TECH

'AI is incompatible with democracy,' says author of book on OpenAI

One aspect that has surrounded the field of artificial intelligence (AI) since the ChatGPT boom is the aura of "magical technology"—something that emerged almost spontaneously and carries promises and concerns as grand and inevitable as anything science fiction could imagine.

That is not how American journalist Karen Hao views the field. To her, it is a project with the hallmarks of an empire, consuming global resources on a massive scale to benefit a tiny elite in Silicon Valley, USA. Her research led her to conclude that AI is incompatible with democracy—a position she argued in a book that garnered significant attention last year.

She is the author of *The AI ​​Empire* (originally titled *The AI ​​Empire* in English), which details the history of the company co-founded by its current CEO, Sam Altman. A Portuguese-language edition of the book was recently released.

Her perspective stems from a unique vantage point: as the former AI editor for *MIT Technology Review* and a reporter for *The Wall Street Journal*, Hao closely followed the emergence and rise of OpenAI. In just a few years, the organization transformed from a non-profit laboratory into a company currently pursuing a $1 trillion IPO.

The book maps out the key figures, companies, technologies, dilemmas, and issues in the field. It serves as a guide to understanding how a technology that once seemed like the stuff of movies became a major driver of political, economic, cultural, and behavioral influence in recent decades.

Hao is currently in Brazil. On Tuesday (the 30th), she is participating in an event leading up to the 21st International Investigative Journalism Congress, organized by the Brazilian Association of Investigative Journalism (Abraji). The talk begins at 4:00 PM at the Camargo Guarnieri Cultural Center on the University of São Paulo (USP) campus. The event is hosted by Abraji, Editora Rocco, and GEIA (the Research Group on AI and Digital Cultures) from USP’s School of Communications and Arts. Yesterday, she met with *O Globo* at a hotel in São Paulo’s West Zone and, in addition to discussing her book, addressed some of the latest topics in the world of AI. These included data center regulations, OpenAI’s market position, Anthropic’s growing strength, the US government’s ban on advanced models, the papal encyclical, the lack of diversity in AI research, and the global influence wielded by Silicon Valley companies.

https://karendhao.com/

Read the highlights of the conversation below.

Throughout the book, Sam Altman and those around him speak as if everything regarding AI is inevitable, yet you counter that view. Is there anything about AI that is truly inevitable?

I don’t think anything is inevitable in general, but when it comes to AI, one of the things I really tried to highlight in the book is the extent to which every decision regarding ChatGPT was based on completely subjective choices. And it’s interesting when you look at OpenAI’s beginnings.

The approach they took to scaling their LLMs was seen as scientifically anomalous at the time. They adopted a brute-force, intellectually "cheap" approach that wasn't the path favored by other researchers in the field. And it was partly because they had an extraordinary amount of money that they managed to make their approach the dominant one. So, how can you say that a technology born from that kind of history is somehow inevitable?

In what ways does AI undermine democracy?

I call these companies—like OpenAI—"AI empires" because of the striking parallels they share with the empires of the past and the way they amass extraordinary economic and political power.

This happens through the dispossession of the majority. They dispossess people of their data, their land—to host these data centers—, water resources—to power and cool these centers—, as well as people's labor, future economic opportunities, and educational opportunities. And that is why the empire manages to extract an extraordinary amount of value so quickly: it extracts it without distributing it back proportionally. And the reason I believe this threatens democracy is that empires and democracy are incompatible.

Empire is founded on the idea that there is a natural hierarchy in the world—that there are superior and inferior groups—and that those at the top deserve to be there and deserve to appropriate all those resources by virtue of some divine right or natural order. Democracy, on the other hand, is based on the exact opposite premise: the idea that we are all equal and all deserve to participate collectively in determining our own future. Thus, at a purely philosophical level, there is a fundamental conflict between the ideology driving the development of the AI ​​industry and the way democratic societies are organized.

Brazil is working on legislation to attract data centers. By offering cheap renewable energy and tax incentives, the country is positioning itself as a “digital colony,” as you describe in your book. What can Brazil learn from countries like Chile, which have both welcomed and rejected data centers?

One notable trend we’ve seen over the past year is the rise of resistance to data centers worldwide. It started in Latin America—in places like Chile—and spread to the US, Europe, and Brazil. This kind of grassroots organizing is really beginning to pressure the AI ​​industry to change its approach.

For instance, OpenAI shelved Sora (its AI video generator). When they announced the product, they billed it as the most important launch since ChatGPT, yet within a few months, they had to shelve it. Grassroots organizing was the reason why. If you look at the three reasons reported for OpenAI’s decision, the first was a massive bottleneck in computing power.

The second reason was stagnant consumer demand—so, this is a case of collective consumer action. The third is that OpenAI is preparing for an IPO and facing a much more uncertain financial landscape. Wall Street is increasingly skeptical about whether the AI ​​industry can actually deliver on its promises, given the massive political and social backlash currently underway.

This is where Brazil can gain insight: by recognizing that when this kind of grassroots organizing and resistance emerges—whether against the infrastructure, the way they harvest data or intellectual property, or the psychological harm inflicted on children—it impacts the trajectory of AI development.

The US government is making it very clear that it decides who gets access to cutting-edge technology—as seen with Claude Fable. What should countries do to preserve their sovereignty while still keeping pace with the latest advancements?

There is a major question here regarding why we actually want to keep up with the latest technologies. If these latest technologies are the same ones exploiting and extracting resources from communities around the world, is it really a good thing to keep up with them? Or should we, in fact, reframe the problem regarding the rules of the game? If we were to redefine our goals—not just to chase the latest tech, but to pursue the objectives of individual communities, such as improving the cost of living, the quality of education and healthcare, and economic opportunities—you would quickly realize that we don't need any of the AI ​​technologies Silicon Valley is trying to force down people's throats.

There is a completely different set of AI technologies we should actually be developing. And there are many ways to develop these technologies without engaging in Silicon Valley's exploitative practices. This would help communities continue to progress in the true sense of the word—not just technological progress for its own sake, but human, social, and economic progress.

Right now, OpenAI is squeezed between Anthropic—which currently has the most popular platform—and SpaceX, which potentially has the capacity to build its own infrastructure. These are two things I don't see happening for OpenAI. Will the empire fall?

If we define the "AI empire" solely as OpenAI, then yes. There is a lot of pressure on OpenAI right now, and it doesn't seem to be in a very strong position. But I think the more important question is: will the AI ​​empires—plural—fall? I am actually quite hopeful about this, because I define OpenAI, Anthropic, SpaceX, Amazon, Microsoft, Google, and Meta all as AI empires. Having OpenAI cease to be an empire only for Anthropic to take its place wouldn't actually solve the core problem I see regarding the destructive and exploitative nature of AI development. What I hope happens is not merely that we keep swapping which entity acts as the dominant empire, but rather that—through grassroots organizing and resistance—we secure genuine accountability from all these empires. I want them to stop being empires and instead become companies that offer value commensurate with what they receive in return. My goal is not to put these companies out of business; it is simply to bring them back to a role where they are not excessively exploitative and do not degrade the environment. We can have companies that provide high-quality products and services without causing extraordinary amounts of damage.

This approach is incredibly circular and baffling, because when you look at what Anthropic is doing, it’s practically the same thing as OpenAI. Dario Amodei and the Anthropic executives left OpenAI, essentially copied and pasted their model, and simply rebranded themselves as the "good guys" without actually addressing the root causes of the problems. Both OpenAI and Anthropic approach AI through scaling, so you still run into the same issues regarding data privacy violations, the erosion of intellectual property, environmental damage, and harm to public health. They are essentially making minor tweaks and claiming moral superiority, when in reality, they are just another empire.

What do you make of the fact that the Pope relied on someone from Anthropic to help present his encyclical, *Magnifica Humanitas*? Leo XIV warned about the threat AI poses to human dignity, justice, and labor, yet he had a representative from one of these companies by his side.

That was a very confusing moment for me, because I found the encyclical to be an incredibly profound and beautifully written document. It discusses how AI represents a new phase of colonialism and perpetuates labor exploitation—potentially giving rise to new forms of slavery—and addresses how the AI ​​industry advances based on an ideology that assumes machines will always be superior to humans and will somehow perfect the flaws of the human species. One of my favorite lines says, "We flourish within our limitations, not despite them." Yet there was Chris Olah, an Anthropic executive, standing right alongside the Pope.

At first, I was quite disappointed and thought, "Okay, so even the Vatican—the Catholic Church—has, in a way, surrendered to these companies." But when Olah spoke, he framed the document as a critique. He didn't say, "We fully support this document." In fact, he said, "We need critics like the Pope to hold companies like Anthropic accountable." So, he framed it as an adversarial dynamic.

On one hand, Anthropic is trying to draw the Vatican closer to its sphere of power and influence, but at the same time, the Vatican is trying to do the same by keeping the AI ​​industry in check. It isn't entirely clear who actually gained more from this arrangement. Was it the AI ​​industry, or was it the Pope? In the end, perhaps the Pope managed to have the final word.

Is Artificial General Intelligence (AGI) a lie? Why isn't it discussed with the same intensity anymore?

It is a myth, in the sense that it is an incredibly compelling story that many people believe in—and one that serves a huge political purpose for these companies to justify all the destruction they cause. If they can get everyone to believe that AGI somehow exists, then they can simply go on doing whatever they want. But reality has set in, and myths only work in information vacuums. The more AI is deployed in society, and the more communities suffer the impact of its development, the more that information vacuum gets filled with actual facts about what the technology really represents—and the more that myth begins to crumble.

You argue that the scientific field of AI has lost transparency and research diversity with the rise of generative AI. How can that be regained?

The reason so much diversity has collapsed is that the AI ​​industry has become the dominant funder, even outside of the companies themselves. They are the primary funders of academic labs, while state funding accounts for a smaller share of the total.

Changing this requires a few things. One is having people with alternative visions for AI development who refuse to accept funding from these companies. New sources of funding will also be required, potentially combining state funding, foundation support, and perhaps even crowdfunding. A richer constellation of startups will also be needed. This will take time.

Restoring the diversity that was lost will require a great deal of careful effort and sacrifice, as AI researchers and other talented individuals will need to make an active choice early in their careers: forgo a $1 million compensation package to invest in a different approach to AI development.

Is there a specific area of ​​AI where you would like to see more research—beyond deep learning, machine learning, and generative AI?

Before deep learning became the primary focus of virtually all AI development, there was a field known as neuro-symbolic AI. This approach involved encoding knowledge and databases into computer systems to create a more deterministic system—one that could retrieve that knowledge and reason through the database to arrive at specific answers. While that approach had its weaknesses and was eventually sidelined due to being too slow and costly, the neuro-symbolic school of thought merges the strengths of deep learning with those of the symbolic approach. It allows a system to learn quickly from data—as deep learning does—while also incorporating fundamental rules that do not need to be learned. We already know that 1 plus 1 equals 2, so there is no need to feed the system vast amounts of data demonstrating this fact. That is part of the reason why deep learning systems are so inefficient; they essentially reinvent the wheel every time. So, I am interested in seeing more work on neuro-symbolic AI.

At the same time, within the realm of deep learning, there were other interesting avenues for making systems more efficient—reducing their consumption of data and computing power. I believe there is significant research to be done both within and outside the current paradigm to explore the new techniques and methodologies we can use to achieve the desired capabilities without relying on an extractive supply chain.

Finally, I would add that the issue isn't just how we achieve better systems, but also how we define the ultimate goal. For some time now, the industry has defined that goal as replicating human intelligence. I don’t think that’s the right goal. The aim of technological development is to complement what we cannot do, not to replace what we can do. When we got the first computers and calculators, part of why that was great was that humans can’t calculate numbers as fast as computers can. So, we offloaded that work to the computer, but there are so many other things humans can do that we would never be able to offload to a machine. Why not focus solely on developing AI systems geared toward the things we could never do ourselves, rather than trying to outdo us in every way?

Do you use AI tools? Which ones, and for what purpose?

I don’t use commercial generative AI tools. I don’t use ChatGPT, Claude, Gemini—none of them—and there are three reasons why. First, because I investigate these companies, so from an ethical standpoint, I don’t want to participate in perpetuating the harmful practices they engage in. Second, for privacy reasons. I investigate these companies, so I don’t want to hand over all my data to them. And third, because I believe that, ultimately, the strengths of my work are simply incompatible with what I would get from a generative AI tool.

However, I do use specialized AI tools. For example, one of the things I wanted to do with my book was detail how OpenAI became better capitalized after shifting from a non-profit organization to a Microsoft-funded venture, and I noticed a huge improvement in their office furnishings.

The office chairs I saw at the first location were simply very different from the ones at the next office. So, I took photos of each chair and ran them through Google Image Search—a specialized AI tool that doesn't try to generate anything and doesn't consume vast resources to perform the task. I discovered that the chairs from the old office actually cost $2,000 each, while the chairs in the newer office were by a famous Brazilian designer and cost $10,000 apiece. I included this in the book, as I felt it helped illustrate the point.

--o Globo--

DIGITAL LIFE

Humans trained to spot AI faces in the battle against deepfake fraud

Humans have been successfully trained to spot AI-generated faces in a study led by researchers at the Australian National University (ANU) Emotions and Faces Lab.

AI-generated deepfake faces have become so realistic that it is difficult for people to tell them apart from photos of real humans, contributing to increases in AI-related fraud.

“Training on visual artifacts, like looking for a sixth finger or odd earrings, has had limited success, partly because the AI is getting too good, and fraudsters may avoid using pictures with obvious flaws anyway,” lead researcher Associate Professor Amy Dawel said.

“Our training directs people’s attention to global qualities that differ between AI and human faces. AI faces tend to be more symmetrical, proportional and attractive, but without training we often think these are markers of being human.”

The researchers trained people to spot AI-generated faces by drawing their attention to six perceptual qualities: distinctiveness, memorability, proportionality, symmetry, attractiveness and expressiveness.

The ability of all participants to spot AI faces improved, with “high performers” achieving near perfection.

“It was amazing to see the dramatic improvement in people’s ability to detect AI faces,’’ Associate Professor Dawel said.

“We've shown our training is effective for some of the most convincing fakes available, StyleGAN faces. Now we need to find out whether that training generalises to other AI-generated faces.

“We are also working on how to optimise the training – making it shorter and ensuring the benefits last over time.’’

The participants in the main study were trained by ANU Honours student Tanya George.

“We found that even relatively short training sessions helped participants improve their accuracy in detecting AI-generated faces, highlighting the potential for practical education tools in this area,’’ Ms George said.

“AI image-generation technology is improving extremely quickly, and many people underestimate how convincing these faces can be. Research like this can help people navigate increasingly complex online environments.”

The research was successfully replicated by a team led by Professor Jim Tanaka and Dr Eric Mah at the University of Victoria, Canada.

“The replication shows that the findings weren’t a fluke – when we trained a new set of people in a different country, we saw them improve just as much,” Dr Mah said.

“Online training was effective, so our training program could easily be implemented at scale for little cost.”

Associate Professor Dawel said it was important to improve human AI-detection abilities because AI could not be relied upon to solve the problem alone.

"While algorithms offer one solution to detecting deepfake faces, their decision-making processes remain opaque and recent benchmarking reveals serious weaknesses,'' she said.

“We need approaches that are ethical and explainable – for which keeping humans in the loop is key.”

The ANU Emotions and Faces Lab would like to hear from people interested in undertaking the AI face detection training or participating in other AI face studies. People can register to participate at: https://tinyurl.com/ai-face-study-register

The study, Training Humans to Detect AI-generated Faces, is published in the scientific journal PNAS. 

Humans trained to spot AI faces...Recent groundbreaking scientific studies confirm that humans can be effectively trained to spot hyper-realistic AI-generated faces in less than an hour, nearly doubling their detection accuracy. Historically, people relied on looking for localized glitch artifacts—like asymmetric earrings, background bleeding, or distorted teeth. 

However, modern generative AI software has largely eliminated these errors, triggering a dangerous phenomenon known as "AI hyperrealism," where people mistake synthetic faces for real human beings.A peer-reviewed study published in the journal PNAS by researchers at the Australian National University and replicated by the University of Victoria proved that teaching people to evaluate structural facial impressions can reliably combat deepfake fraud.

Instead of searching for tiny pixel mistakes, successful training program frameworks direct human vision toward six holistic perceptual qualities that distinguish real humans from synthetic counterparts:

Symmetry: AI faces generated by modern networks are often structurally hyper-symmetrical, which rarely happens in nature.

Proportionality: Generative models tend to deliver flawless, mathematically average distance intervals between facial landmarks.

Attractiveness: Synthetic profiles are generally optimized to look highly attractive and universally appealing.

Expressiveness: Real human faces communicate micro-emotions; AI outputs often look rigid or slightly devoid of raw feeling.

Distinctiveness: Human faces have unique, non-average irregularities, whereas fake profiles tend to appear "hyper-average".

Memorability: Because synthetic generation aggregates standard database data, fakes can leave a less memorable psychological impression.

source: The Australian National University

HMD

Leak reveals innovative feature phone with touchpad

HMD Global appears to be developing a brand-new feature phone that is not only expected to offer a modern design but, above all, an innovative keypad that can be transformed into a touchpad to simplify the use of certain apps.

With phones like the Nokia 110 4G, HMD Global has been offering feature phones with a classic design for years – devices that are particularly popular as secondary phones without distracting social media apps. The leaker @smashx_60, who has frequently revealed accurate information in advance about unreleased HMD Global products over the past few years, has now published the first images of a next-generation HMD feature phone.

The device stands out not least for its heavily rounded body. The right side features a power button and a volume rocker. On the back, there is a single camera and an LED flash. Pogo pins should allow the device to be charged on a wireless charging station. The front features an almost square display, which is relatively large by feature phone standards. Below the screen is a keypad with a T9 layout.

The standout feature of this model is that this keypad can be transformed into a touchpad when needed, though the leaker is not yet certain whether HMD Global will use a flip-out or removable cover for this purpose. This touchpad should make scrolling through long texts and web pages much more comfortable, since it prevents a finger from covering the already quite small touchscreen. So far, it is not known when or at what price this feature phone will be released, and the model name has not yet been announced.

The most intriguing feature is a touch-enabled flip cover, which would transform the device into a hybrid model. The concept envisions a phone that is simple for daily use yet offers access to cloud services and browser-based apps via an RTOS Touch platform.

The released sketches show a phone with soft lines that strongly resemble historic BlackBerry models designed for users who preferred physical keys. The front houses a landscape-oriented display paired with a T9 numeric keypad—a configuration that prioritizes the convenience of calling and texting over traditional smartphone functions.

Rounded design with clear echoes of old BlackBerry phones.

Landscape-oriented screen rather than the classic vertical panel.

Physical T9 keypad for typing and quick commands.

The detail that truly sets this HMD device apart is the potential inclusion of a touch-enabled flip cover. Essentially, the phone would combine the immediacy of a feature phone with more modern interaction, creating a hybrid format that offers some advanced functions without becoming a full-fledged smartphone.

The flip section of the device is expected to support touch controls.

It is slated to run on RTOS Touch, a lightweight platform designed for basic usage.

Applications could be delivered via a cloud-phone service providing access to browser-based tools.

mundophone

 

TECH

Chinese cars are arriving so rapidly that European logistics are beginning to feel the strain...Why is this both a good and a bad thing?

For years, Europe has sought to accelerate the adoption of electric vehicles through incentives, environmental targets, and openness to new brands. This strategy helped boost competition and drive down prices. However, a curious phenomenon is now capturing the automotive industry's attention: the success of this Chinese expansion is creating bottlenecks that extend far beyond dealerships and factories. The impact is already visible at some of the continent's largest ports.

The presence of Chinese automakers in Europe has grown impressively in recent years. Companies such as BYD, Chery, MG, Omoda, Jaecoo, Great Wall Motor, and Changan have accelerated their entry into various European markets with a combination that is hard to ignore: competitive prices, advanced onboard technology, and a growing lineup of electric and hybrid models.

The results of this strategy are not limited to sales figures; they are also evident at the ports receiving thousands of vehicles from Asia.

Ships arrive laden with cars, but distribution to dealerships and logistics centers does not always keep pace. In some instances, areas intended merely as transit points are turning into massive temporary storage yards.

The issue goes beyond the sheer volume of vehicles. European logistics systems are already facing significant constraints, including a shortage of specialized truck drivers, limitations in rail transport, and difficulties in rapidly expanding the infrastructure needed to handle such large numbers of units.

Furthermore, many Chinese brands are still in the process of building their networks for dealerships, service centers, parts distribution, and after-sales support. Yet, thousands of cars have already arrived on the continent while this infrastructure is still being established.

This creates an inevitable bottleneck. While China is capable of producing and exporting on a massive scale, absorbing that volume depends on a logistics chain that takes time to adapt.

The Port of Barcelona stands out as one of the most emblematic examples. Its strategic location makes it a key gateway for vehicles destined not only for Spain but also for other countries in Southern Europe, the Mediterranean region, and even North Africa.

Activity has been so intense that new investments have begun to emerge. A highlight is the infrastructure expansion undertaken by Japan’s NYK, featuring a new facility designed to significantly boost vehicle handling capacity.

The message behind this investment is clear: Barcelona aims to establish itself as a major logistics hub for distributing Asian automobiles across Europe.

However, the situation extends beyond Spain. Other major European ports are also seeing a steady rise in the flow of vehicles imported from China. This demonstrates that China's expansion has moved past the experimental stage and is now taking place on a massive scale.

At the same time, there is a significant reason driving this push. China’s domestic market is facing extremely aggressive competition. With numerous manufacturers vying for market share, shrinking margins, and excess production capacity, exporting has become a strategic necessity.

Europe stands out as a natural destination, offering consumers with high purchasing power and a growing demand for electrified vehicles.

The true barometer of the global automotive contest...The battle between European and Chinese manufacturers is often framed in terms of price, technology, and innovation. Yet, ports are revealing a less visible side of this rivalry.

A car sitting in a port area represents a cost. It takes up space, requires logistical management, and reduces capacity for incoming shipments. The greater the accumulated volume, the greater the operational challenge.

Consequently, the success of the Chinese strategy also entails risks. Simply manufacturing good vehicles and shipping them to Europe is not enough; it is essential to build a comprehensive infrastructure capable of transporting, selling, financing, and supporting thousands of customers.

The current situation reveals a curious reversal of roles. For decades, the challenge was producing enough vehicles to meet global demand. Now, in some cases, the problem appears to be exactly the opposite: finding the space and logistical capacity to handle a volume of cars arriving faster than they can be distributed.

This is the new reality for the automotive industry. The battle is not playing out solely in factories or dealerships; it is also being waged at ports, on railways, on trucks, and at distribution centers.

And the logistical bottlenecks now emerging send a clear message: China’s expansion into Europe has moved faster than many had anticipated.

Europe seeks to shield itself from the 'flood' of Chinese products... In April, the trade deficit with China surpassed the €30 billion mark—a level deemed unsustainable by the European Commission itself, which is proposing to equip the Union with tools to rebalance its trade relationship with Beijing.

Among the measures under consideration is a mechanism that would exclude certain products from European public procurement markets and limit the acquisition of European companies by Chinese groups. France, in particular, advocates for the creation of a European equivalent to the United States' "Section 301," which allows for the imposition of targeted surcharges on products from countries accused of unfair trade practices.

"We must adopt defensive measures," argued French President Emmanuel Macron, asserting that Europeans have the right to react "when our sovereignty is at stake."

Trade war...Since 2024, with the imposition of additional tariffs on Chinese electric cars, the trade relationship between the EU and China has become an extremely sensitive issue. Some European countries fear the onset of a trade war with uncertain consequences for the EU.

Following the European surcharges on Chinese electric vehicles, Beijing retaliated by targeting sectors such as cognac, pork production, and European dairy products.

Another major concern is the EU's heavy reliance on China, which controls rare earth elements and strategic raw materials essential to high-tech industries. Restrictions imposed by Beijing last year on certain exports served as a wake-up call for Europeans.

"This shows just how important it is to diversify our sources of supply," declared Ursula von der Leyen during the G7 summit in Évian.

mundophone

TECH

Affordable Raspberry Pi 4 Model B surfaces with binned CPU

You'll recognise the price along with the basic shape and size, so you can simply drop your new Raspberry Pi into your old projects for an upgrade; and as always, we've kept all our software backwards-compatible, so what you create on a Raspberry Pi 4 will work on any older models you own too.

The speed and performance of the new Raspberry Pi 4 is a step up from earlier models. For the first time, we've built a complete desktop experience. Whether you're editing documents, browsing the web with a bunch of tabs open, juggling spreadsheets or drafting a presentation, you'll find the experience smooth and very recognisable — but on a smaller, more energy-efficient and much more cost-effective machine.

The Raspberry Pi 4 Model B originally launched in 2019, featuring a CPU with support for a 1.8GHz clock speed. Cytron, an official reseller, is offering a "special version" of the SBC with a binned chip, and it's more affordable.

There's a new version of the Raspberry Pi 4 Model B available. This one costs less than the regular SBC variant, but it's not exactly the same. Specifically, it has a binned chipset, which runs slower than the CPU found in the standard edition.

As Cytron, the reseller offering the affordable Raspberry Pi 4 Model B, notes, the Broadcom BCM2711 of this affordable SBC runs at 1.25GHz. To look back, the original variant, launched in 2019, had its CPU clocked at 1.5GHz, but with a software update, it got support for a 1.8GHz clock speed.

The reseller further highlights that these binned Pi 4 Model B are sourced directly from Raspberry Pi, but they "don't meet the peak 1.8GHz specifications." Instead, they are said to run "flawlessly" with the CPU frequency set at up to "1.25GHz."

This downgrade in the clock speed shouldn't be a big issue for most applications. If anything, the binned Pi 4 Model B would be a little slower than the standard unit. Of course, there can be a notable difference in benchmarks and heavy workloads (GeeekPi aluminum heatsink for the SBC curr. $11.99 on Amazon).

Regarding how affordable the binned version is, there are two configurations available, and the one with 4GB of RAM costs $87.25. To compare, the regular SBC with the same memory goes for $110 on Cytron, while the 8GB configuration costs $181.50.

The binned 8GB version, on the other hand, costs $147. So, these newly surfaced configurations would be good picks for projects with very tight budgets.

mundophone

TECH

Dog-bone design helps 2D nanoribbon transistors stay fast and efficient as widths shrink

Transistors, small semiconductor-based switches that control the flow of electricity, are central components of all electronic devices, from computers to smartphones, wearables, sensors and smart appliances. Over the past decades, electronics engineers have been continuously working to boost the speed and performance of transistors while also reducing their size.

A promising approach for miniaturizing transistors entails the use of two-dimensional (2D) semiconductors, materials that are only one or a few atoms thick. Despite their potential, most high-performing 2D transistors have so far been demonstrated using relatively wide channels, and it has remained unclear whether their performance can be preserved when the channels are made much narrower.

Researchers at Stanford University recently developed new compact transistors based on narrow strips of monolayer 2D semiconducting materials known as nanoribbons. These transistors, introduced in a paper published in Nature Nanotechnology, were found to perform remarkably well despite their small size, outperforming previously developed nanoribbon transistors based on the same 2D materials.

"We wanted to reduce 2D transistors in all dimensions, including width," Eric Pop, senior author of the paper, told Tecplor. "Using a monolayer 2D semiconductor, the channel is automatically sub-nanometer thin, but to be technologically relevant, such transistors should also be very small in both length and width."

The team's recent study specifically focused on reliably reducing the transistors' width without significantly affecting their performance.

"Most academic works have looked at the 2D channel's thickness and length, which motivated us to systematically study width scaling in these materials and their devices," said Tara Peña, co-first author of the paper.

To prevent nanoribbon delamination (i.e., peeling from the surface during fabrication), the researchers employed a new approach, patterning 2D semiconductors into a dog bone-like shape. Metal contacts were integrated on the wider regions of this bone-like pattern, acting as anchors.

"This approach allowed us to study many nanoribbon channels as the narrow part of the dog bone, for several 2D semiconductors," said Pop. "In future industry use cases, the anchoring of nanoribbons will need to be achieved in a more compact way."

Using their approach, the researchers fabricated nanoribbon transistors based on three different monolayer 2D semiconductors, namely MoS2, WS2 and WSe2. Electrical measurements showed that the narrow nanoribbon channels retained good transistor behavior across all three materials.

"Importantly, the nanoribbons all behaved well with our nanofabrication approach at dimensions down to about 25 nanometers, including both n- (MoS2, WS2) and p-type behavior (WSe2)," explained Pop.

"This means that the edges are not fundamentally limiting the performance of these materials, and the edges could be further improved. The WS2 transistors were also able to carry about one hundred times higher current density than previous demonstrations, partly due to our improved contacts."

When they tested their newly developed transistors, Pop and his colleagues were surprised to discover that they did not show higher off-state leakage than wider transistors based on the same 2D materials. This suggests that despite their reduced width, the edges of the nanoribbons did not cause excess leakage, which is important for low-power operation.

"Another important part of our approach to reach the narrowest widths was how we etched the transistor channels," said Anton Persson, co-first author of the paper. "Instead of etching the channel in one step, we used two separate etching steps, which etched the channel from opposite sides. This allowed us to form narrower channels than with the conventional one-step approach."

The transistors developed by the researchers achieved good on-state currents of 560 µA/µm for n-type MoS2, 420 µA/µm for n-type WS2 and 130 µA/µm for p-type WSe2. Notably, all three transistors performed better than most other nanoribbon 2D transistors introduced in the past.

"We found that nothing dramatic happens when the transistors become very narrow," said Persson. "We were concerned that the etched-out edges of these semiconductors would cause problems, but the devices still behaved well or at least similarly to their wider counterparts. This suggests that these monolayer 2D semiconductor channels are relatively robust when scaled down in width."

Future research directions...The design and fabrication strategies introduced by this research team could soon be refined further and used to create other electronic components based on 2D semiconductors. This study demonstrated the potential of these strategies for realizing extremely small devices.

"The 'dog-bone' design and multi-step etching approaches both helped with adhesion and achieving narrower widths," said Peña.

"We also believe reducing electron beam dose and polymer contamination during the fabrication process allowed us to obtain 'cleaner' edges for our 2D nanoribbons. We hope our work will inspire other groups (and industry) to think carefully about how to limit sources of disorder that ultimately impact 2D device performance."

Pop and his colleagues are now planning further studies aimed at evaluating their proposed design and patterning approach. They will also try to realize nanoribbons that can operate at lower voltages, with improved edges and smaller contacts.

"For example, here we showed pretty good behavior is possible at 1 V drain-to-source voltage," explained Pop. "It will be important to achieve good behavior at 0.5 V on both the drain and gate, in order for these to be considered viable alternatives to silicon nanosheet transistors."

As part of their next studies, the researchers are curious to determine how far their downscaling approach can go before the performance of transistors starts declining.

"We expect that the devices will eventually start to degrade as the channels become even narrower, but we do not yet know at what width that happens," said Persson. "Understanding what happens below 10 nanometers in width will eventually be important if 2D semiconductors are to be compared seriously with future silicon nanosheets."

"Understanding how various strains and defects impact these ultra-scaled 2D nanoribbons will also be critical, which will require sophisticated materials characterization approaches," added Peña.

2D nanoribbons are ultra-narrow, strip-like structures of two-dimensional (2D) materials (like graphene, transition-metal dichalcogenides, or boron nitride) with widths typically scaled to under 50 nanometers. By restricting 2D sheets into 1D-like ribbons, researchers unlock tunable bandgaps, high charge mobility, and superior quantum edge effects.

Why 2D nanoribbons matter:

-Bandgap engineering: Unlike pristine 2D sheets (such as zero-bandgap graphene), nanoribbons exhibit tunable electronic bandgaps, which are strictly dictated by their precise width and edge orientation.

-Enhanced performance: Nanoribbon architectures offer highly exposed active edges that facilitate rapid electron transport and improved charge mobility, making them ideal for scaling down semiconductor logic devices.

-Quantum confinement: The extremely narrow channels lead to quantum effects, giving rise to unique magnetic behaviors, spin-filtering capabilities, and potential applications in quantum computing.

Key applications:

-Next-generation nanoelectronics: Nanoribbons are highly promising for the creation of ultra-compact Field Effect Transistors (FETs). For instance, researchers have developed high-speed, 35 nm channel nanoribbon transistors using monolayer 2D semiconductors to maintain performance despite shrinking dimensions.

-Energy storage & catalysis: The high surface-area-to-volume ratio and edge activity allow 2D nanoribbons (like those derived from unzipped carbon nanotubes) to act as excellent catalysts for hydrogen evolution, oxygen reduction, and energy storage.

-Optoelectronics: Their tunable optical and electrical properties make them prime candidates for advanced solar cells, sensors, and photodetectors.

Common types:

-Graphene nanoribbons (GNRs): The most well-studied type, often created using bottom-up molecular polymerization or unzipping of carbon nanotubes. They are prized for ballistic electron transport.

-Transition-metal dichalcogenide (TMD) nanoribbons: Materials like MoS₂ are naturally semiconducting and thin, making them optimal for extreme miniaturization in modern computing without the leakage currents found in traditional silicon.

-Boron nitride nanoribbons (BNNRs): Structurally similar to graphene but with a large bandgap, often used as an insulating dielectric layer or combined with graphene in heterostructures.

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DIGITAL LIFE

OECD warns of AI-induced “cognitive laziness”

Over-reliance on generative models may impair human judgment in the long run, according to new OECD data pointing to a pattern of AI-induced cognitive laziness. The document indicates that repeatedly delegating analytical tasks to automated systems can weaken autonomous intellectual skills and diminish critical capacity when these tools are unavailable.

Picture the scene. You’re behind the wheel in an unfamiliar city. Twenty years ago, a crumpled map lay on the passenger seat, eyes shuttling between paper and road, the mind charting streets, landmarks, intersections. Today, a soft, synthetic, almost reassuring voice takes care of everything. Attention drifts off, memory dissolves, and thought becomes a simple act of obedience. The comfort is total, almost anesthetic.

In the same way, when a question arises, there’s no need to search, to doubt, to build an answer step by step. You simply pose it to a machine. Within seconds, an artificial intelligence delivers a clear, well-argued, polished synthesis. The slowness of reasoning has vanished, replaced by immediate efficiency.

We now live in a world of universal assistance, a world where everything seems fluid, rational, effortless. These tools promise to free us from the burden of tedious tasks, to augment our capabilities, to make life simpler. Yet behind this technological comfort lurks a discreet paradox: as we delegate our cognitive faculties, we risk losing the ability to use them. By being constantly assisted, we cease to exercise what made us unique: autonomous thought.

It’s this slow drift—what I call cognitive laziness—that I’d like to discuss today. A laziness born not from disinterest, but from delegation. That of a mind that gradually surrenders to the machine to sort, choose, decide. The temptation of shortcuts becomes permanent, the fatigue of thinking disappears, and with it, the effort that forged lucidity.

Students who use digital assistants for academic assignments show a drop in performance on analog assessments. The OECD’s *Digital Education Outlook 2026* report reveals that introducing these platforms into the educational ecosystem creates an illusion of technical proficiency. Statistical data collected by the international organization suggests that the initial quality of schoolwork produced with computational support does not translate into retained knowledge.

In-person exams conducted without network access showed a reversal in grades for students who rely on automation. Removing intellectual friction during the research and writing phases hinders the consolidation of concepts in long-term memory. The OECD warns of the risk of widespread intellectual disengagement in educational institutions if language models continue to be integrated without criteria for maintaining analog-based analysis.

The illusion of competence in the professional environment... A decline in autonomous judgment is also becoming apparent in the corporate world—a phenomenon researchers liken to the loss of spatial awareness caused by the systematic use of GPS. A study cited by Harvard Business School measured the performance of professionals assisted by generative models. The results show that immediate gains in speed can come at the cost of quality or rigor in certain tasks, particularly when users encounter problems that lie beyond the AI's capabilities.

Professionals using the technology completed tasks 25.1% faster. However, when dealing with complex problems outside their specific area of ​​expertise, users showed a 19-percentage-point higher likelihood of making serious errors. The study further suggests that many employees accept incorrect machine-generated answers without verification, reinforcing the risk of over-reliance on AI in learning and operational oversight.

The loss of critical friction also affects system development and software coding. Research from Stanford University focusing on the use of autonomous computational assistants found that automation reduces the natural skepticism of technical operators. The study quantified output quality and found that professionals assisted by algorithms produce solutions with a higher rate of structural security flaws.

The research highlights a specific cognitive bias in which the user assumes automated work is correct due to the tool's seamless interface. This dynamic eliminates traditional manual validation processes, leading to an accumulation of logical errors that overwhelms senior professionals during the auditing phase. Reliance on generative models alters work structures, necessitating the reintroduction of verification methodologies based on primary sources.

Data presented by the OECD and academic institutions demonstrate that digital tools should serve to amplify, not replace, human intellect. Mitigating skill erosion requires creating artificial barriers that force a period of mental deliberation before resorting to algorithms. The sustainability of technological evolution depends on organizations' ability to preserve critical thinking as a strategic asset that cannot be entirely delegated to automation.

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