in a different occupation?

Some skills are transferable across professions, while others require years of preparation; certain competencies appear across various sectors.

Communication and business management are prime examples.

According to data cited by the Indeed Hiring Lab, basic skills related to business operations appear in over 70% of job postings in the United States.

This means workers can carry over part of their experience when switching careers.

The expansion of AI itself demonstrates that these boundaries are becoming more fluid.

In the United States, 63% of AI-related job openings fall outside traditional technology occupations.

Across the six countries analyzed, five show at least half of these openings outside the traditional tech core.

However, there is a significant obstacle.

Not every competency can simply be transferred.

Working in healthcare, for instance, often requires specific education, professional certifications, and experience that cannot be gained merely by using a chatbot.

At the same time, companies are demanding more from candidates.

Job postings now require two more skills than they did before the pandemic; the average job listing currently calls for two additional competencies compared to pre-pandemic levels.

There is also a growing number of non-tech sector jobs requiring at least one technical skill.

This creates two vastly different realities.

For workers with access to training who can quickly update their skills, this transformation may open up new possibilities.

For those lacking the time, money, or access to training, this same shift may raise the barrier to entry even higher. That is where one of the most interesting paradoxes of artificial intelligence arises.

The very technology that contributes to changing professional requirements could also help workers navigate this transition.

AI systems can analyze existing skills, identify similarities with other professions, and indicate the knowledge required to switch fields.

They can also serve as learning tools during career transitions.

But this is far from solving the entire problem.

Even with AI’s help, the numbers still don’t add up. According to the projections presented, even in a scenario where artificial intelligence complements workers, boosts productivity, and helps generate new opportunities, these effects would offset only about 11% of the workforce losses associated with the demographic shifts considered in the study.

This figure reveals why focusing solely on how many jobs AI might destroy may be insufficient.

There may be job openings.

There may be workers seeking opportunities.

And yet, the two sides might simply fail to connect.

An administrative professional does not instantly transform into a nurse. A marketing worker cannot step into a specialized construction role overnight. And someone with decades of experience may struggle to compete for positions that now require digital skills that did not exist when they began their career.

Therefore, the future of employment may depend as much on education, training, professional mobility, and the recognition of transferable skills as it does on artificial intelligence itself.

The paradox is striking.

For years, the question was whether machines would leave people jobless.

The scenario now emerging is different—and perhaps more difficult to resolve: we could end up with an economy full of job openings and, at the same time, full of workers who lack the necessary pathway to fill them.

AI can create new jobs while leaving millions behind because the new roles require advanced technical skills that current workers do not have and cannot easily learn in time.This problem is called the skills mismatch. It means the type of work being created does not match the skills of the people who lose their jobs.

What new jobs does AI create?

-AI trainers: People who teach AI models by labeling data or writing prompts

-Data analysts: Workers who clean and study large sets of information

-Ethics and policy experts: Professionals who make sure AI is used safely and legally

-Maintenance engineers: Technicians who repair and update AI hardware and servers

Why millions cannot access them:

-The skills gap: New jobs need high-level computer science, math, or engineering knowledge. Factory workers, cashiers, or office clerks cannot switch to these roles overnight

-High cost of training: Going back to school or taking specialized courses costs a lot of money and time. Many workers live paycheck to paycheck and cannot afford to stop working to study

-Fast speed of change: AI technology improves much faster than schools and training programs can update their classes

-Location and access: Most high-paying tech jobs are in big cities or wealthy countries. Workers in rural areas or developing regions often lack good internet and local training centers

The displacement problem:

-Old jobs disappear fast: AI can replace routine tasks in customer service, writing, and coding very quickly

-New jobs grow slowly: Companies take time to build new departments and hire new teams

-The gap widens: People who lose routine jobs face long periods of unemployment before they can find a way back into the workforce

mundophone

IFA 2026

New AA and AAA lithium-ion batteries with USB-C unveiled

Verbatim plans to launch lithium-ion AA and AAA rechargeable batteries, along with a 9V battery. All models can be charged via USB-C, eliminating the need for a dedicated battery charger. Thanks to voltage conversion, the rechargeable batteries can replace standard disposable batteries.

Verbatim unveiled its new rechargeable batteries at IFA. The lineup includes AA and AAA models as well as a 9V battery. Instead of NiMH, Verbatim uses lithium-ion cells paired with voltage converters to ensure compatibility with conventional battery compartments, delivering 1.5V for the AA and AAA models and 9V for the larger battery. This offers an advantage over 1.2V rechargeable batteries, which do not work well with some devices, partly because those devices cannot accurately estimate their remaining capacity.

Verbatim could not tell us at its booth whether the batteries simulate a discharge curve like Nitecore's NH2400 or maintain a fixed voltage. A simulated discharge curve would allow basic devices to estimate the batteries' remaining charge. With a fixed voltage, the batteries could stop working abruptly and without any prior warning, which would be a disadvantage.

On the other hand, USB-C allows the batteries to be charged without a dedicated charger, including while traveling. Verbatim includes a multi-connector charging cable with the AA and AAA batteries, reducing the number of cables needed. Interestingly, the USB-C port on the AAA batteries has been positioned lengthwise. Acebeam uses a transverse orientation at this size, although its model is a 3.7V 10440 battery, as is common with USB-C rechargeable AA batteries.

The batteries have capacities of 0.75Wh for AAA, 3.15Wh for AA and around 3Wh for the 9V model. Verbatim also lists capacities in mAh, but the different voltages make these figures unsuitable for direct comparison. The AAA batteries are rated at around 500mAh, while the 9V battery offers "only" 335mAh. This could make the 9V battery appear to have less capacity, even though the 335mAh model actually stores significantly more energy than the 500mAh model.

According to Verbatim, the rechargeable batteries are expected to launch within the next few weeks. The company has not revealed any pricing details thus far.

Verbatim's new USB-C rechargeable batteries come in three versions: AAA (500 mAh / 750 mWh / 1.5V), AA (2100 mAh / 3150 mWh / 1.5V), and 9V (335 mAh / 3015 mWh / 9V).

Key features:

-Direct Charging: They eliminate the need for traditional wall chargers, as they feature a built-in USB-C port directly on the battery itself.

-Voltage Conversion: They utilize internal technology to stabilize voltage and precisely replace conventional disposable batteries.

-Included Accessories: The manufacturer provides a multi-connector charging cable to facilitate simultaneous charging and reduce cable clutter.

Capacity and formats:

-AAA: Nominal capacity of approximately 500 mAh (0.75 Wh), with a lengthwise-positioned USB-C port.

-AA: Energy equivalent to 3.15 Wh.

-9V: Model with a capacity of approximately 3 Wh.

mundophone

TECH

Quantum computing is an inevitable threat to bitcoin?

There is a question that has accompanied Bitcoin almost since quantum computing began to advance: what would happen if a sufficiently powerful machine managed to break the cryptography protecting digital currencies? This hypothesis has already raised alarms among researchers, investors, and developers. Now, two studies viewed side-by-side suggest a much more complex—and potentially reassuring—scenario, although the debate is far from over.

The quantum threat to Bitcoin is primarily linked to the elliptic curve cryptography used in its digital signatures. In theory, a sufficiently advanced quantum machine could use Shor's algorithm to solve the mathematical problem protecting certain keys.

Quantum computers and cryptography...A great amount of digital ink has been spilled on the topic of how quantum computers pose an existential threat to currently used asymmetric cryptography. We will therefore not discuss this in detail, but only explain the aspects that are relevant for the analysis in this article.

In asymmetric cryptography, a private-public key pair is generated in such a manner that the two keys have a mathematical relation between them. As the name suggests, the private key is kept as secret, while the public key is made publicly available. This allows individuals to produce a digital signature (using their private key) that can be verified by anyone who has the corresponding public key. This scheme is very common in the financial industry to prove authenticity and integrity of transactions.

The security of asymmetric cryptography is based on a mathematical principle called a “one-way function”. This principle dictates that the public key can be easily derived from the private key but not the other way around. All known (classical) algorithms to derive the private key from the public key require an astronomical amount of time to perform such a computation and are therefore not practical. However, in 1994, the mathematician Peter Shor published a quantum algorithm that can break the security assumption of the most common algorithms of asymmetric cryptography. This means that anyone with a sufficiently large quantum computer could use this algorithm to derive a private key from its corresponding public key, and thus, falsify any digital signature.

Bitcoin 101...To understand the impact of quantum computers on Bitcoin, we will start with a brief summary about how Bitcoin transactions work. Bitcoin is a decentralized system for transferring value. Unlike the banking system where it is the responsibility of a bank to provide customers with a bank account, a Bitcoin user is responsible for generating his own (random) address. By means of a simple procedure, the user's computer calculates a random Bitcoin address (related to the public key) as well as a secret (private key) that is required in order to perform transactions from this address.

Moving Bitcoins from one address to another is called a transaction. Such a transaction is similar to sending money from one bank account to another. In Bitcoin, the sender must authorize their transaction by providing a digital signature that proves they own the address where the funds are stored. Remember: someone with an operational quantum computer who has your public key could falsify this signature, and therefore potentially spend anyone’s Bitcoins!

In the Bitcoin network, the decision of which transactions are accepted into the network is ultimately left to the so called miners. Miners compete in a race to process the next batch of transactions, also called a block. Whoever wins the race, is allowed to construct the next block, awarding them new coins as they do so. Bitcoin blocks are linked to each other in a sequential manner. Together, they form a chain of blocks, also called the “blockchain”.

The victorious miner who creates a new block, is free to include whichever transaction they wish. Other miners express their agreement by building on top of blocks they agree with. In case of a disagreement, they will build on the most recently accepted block. In other words, if a rogue miner attempts to construct an invalid block, honest miners will ignore the invalid block and build on top of the most recent valid block instead.

Under certain circumstances, this would allow a private key to be derived from public information, thereby compromising funds.

A new chapter in this story has emerged with a study by researchers from Chinese universities. They calculated the resources required to solve the so-called discrete logarithm problem on 256-bit elliptic curves.

The result is striking: approximately 835 logical qubits would be required.

This estimate represents a reduction compared to previous calculations, which pointed to 1,098 or 1,175 qubits in certain configurations. For the secp256k1 curve used by Bitcoin, the researchers also arrived at a figure of 835 logical qubits, while estimating a cost of approximately 230.88 million Toffoli gates.

At first glance, reducing the resources needed for an attack seems like terrible news.

But there is another figure that completely changes the interpretation.

A potential physical barrier stands in the way of quantum computers... Physicist Tim Palmer, from the University of Oxford, is working on a formulation called Rational Quantum Mechanics. Among the implications discussed in this work is a hypothesis that is particularly relevant to large-scale quantum computing.

Investor Fred Krueger publicly linked this research to the new cryptography calculations and highlighted a potential limitation of approximately 400 coherently entangled qubits. It is precisely the difference between the two numbers that has sparked interest.

If breaking a 256-bit cryptographic curve requires around 835 logical qubits, yet there exists a fundamental physical barrier near 400 coherently entangled qubits, a quantum computer capable of executing such an attack could face an obstacle far deeper than merely scaling up its technological capacity.

In other words, it would not simply be a matter of waiting for better computers.

Physics itself could impose a limit.

This interpretation, however, hinges on important conditions. Palmer’s hypothesis does not definitively establish that no quantum system can surpass this threshold, nor does it prove in isolation that Bitcoin is permanently secure.

For now, it remains a theoretical possibility contrasted against another estimate.

Logical qubits are not the same as the qubits advertised by companies...There is yet another essential detail to understanding why these numbers can be misleading.

The 835 qubits mentioned in the study are logical qubits, not merely physical qubits.

Real-world quantum computers suffer from noise and errors. Constructing a single reliable logical qubit—protected by error-correction systems—may require many physical qubits.

This means that a machine capable of carrying out a cryptographically relevant attack would need to be far more sophisticated than a computer simply advertised as having 835 qubits.

The researchers themselves acknowledge that quantum algorithms relevant to cryptography continue to face limitations regarding hardware, error correction, and the need to maintain sufficiently low error rates.

Consequently, the scenario of a quantum computer stealing bitcoins does not appear imminent.

However, this does not mean developers are ignoring the issue.

The technical community has been exploring ways to make the protocol more resilient should cryptographically relevant computers actually emerge.

One such initiative is BIP-360, a proposal introducing a new type of output called Pay-to-Merkle-Root, or P2MR. Its goal is to reduce exposure to certain long-term quantum attacks by removing a vulnerable spending path associated with elliptic curve cryptography.

The proposal itself makes it clear that this alone would not resolve all possible attacks.

More comprehensive protection might eventually require the introduction of post-quantum signature schemes. The idea is to allow Bitcoin to evolve gradually as the actual threat level becomes clearer.

This precaution is important because there are still many unknowns.

By March 2026, a Google Quantum AI study had already reignited the discussion by indicating that the resources required to attack the encryption used by Bitcoin could be significantly lower than previous estimates suggested. Even so, there is currently no quantum machine capable of executing such an attack under real-world conditions.

The threat has not vanished, but the story has become more complex...It would be premature to declare Bitcoin definitively secure against quantum computers.

These new studies do not settle the debate; in fact, they add another layer of uncertainty.

On one hand, researchers continue to find ways to reduce the theoretical resources needed to break cryptographic systems. On the other, there are hypotheses suggesting that fundamental physical limitations could prevent quantum computers from reaching the necessary scale.

While this contest plays out in laboratories, Bitcoin developers are working on alternatives to avoid relying on a single bet regarding the future.

Perhaps this is the most important conclusion.

Bitcoin’s security in the face of quantum computing depends on more than just determining whether a machine capable of cracking its encryption might one day exist. There is also a race to modify defenses before such a machine appears.

And, following these new calculations, a threat that once seemed to hinge solely on time and technological progress has raised a far more intriguing question: what if there is a barrier that even quantum computers cannot cross?

mundophone