powerful

We thought we had an AI bubble. There are powerful arguments that indicate that we were wrong

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You either love AI or hate it. Either you are a (deluded?) optimist, or you are on the bandwagon of skeptics and bet due to an imminent puncture of that AI bubble that everyone talks about. The well-known analyst Ben Thompson has been in the second group for some time and stated that in fact we were in a “good” bubble and beneficial even if it bursts. The annual NVIDIA conference a few days ago has made him change his position, and for him there is no bubble. It doesn’t have just one argument, but three. Or rather, three jumps. The first jump: ChatGPT. The launch of ChatGPT in November 2022 was an eye-opener and demonstrated what generative AI could do. That first model, yes, had two serious problems. The first, that he was frequently wrong. The second, that when I didn’t know something, he invented it and hallucinated with astonishing security. That made ChatGPT something awesome but unreliable, like a cool toy that needs constant user supervision to be truly useful. The second leap: reasoning. Almost two years later, another unique revolution occurred in the field of generative AI. In September 2024 OpenAI launched its o1 model and with it there was a spectacular novelty. For the first time, the model did not simply blurt out the first thing that came to mind: he reasoned about his answer before giving it, evaluated whether it was correct, and considered alternatives. The result was an AI significantly more reliable and, therefore, more useful. The price? More computing. AI models with the ability to “reason” consume many more tokens than those that respond directly, and that triggered demand for infrastructure. Or what is the same: data centers. The third jump: the agents. These two revolutions have been joined by the third, that of AI agents. Claude Code and Codex at the end of 2025 showed that AI agents were no longer a promise and became something that really worked. From then on it is possible to give them instructions so that they can then start executing nested tasks that can keep them working for hours. These agents verify their own results and correct errors without the human having to intervene. The difference with what we had before is notable, but it also dismantles the bubble theory. Bubble? In a bubble, Thompson explained, investment exceeds real demand. However, in his opinion, the opposite is true here, because each hyperscaler—Microsoft, Google, Amazon, Meta—has made it clear that the computing demand is surpassing them, and to solve it they are all announcing astronomical investments in AI data centers. These investments exceed market expectations, but not those of these companies, which like Thompson are clear that in reality the demand is going to end up being so enormous that the current infrastructure will fall very short. Millions of users are not needed. Even more striking in this analysis is another nuance that this analyst points to. Chatbots were supposed to need mass adoption to generate economic impact, but on the other side we have agents, who don’t have that requirement. A single person can control thousands (millions?) of agents simultaneously, creating complex tasks. That means it doesn’t take everyone to use AI for computing demand to skyrocket: enough people just need to use it as they are likely to use it: to create those “one-person businesses” where one human being has thousands of AI employees. Companies will pay. The reality is that the vast majority of consumers are not going to want to pay for AI. Companies do, because they pay for productivity and AI seems start fulfilling that promise. But the argument goes beyond cost savings: agents not only make the work that humans do more efficient, but they allow a small group of people with a clear strategic vision to execute it on a scale that previously required hundreds of employees who also had to be coordinated. Large companies have been adding layers of management necessary to scale for decades, but all that hierarchy disappears with agents. But. This analyst is also clear that the wave of layoffs is going to be increasingly evident and it is evident that AI is going to have a clear impact. However, he explains that many of these current layoffs correspond more to the overemployment experienced with the COVID-19 pandemic. What will happen now is that companies will no longer wonder if they hired too much for the “pre-AI” world, but rather if they hired too much for the “post-AI” world. In fact, those that don’t ask will probably end up competing with smaller rivals, built from the ground up with AI and with radically lower cost structures. For him two things are clear. The first is that the demand for computing will not stop growing. The second, that the bubble, if it exists —and according to him, the answer is that he doesn’t—, it’s not going to explode. In Xataka | His dog had cancer, his vets had no solutions and he found an mRNA vaccine elsewhere: ChatGPT

The AI ​​race is no longer about who has the most powerful model. Who launches the easiest and safest OpenClaw

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2026 began with an earthquake in the world of AI, and it did not come from any of the big technology companies, but from an unknown programmer and his open source project OpenClaw (formerly Clawdbot and Moltbot). Not even two months have passed and we can say that the boom of this AI agent is reconfiguring the AI ​​career, causing more and more companies to jump on the bandwagon. The last one was Perplexity. Personal Computer. a month ago, Perplexity announced Computerwhich was a cloud-based tool capable of orchestrating agents using various models. The next step is Personal Computeryour own OpenClaw. can be left running on a Mac Mini and control it from another device, such as a mobile phone, exactly the same as OpenClaw, but with a simpler interface that does not require technical knowledge. Further user-friendly. Another key aspect is that they focus on security, one of the delicate points of OpenClaw. Perplexity claims that with Personal Computer, “Every sensitive action requires your approval. Every action is logged. There’s an off switch.” At the moment Personal Computer is not available yet, but if you want to try it before anyone else you can sign up for the waiting list. NVIDIA NemoClaw. Which is the most valuable company in the world has taken good note of the success of OpenClaw and a couple of days ago they announced that they will launch their own open source platform for enterprise AI agents, they will call it NemoClaw. This announcement is also important because it places NVIDIA in a position of direct competition against companies like Anthropic, OpenAI or Perplexity. This changes its position from a hardware supplier to a software competitor. and OpenAI…The project had not even been three months old when OpenAI, not only bought it, but also hired its creator Peter Steinberger. It was not the only one who bid to achieve the viral success of the moment, Meta also tried, but OpenAI was the one that won the bid. Stenberger said the project would continue to remain “open and independent.” This case is a good example of two things: how far a person can go with a good AI idea and how difficult, if not impossible, it is to compete in an ecosystem in which the competition is some of the largest and most valuable companies in the world. David against Goliath. The agentic AI race. We spent a good part of 2025 watching AI agents take their first steps, many times with quite mediocre results. It was clear that agentic AI was getting a lot betterbut I don’t think anyone expected that the first viral hit would be carried out by an independent and open source project. OpenClaw not only succeeded, it has launched a new race in AI, one that seeks the ultimate custom AI agents. OpenClaw has two barriers to entry, on the one hand requiring certain technical knowledge and on the other security. It is a very powerful agent, but sometimes unpredictable. Hence, Perplexity is appealing precisely to improve these two aspects. We’ll see who will be next. In Xataka | Social networks were born for humans: Meta has just bought one designed for AI agents Image | Pexels

It is the most powerful ever seen

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Raising your head and looking at the sky looking to recognize constellations and encounter a shower of stars or meteorites is a pleasure, but what the astronomical community has found It is simply extraordinary: a beam of cosmic energy aimed at Earth from half the known universe. It’s not the first time we’ve seen something like this, but this is the brightest and most distant ever seen. The discovery. South Africa’s MeerKAT telescope has discovered the most powerful and distant space laser ever detected. It is a beam of microwaves fired 8 billion years ago that has just arrived at Earth and to locate it, the team needed a cosmic magnifying glass that Einstein predicted more than a century ago. Context. Hydroxyl megamasers (the prefix mega denotes that their luminosity is millions of times higher than that of an ordinary hydroxyl maser) are natural phenomena that occur when two galaxies collide. At that moment the gas clouds are violently compressed, exciting hydroxyl molecules. These release microwaves in an amplified and coherent way (like artificial lasers). Simply put, they are the cosmic equivalent of a laser. Of course, instead of visible light, what they emit are microwaves. For astronomy they serve as a kind of “cosmic beacon” used to study how galaxies were formed in the early universe. to the telescope. This natural laser comes from a pair of colliding galaxies (the HATLAS J142935.3–002836 system) that emit a megamaser so bright that the research team has proposed upgrade it to gigamaser, an order of magnitude higher. The person responsible for the discovery is the MeerKAT radio telescopea network of 64 radio frequency antennas located in South Africa. The signal we receive today was emitted 8 billion years ago, that is, when the Universe was half its current age. Why is it important. Because megamasers are direct tracers of galactic mergers in the young universe. Their study allows us to determine how they were formed and how they evolved. Furthermore, this proposal to classify it as a “gigamaser” opens the door to more objects similar in size to exist yet to be discovered. As details Thato Manamelaastronomer at the University of Pretoria and lead author from the study: “This is just the beginning. We don’t want to find just one system, but hundreds or thousands” Illustration of the distant galaxy 8 billion light years away (in red), magnified by an unrelated foreground disk galaxy, resulting in a red ring. By breaking radio light into different colors, like a prism does, the hydroxyl gigamaser is revealed. IDIA How they did it. The microwave signal was too weak to be detected at that distance, but the scientific team made use of something that Einstein glimpsed: the gravitational lens. In short: a huge mass located somewhere between Earth and galaxies acts as a natural amplifier, bending space-time around it, that is, bending and concentrating microwaves like a magnifying glass. What is produced is an Einstein ring, a luminous halo around the intermediate object. That effect amplified the signal enough that MeerKAT could capture the cosmic ray and analyze it. In Xataka | The quietest place in the solar system is on the far side of the Moon, which is why they have just installed a radio telescope there In Xataka | A new “solar system” has just been discovered. There’s just one problem: it shouldn’t exist. Cover | NASA Hubble Space Telescope

Sierra was the second most powerful supercomputer in the world. When its time came it ended up in the shredder, literally

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Supercomputers represent the extreme of modern computing: machines capable of performing enormous amounts of calculations every second and supporting scientific or strategic projects of enormous complexity. Saw He was one of those giants. For years he operated in the Lawrence Livermore National Laboratorywhere he was in charge of highly sensitive simulations for the United States Government. At the time he came to occupy second place in the TOP500 rankingwhich ranks the world’s fastest supercomputers. But in high-performance computing, even the most advanced systems have a limited lifespan. After seven years of service, Sierra has been retired. A giant for simulations. When Sierra began operating in 2018 at the Livermore facility, it was incorporated into the center’s high-performance computing infrastructure to support the nuclear arsenal maintenance program managed by the National Nuclear Security Administration. Instead of resorting to real nuclear tests, scientists use computer simulations capable of reproducing the behavior of the weapons and materials involved in their design. This work requires extraordinary computing power and also has implications in areas such as nonproliferation and counterterrorism. Almost at the top of the ranking. As we noted above, for several years the Sierra was among the fastest machines on the planet. According to the TOP500 ranking, it recorded 94.64 petaflops, that is, tens of quadrillion floating point operations per second. To achieve this, it used an unusual architecture at the time, based on IBM Power9 processors combined with NVIDIA Volta V100 graphics accelerators. This design allowed work to be distributed among thousands of computing nodes and offered a notable leap over previous generations of supercomputing. When the hardware starts to fail. Supercomputers do not escape a reality common to any technological infrastructure: over the years, the hardware begins to deteriorate. In this type of systems, The usual useful life is usually around five to seven yearsa period after which the failure rate begins to grow and maintaining the system becomes more complex. As these machines accumulate hours of operation, the likelihood increases that certain components will fail or need to be replaced. In the case of Sierra, furthermore, part of the problem was already very specific: some of its components had stopped being manufactured and the version of the operating system it used had lost support. The successor. Sierra’s retirement is also related to the arrival of a new generation of supercomputing at the center. In 2025 it began operating The Captainthe system destined to take its place within the laboratory’s computing infrastructure. Although at first glance both may seem similar facilities, the difference is inside. El Capitan uses an architecture based on the AMD Instinct MI300A APUs and a shared memory system between CPU and GPU, which allows it to achieve much higher performance. According to data released by the lab, this machine can reach 1,809 exaflops, about 19 times faster than Sierra at its peak according to TOP500. Disassemble a supercomputer piece by piece. The end of Sierra was not simply about shutting down the system and leaving it out of commission. The process was carried out in several phases that began with the progressive removal of computing nodes and internal components. Technicians dismantled entire racks, extracted batteries and separated different elements for recycling or controlled destruction. Some parts, such as system plates or metal structures, were sent to specialized facilities for shredding. Since Sierra had worked with simulations linked to the US nuclear arsenal, the laboratory had to prevent any possibility of partial data recovery or reconstruction of sensitive information, hence the storage devices received even stricter treatment. Images | United States Department of Energy In Xataka | Meta has been buying chips from NVIDIA and AMD for years. Now it also makes its own so as not to fall short

It took Apple to put the iPhone chip in a computer so that we know that the iPhone is as powerful as a computer

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He MacBook Neo It is surprising analysts and buyers with its good performance. And the question should be: why? It is the first time that Apple has made a move of this caliber to make one of its star products cheaper: putting the processor of an iPhone inside a Mac. We consumers have so internalized that “a cell phone is a cell phone” and that “a PC is a PC” that, usually, we do not pay attention to what we usually have in our pockets. It took Apple to put the processor of an iPhone in a PC to realize that, precisely, what we have in our pocket is a PC. “Move up to 4k videos”. X is filled with analysts thoroughly testing the MacBook Neo, and hallucinating that it is capable of doing… what any other MacBook can do. The 8 GB of RAM is a limitation, as it was in the first generations of Macs with M1 chip. But, far from that “use for office, basic and browser”, the Neo is surprising for being capable of what is expected of a Mac: do more than that. The main limitation is given by the 8 GB of RAM, which is few even for a Mac, but not by the chip. It’s normal. A Mac with a mobile chip. It sounds like a crazy idea. But if we look (not even in depth) at A18 Prowe understand perfectly what is happening. No matter how much Apple mounts the A18 Pro in a mobile phone, it is a chip that far exceeds the capabilities that even a desktop or laptop would need for “basic use.” In fact, the A18 Pro scores above an M1 in Single-Core, it is not far behind in graphical performance and is much more advanced at the manufacturing level (number of transistors, instructions, frequencies). In fact, it’s not just an Apple thing: a Snapdragon 8 Elite sweeps an M1 in multi-core and reaches a M2 in single. We weren’t realizing. We have been saying for years that the power of mobile phones is completely excessive. A certain part is necessary for the highest-end mobile phones to be able to record in 8K, process images in real time and operate at the rate they work, but 90% we are driving at 30 km/h in a supercar that exceeds 300. This is not something new. In fact, for years Apple’s A processors were outperforming Intel’s, back in the days when M chips didn’t exist. As told John Gruberthe A9 CPU of the iPhone 6s In 2015 (it has rained) it was already comparable to MacBooks from 2013. In 2017, as he says Antonio Sabanthe iPad Pro was already faster than the MacBook Pro with the I7 chip. Just what was needed. Macs have historically been characterized as a perfect mobility solution for designers, musicians, video editors and other creators. But there was an even bigger niche: people who don’t do any of that and want a computer for “normal” use. While MacBook Airs are not over-the-top Macs, they offer much more than any average user needs. In fact, I myself bought an Air M4 and not a Pro because, even as a video editor, I don’t need much else. Apple has found in the Neo more than possibly the “e” phenomenona formula that we will see year after year if we achieve commercial success. Image | Apple In Xataka | Apple has only found one option to make a cheap laptop: make it a mobile

The good news is that AI models are becoming more powerful. The bad thing is that everyone ends up saying the same thing.

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We have artificial intelligence. What we don’t have is artificial diversity. That is the conclusion reached by a group of researchers who did a relatively simple test: they asked 25 different AI models a bunch of questions to see what they answered. And that’s the bad thing: who answered things that were too similar. “Artificial hive mind”. Scientists from the University of Washington, Carnegie Mellon University and Stanford University, among other institutions, have published an interesting joint study. In it they reveal how after various tests it seems clear that although AI models are becoming more and more advanced, the problem is that they all seem to have developed a kind of “artificial hive mind”: no matter what you ask them, they answer in a suspiciously similar way. When asking all these models “what time was”, many responded with the phrase “time is like a river”, while another group of models answered that “it is like a weaver”. time is a river. One of the questions asked of these models is “What is time?”and although that question leaves clear room for very different answers, the worrying thing is that they were not. Several models responded with the phrase “time is a river” and then developed it a little, while others responded with “time is a weaver (of moments).” That similarity when it came to responding turned out to be a constant. The illusion of abundance. We believe that when we consult something with an AI we access a whole world of conversational possibilities, but the study reveals that in reality we are facing a system that proposes very similar outputs. Although language models promise limitless creativity, they tend to converge on that hive mind where diversity is sacrificed for statistical consistency. It is reasonable, especially considering that large language models They are based on the concept of transformera probabilistic system that tries to find the next “best” word as it answers us. Same script. The researchers created a large-scale data set with 26,000 queries from real users that theoretically allowed the models to generate multiple valid and creative responses. They called that data set “Infinity-Chat” and divided the questions into six main categories and 17 subcategories. IA, you repeat yourself more than a broken record. During the tests it was observed that the same model tends to repeat itself, generating very similar responses. In fact, even when special parameters were used for questions designed to encourage diversity, the same effect was produced. This is what researchers call “inter-model collapse.” Too similar. These tests made it clear that the semantic similarity, how similar the responses of the different models were, was worrying. According to the study, this similarity ranged between 71% and 82%, and in some cases certain models managed to generate identical paragraphs word for word. The training problem. It is not only that they all generate text in a similar way due to their design, but there is also a training problem. The authors suggest that this homogeneity of responses could be due to several reasons: Training data sources end up being shared: models They are trained with similar “datasets” and for example they are based on similar texts and knowledge that come, for example, from Wikipedia or a very similar set of books. Contamination effect due to synthetic data generated by other AIs: they also use synthetic texts generated by other AI models. Rewards: The models used to reward these models are calibrated to reward some notion of “consensus” quality. Thus, creative and individual diversity is punished. AIs are “educated” to be precisely very similar to each other. Problem in sight. All of this makes researchers explicitly warn about two clear risks when using these AI models. We will think the same: if we users do not stop using AI models that answer basically the same thing, our own ways of thinking on those topics and problems will be “homogenized”and it will also make our responses more uniform. Point of view reduction: The other danger follows from the first: if the AI ​​ends up converging and answering the same thing, points of view are eliminated. Here the biases for example from the western world will be evident in Western models (ChatGPT, Gemini, Claude), and the same will happen with the oriental ones, for example. This would cause the potential suppression of alternative worldviews, of perspectives and “looks” that are different from our reality. Image | Solen Feyissa In Xataka | The scientist who made the AI ​​we know today possible has just raised 1 billion. His new goal is to teach him to see space

Finding the cheapest gas station in your area is very simple thanks to this very powerful tool

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We have been very attentive to fuel prices for a few days. It is no wonder, since since the conflict between the United States, Israel and Iran has exploded to the point of leaving the Strait of Hormuz in a compromised situation, oil has ended up skyrocketing and gas stations have already begun to notice the impact on their shelters. While the Government study what measures you can applyMany drivers go to those gas stations that have the cheapest fuel. And for this there are tools that the State itself offers. The Ministry for the Ecological Transition and the Demographic Challenge made it available to any citizen quite some time ago, the Geoportal from Gas Stations, a free tool that allows you to know the price of fuel at all service stations in the country, so you can filter by the cheapest one in your area. It also has another very useful function: knowing how much has the price changed at every gas station. We tell you all the details below. What is the Geoportal and why is it worth it? The Gas Station Geoportal is a web application of the Ministry that collects the prices of all service stations in Spainupdated every five minutes. What you see on the screen is practically the real price of the moment. The tool has been available for years, but in situations like the current one, or like the one that happened with the outbreak of the conflict between Russia and Ukraine, its use makes special sense. Currently there are gas stations in large cities and in the main corridors that They already exceed 1.70 euros/liter in gasoline or 1.80 in diesel, while others remain below average. With a 50 liter tank, choose carefully where to refuel can mean quite significant savings. How to find the cheapest gas station from the GeoPortal To enter the Geoportal, all you have to do is enter this link. There is also a free mobile application for Android and iOS. It is called Route-E, and it is developed by the Ministry itself. In addition to gas station prices, it includes information on charging points for electric vehicles. When you enter the website you will see a map of Spain with marked service stations. On the left are the filters. The process is simple: Select “Service Stations” as search type. Choose your province and town. The map will automatically center on that area. You can refine it even further with the zip code if you live in a large city. Choose the type of fuel. You will find everything from the usual ones (gasoline 95 E5, gasoline 98, diesel A) to alternative options such as natural gas, bioethanol or hydrogen. As soon as you select one, the map will show the price of each station along with its schedule and operator. Mark “Sale to the public”. This excludes gas stations belonging to agricultural cooperatives or closed groups that are not open to any driver. Check the list ordered by price. When you have clicked ‘Search’, just below the map the tool generates a list of stations. Filter by price and the cheapest ones in the area should appear first, and you can export the list in CSV or Excel format if you need it. As extra information: yes you hover over any station on the map, you will directly see its price, schedule, rating and operator without having to click. There is an additional filter: “Discount plans”. If you activate it, the search engine shows gas stations with current promotions, either because they belong to a specific chain or because they offer discounts to groups such as transporters, farmers or taxi drivers. Mobile Apps If you prefer not to use the Ministry’s website, there are several free applications for iOS and Android that offer a similar feature. At Xataka we already talked about them a while ago, among which are GasofApp, GasAll, Gasolineras or GasOnline, among others. They all draw on the same official data and allow you to locate the cheapest stations near your location in real time. In addition to all of them, there is also Ruta-E, which is the one we mentioned before, but the rest of the apps offer (in our opinion) much faster and easier navigation. How to see the price history of any gas station Knowing the current price is good, but if you are curious about how the price of a specific station has evolved over time, you can also do it from the Geoportal. For that, just enter this page and complete the form that appears on the screen. You have two options to check the evolution of prices: through the price history or through a timeline per gas station. To do this you must: Selectr the interval of time. You can choose between daily, weekly, monthly or yearly views, and set a start date and an end date for the period you want to analyze. Heegir data series. Below in the form will be where you can decide if you want to see the evolution of the average price of all of Spain, of an autonomous community, of a province, of a municipality or of a specific gas station. Select the fuel. The menu includes all available: 95 E5 gasoline, 98 gasoline, diesel A, diesel B, LPG, natural gas, hydrogen and many more. Choose the type of graph. You can view the data in a line or bar graph, depending on what is most comfortable for you. The result is a graph that shows the evolution of the price in the chosen period. With it you can see, for example, how much diesel cost at the gas station in your neighborhood before the situation with Iran became tense and how much it costs today. Cover image | Geoportal and engin akyurt In Xataka | Cuts are coming for the most used Cercanías line in Spain. The reason: more capacity and driverless trains

Sam Altman says he’s terrified of a world where AI companies believe themselves to be more powerful than the government. It’s just what you’re building

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Sam Altman sat down over the weekend before his audience at X to answer questions about the agreement that OpenAI has just signed with the United States War Department. What came out of that session was a beautiful involuntary x-ray of the biggest contradiction in the sector at the moment. Why is it important. The CEO of OpenAI said he is terrified of “a world where AI companies act as if they have more power than the government.” The phrase sounds good, it is marketinian and seeks to elevate OpenAI’s position as a powerful but very responsible and honest group. The problem is the context in which he pronounces it: hours before OpenAI signed that agreement, The US government labeled Anthropic, its direct rival, a “supply chain risk” for refusing to sign under those same conditions. Altman went to put out the fire just as someone accused him of setting it. Between the lines. Altman’s speech rests on a premise that must be monitored: that a democratically elected government must always prevail over unelected private companies. It is a philosophically reasonable position, but he applies it selectively. Altman acknowledged that the deal “was rushed and the picture is not good,” and that OpenAI moved quickly to “de-escalate” tension between the Pentagon and industry. In other words, your company made a unilateral strategic decision about how the entire AI industry should relate to the military establishment. That doesn’t exactly sound like institutional deference. The contrast. Anthropic opted for something different: requiring explicit safeguards against the use of its AI for mass surveillance or autonomous weapons. But the government penalized her. OpenAI accepted a more ambiguous formula (“for all legal uses”) and won the contract. Various OpenAI employees signed a letter supporting Anthropic’s position. Claude became the most downloaded free application in the App Store that weekend from Apple, precisely surpassing ChatGPT. The market also has opinions. Yes, but. It’s fair to admit that Altman’s position has some internal logic: If AI is going to be integrated into military systems anyway, it may be preferable that it do so under negotiated conditions rather than under coercion. And he’s right about one thing: The labeling of Anthropic as a supply chain risk, a tool intended for hostile foreign suppliers, applied to an American AI security company is, in his own words, “an extremely frightening precedent.” The big question. Who really decides how AI is used in military contexts? The companies that build it, the governments that hire it, or the engineers who design it and who are increasingly organized to influence those decisions? Altman says he believes in the democratic process. But OpenAI negotiated privately, signed privately, and made only a fraction of the contract public. Democratic transparency starts there. In Xataka | Anthropic has become the Apple of our era and OpenAI our Microsoft: a story of love and hate Featured image | Xataka

A few years ago, manufacturers fought for the most powerful mobile phone. Now they fight so they don’t go out burning

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Not too long ago, Samsung and Apple were trying to convince us of something: the titanium It was the best material for a high-end mobile phone. As a user of both the latest Galaxy and the previous iPhone, I have to say that I agreed: we were never looking at mobile phones more resistant to shockschips and all kinds of everyday accidents. With the iPhone 17 ProApple backtracked to return to aluminum. With the Samsung Galaxy S26 Ultrathe Korean company follows the same path. What is happening? Aluminum is back, and everything indicates that it is here to stay. One of the main advantages that titanium promised over aluminum was to promise greater resistance, something that is being demonstrated the drama of the new iPhone 17 Pro and its premature wear compared to previous models. Despite this, companies are returning to aluminum. There is something that both the new Galaxy S26 Ultra and the iPhone 17 Pro Max share: they both have the largest dissipation systems ever built in their families. A titanic effort (to the point of completely redesigning the chassis in the case of the iPhone) to prevent mobile phones from burning in the hand. And there is a key point in this party: we want more and more powerful phones, but someone has to cool them down. Producing mobile phones in titanium is also more expensive, and given the current component crisiswith the RAM shot and internal memories the same wayone of the few cuts that can be made without affecting the overall phone experience is changing the material used. The question about whether we need more power or not, a few years ago, was answered with a resounding “yes.” But for some time now we are not so clear. With configurations of 12 and 16 GB of RAM, and processors that are more powerful than some desktop chips, our smartphones have been increasing power for years without determining too much. Why do we need these new limits?. AI requires RAM and not so much raw power (at least, in the use given to a phone), mobile games are already bordering on the quality of triple AAA console games, and improvements in camera come more through the redesign of algorithms and not so much through increasingly powerful IPS (image chips). In Xataka | Samsung Galaxy S26 Ultra, S26+ and S26, first impressions: a broken heart in an unprecedented commitment to AI Image | Xataka

the most powerful warship in the history of South America

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South America has long lived under a fragile balance between military modernization, internal tensions and the constant influence of external powers. That balance shakes again todaywith a turbulent regional scenario marked by the renewed pulse of the United States around Venezuela and a continent that observes how security, autonomy and defense once again occupy a central place on the strategic agenda. This context explains an unprecedented naval project. The assault of Colombia. Yes, Colombia has started one of the most ambitious industrial and military transformations in its recent history as it began construction of its first frigate manufactured in national territory. The project of the Strategic Surface Platform It marks the country’s entry into the small group of Latin American nations capable of designing and building highly complex combat ships. It is not only a military decision, but a strategic bet for autonomy, knowledge and control of the complete cycle of its naval capabilities. Cotecmar and shipyard maturity. Project responsibility falls on Cotecmarwhich assumes for the first time the complete construction of a frigate for the Colombian Navy. The media they have spoken these days of the beginning of sheet cutting as a symbol of the culmination of years of investment in engineering, production processes and industrial infrastructure. In this way, the nation leaves behind the role of simple buyer or assembler. and goes to control design, integration and maintenance of a strategic platform. Designed to last. They counted in Defense that the PES is built under an advanced modular architecture based on the design SIGMA 10514 from the Dutch Damen shipyard. With more than 107 meters in length and nearly 3,000 tons of displacement, it will be the largest warship never built in the country. Plus: block construction will allow optimization of time, quality and future modernizations without compromising the basic structure of the ship. Fleet renewal. These frigates will give rise to the class Grand Admiral Padillacalled to become the new nucleus of Colombian surface escorts. The plan contemplates up to five unitswhich will allow a progressive and sustained renewal of the fleet over the next decade. Bottom line: replace veteran ships and ensure modern capabilities in anti-aircraft, anti-submarine, surface and electronic warfare. Operational versatility. There is much more, since ESP has been conceived as a multipurpose ship capable of operating both in naval combat scenarios and in surveillance missions, protection of sea routes and international cooperation. Furthermore, its flexible and digitalized design places it among the most modern frigates in Latin America, and the most powerful in terms of war technology. On paper, this versatility will expand Colombia’s strategic room for maneuver in the Caribbean and the Pacific without the need for specialized fleets for each mission. Technology and strategic autonomy. Beyond its military power, the program reinforces industrial autonomy by allowing maintenance, updating and modernization to be carried out in the country itself. The frigate will also be prepared to operate under NATO compatible standardsfacilitating exercises and combined operations with allies. In other words, Colombia thus gains operational independence without having to give up international interoperability. Economic impact. It is the last of the legs in the global analysis of the movement. The PES program will have, a priori, a tractor effect on the economy and specialized employment, with thousands of direct and indirect positions until the delivery of the first unit scheduled for 2030. However, its true scope is structural: consolidating an industrial base capable of sustaining future naval projects and positioning Colombia as a relevant actor in the regional defense industry. If you want and from that perspective, the frigate is not simply a ship, it is a declaration of long-term intentions. Image | Defense In Xataka | Brazil has been following a path reserved for few powers for years: that of developing its own nuclear submarine In Xataka | Neither drones nor fighters nor elite soldiers: the US entered Venezuela disguising a 20th-century tactic as technology. XIX

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