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

ACER

Acer releases new 16-inch laptop with 120 Hz OLED, 32 GB RAM and Intel Arc B390 graphics

Acer just announced the Vero 16 laptop, which has been designed with repairability in mind. To that end, the computer features removable port covers, a detachable external hinge and a latch on the bottom that can be opened without tools. There's even an engraving on the exterior that shows the location of the battery and motherboard. All of this combined should make service calls and installing upgrades much simpler than usual.

This looks to be an extremely capable laptop, beyond repairability. It can be outfitted with up to the Intel Core Ultra X9 processor 388H, 32GB of RAM and 512GB of SSD storage. The computer ships with up to a 16-inch 3K OLED display and comes with a nice 71Wh battery. There's a 5MP IR camera with a built-in privacy shutter, an SD card reader, an HDMI 2.1 port and a pair of Thunderbolt 4-rated USB-C ports.

It's also billed as a Copilot+ PC, so it has all of those AI tools that nobody really uses. This includes stuff like Windows Studio Effects, Cocreator and the Click to Do platform.

The Vero 16 will be available early next year, but only in Europe, the Middle East and Africa (EMEA) at launch. We don't know the price just yet, or when it's slated to come to America.

This isn't the only big Acer news of the day. The company also just refreshed its Swift series of laptops. The new Swift Air 16 is still thin, but is a bit heavier than the previous generation. It's powered by up to an Intel Core 7 processor 350 and will be available in most territories by early 2027.

The new Swift Blade 14 is an extremely light and thin laptop, clocking in at 1.76 pounds and just a half-inch thick when closed. It can also be outfitted with up to an Intel Core 7 processor 350. It'll be available for purchase in December but, again, only in EMEA.

Acer has quietly updated the options with which one of its lightweight 16-inch laptops is available. Previously restricted to the Core Ultra 9 386H, the latest Swift Go 16 can now be purchased with Arc B390 graphics via the Core Ultra X7 358H and Core Ultra X9 388H in some markets across East Asia and Southeast Asia.

A few months have passed since Acer started selling the new Swift Go 16 globally. Initially, the company claimed that it would be offering its lightweight 16-inch laptop with up to the Core Ultra 9 386H, a 16-core processor with a 4-core Xe3 iGPU. However, we have now spotted Acer selling its latest Swift Go 16 with Arc B390-backed processors.

For context, we reviewed the Swift 16 earlier this year with the Arc B390 and Core Ultra X7 358H (curr. $1,269 on Amazon). According to our benchmarks, these processors should provide Acer's 16-inch laptop with twice the GPU performance thanks to their Arc B390 iGPU. Conversely, CPU performance broadly remains unchanged from the Core Ultra 9 386H.

Acer Vero Aluminum is made from a purposeful blend of 50% post-industrial recycled aluminum and 50% low-carbon aluminum, then finished with a powder-coated surface for a refined tactile feel and a grounded, natural look.

Acer Vero 16 is designed around Acer’s circular economy approach, considering environmental impact across the full product lifecycle, from materials and manufacturing to distribution, use, and end-of-life recycling.

Intel Inside®...Your world is on your laptop. That’s why it delivers the power and efficiency to make, play, and finish it all on one charge. With performance built for mobility and seamless compatibility, your go-to apps and games always run smoothly.

Built for repairability...A more serviceable design makes key components easier to access, repair, and replace, helping extend the device’s usable life while supporting a more thoughtful approach to long-term ownership.

It's also built for longevity, with a MIL-STD-810H certified chassis that's made primarily from low-carbon aluminum. The battery and SSD are user-replaceable, which should increase the lifespan of the unit. The company promises that users will be able to upgrade with new processors "for at least two generations."

At the time of writing, a Core Ultra X7 358H-powered Swift Go 16 starts at SGD 2,499 (~$1,972) in Singapore. For reference, this configuration also includes 16 GB of RAM, a 1 TB SSD and a 1200p OLED display that outputs at 60 Hz. Alternatively, Acer offers the Core Ultra X7 358H with an 1800p OLED display, which boasts a 120 Hz refresh rate and 500 nits peak brightness.

Moreover, this SKU contains 32 GB of RAM. Unfortunately, these upgrades increase the Swift Go's price by over 30% to SGD 3,299 (~$2,605) and to HKD 15,398 in Hong Kong. Incidentally, a Core Ultra X9 388H variant costs SGD 3,599 (~$2,840) with the same display and memory as Acer's more expensive Core Ultra X7 358H SKU.

Currently, we only have found Arc B390-powered SKUs in Hong Kong and Singapore. Please note that while an apparent Best Buy US listing exists, this is actually the Swift 16 mislabelled as a Swift Go 16. At this stage, it is unclear whether Acer will offer the Swift Go 16 with the Core Ultra X7 358H or Core Ultra X9 388H in Europe, either.

 

by mundophone

TECH

Artificial Intelligence and the cognitive cost of convenience

The theme of the KES Summit 2026, held from August 25 to 27 in Trancoso, was "Conjugated Intelligences." Impeccably produced, the event brought together around 200 participants and nine speakers—André Alves and Lucas Liedke (Float), Heather Collins, Oliver Stuenkel, Alysson Muotri, Duda Franklin, Nilton Bonder, Kaká Werá, and this columnist (Dora Kaufman)—as well as a CEO panel featuring Rafaela Rezende (Decolar) and Alexandre Guerrero (Eletromídia).

Recognized as one of these conjugated intelligences, artificial intelligence (AI) permeated the entire event. Two insights stood out: the relational nature of the technology—where both ideation and implementation are co-creation processes between humans and AI—and the potential cognitive decline resulting from the intensive use of AI, exacerbated by a tendency among leaders to prioritize optimization and convenience over creative capacity. As Heather Collins put it: “AI should think with you, not for you.”

This tension between the convenience offered by AI and the cost it may impose on our ability to think and create finds empirical support in three studies conducted in distinct geographic and educational contexts, yet yielding convergent conclusions: intensive use of generative AI is associated with a reduction in critical thinking skills and learning outcomes. Analyzed together—a quantitative study with 666 participants in the UK, a longitudinal study involving over 26,000 Chinese secondary school students, and a survey on AI adoption at an elite American college—the three studies paint a coherent picture of the cognitive risks associated with the automation of reasoning.

Study 1: The AI ​​Learning Penalty in Chinese Secondary Education (June 2026, “The Generative AI Learning Penalty: Evidence from Chinese Secondary Education,” by David Stromberg, Victor Lei, and Yanhui Wu). This study features the most robust design from a causal perspective. Based on administrative data from 26,811 students in a Central Chinese county—tracked over 30 months—researchers isolated the causal effect of using generative AI. Key findings:

Productivity paradox: AI usage boosted homework grades by 18% and cut task completion time by 30% (from 64 to 45 minutes), yet led to an approximately 20% drop in performance on exams taken without AI within just six months.

Mechanism: 81% of users engaged in "homework outsourcing" by copying ready-made answers. The 20% who maintained study times comparable to non-users did not suffer significant losses—evidence that the issue lies in usage patterns, not the technology itself.

Heterogeneity: Greater losses occurred in Social Sciences (-27%) and STEM (-22%); impacts were more severe among boys and, notably, among previously high-performing students (-24% vs. -16% for low-performing students).

Study 2: AI Adoption and Equity in Higher Education (Middlebury College / IZA) (August 2025, “Generative AI in Higher Education: Evidence from an Elite College,” Zara Contractor and Germán Reyes).

The study by the IZA Institute of Labor Economics at Middlebury College does not directly measure academic performance but rather the speed at which generative AI spread and its implications for educational equity. Adoption rates surged from less than 10% in early 2023 to over 80% by late 2024. Key findings:

Near-universal adoption, though uneven across disciplines: 91.1% in Natural Sciences, compared to just 57.4% in Languages ​​and 48.6% in Literature. Demographic disparities: men use it more than women (88.7% vs. 78.4%); Black (92.3%) and Asian (91.3%) students adopt it more than White students (80.2%); students with a GPA below the median adopt it more (87.1%) than high achievers (80.3%)—a counterintuitive pattern that may indicate a risk of dependency among the most vulnerable.

Augmentation vs. Automation: 61.2% of usage cases involve "augmentation" (AI acting as an on-demand tutor to explain concepts or review texts), while 41.9% involve "automation" (replacing effort, such as writing entire essays). The authors warn that using automation under time pressure poses the greatest threat to human capital development.

Governance failure: only 10.1% of students are aware that the university offers free access to Microsoft Copilot Premium—an information gap that perpetuates inequality, as lower-income students rely on inferior free solutions.

Institutional policy dilemma: outright bans reduced reported usage by 37.8% but created a "prisoner's dilemma" that penalizes rule-followers while rewarding covert use. Only 32.6% of students know how to cite AI correctly, and 19.2% consider their course rules confusing or nonexistent.

Study 3: AI, Cognitive Offloading, and Critical Thinking (September 2025)...This is the only one of the three studies focused on the general population, combining quantitative research and qualitative interviews with 666 participants in the UK across various age groups and educational levels. Key findings:

Core correlations: the more people delegate mental tasks to external resources, the less they exercise evaluation, analysis, and inference.

Proven mediation: statistical analyses demonstrated that a significant portion of the impairment to critical thinking stems from the mechanism of "outsourcing" mental tasks to AI.

Age gradient: young people aged 17 to 25 showed greater reliance on AI and lower critical thinking scores; adults over 46 tend to choose traditional methods and maintain higher scores.

Education as a "cognitive antibody": individuals with master’s or doctoral degrees maintain analytical skepticism and verify sources even when using AI, whereas those with lower levels of education tend to accept AI responses without question.

Under different labels—"homework outsourcing" (China), "automation versus augmentation" (Middlebury), and "cognitive offloading" (Gerlich)—the three studies describe essentially the same behavior: using AI to obtain a final product without going through the mental process that fosters learning. In all three cases, the issue is not the technology itself but the pattern of use: those who turn to AI to verify, question, and complement their own judgment ("augmenters") do not suffer the losses observed among "automatizers" or "outsourcers."

Taken together, the three studies suggest that integrating generative AI into education requires an urgent recalibration of incentives: placing less weight on automatable out-of-class tasks and more on in-person, closed-book assessments; monitoring effort rather than just results; and establishing a clear pedagogical distinction between tools that teach one how to think and those that merely provide answers. The risk identified by all the authors is not the technology itself, but the possibility of an entire generation developing what one of the studies calls "cognitive debt"—an apparent productivity today that translates into a genuine inability to think, analyze, and solve complex problems in the future. This is a warning that applies equally to the classroom and to organizational processes.

Excessive reliance on artificial intelligence tools significantly reduces brain activity, recall accuracy, and critical thinking scores through a process known as cognitive offloading.

What happens to the brain:

-Reduced Neural Activation: Studies measuring EEG brain activity show that heavy AI use diminishes electrical connectivity and brain engagement compared to traditional searching or independent problem-solving.

-Weaker Memory Encoding: When an external tool generates ready-made answers, the brain skips the "productive struggle" required to encode and retain information deeply.

-Loss of Epistemic Authority: Over time, continuous outsourcing can lead to cognitive dependency, reducing a person's independent capacity to verify facts, evaluate biases, and think critically.

Preserving cognitive health:

-Use AI as a Co-Pilot: Treat AI outputs as a first draft or a brainstorming partner rather than absolute truth.

-Embrace Productive Struggle: Attempt to solve problems, outline arguments, or retrieve facts from memory before turning to automated tools.

-Verify and Cross-Check: Actively research multiple primary sources to maintain analytical reasoning skills

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