matters

the fine print matters as much as the price

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If you were thinking about signing up for PlayStation Plusit may be worth checking the calendar. Sony has announced a price rise for its subscription service, which offers monthly games, online multiplayer and other benefits to users of its consoles. The change comes into effect tomorrow, Tuesday, May 20, although with an important nuance: it will not affect all users equally. The announcement has come through a message posted on X. The Japanese company has indicated that the increase responds to current “market conditions” and that the new rates will start at 10.99 dollars, 9.99 euros and 7.99 pounds for one-month subscriptions, and at 27.99 dollars, 27.99 euros and 21.99 pounds for three-month subscriptions. Click to see the original publication in X The immediate question is which markets exactly this rise will reach. At the moment, Sony has not published a official list of countries not one entry in the PlayStation blog with more details. We only have that message, quite brief, for reference. Sony explains that the new PlayStation Plus prices will only apply to new subscribers in “select regions.” It also adds the following: “This price change does not apply to current subscribers (except in Türkiye and India) unless the existing subscription changes or expires.” The statement leaves some unknowns open. The expression “prices will start at” presumably points to the plan essentialthe entry level of the service. In Spain, this modality currently costs 8.99 euros per month and 24.99 euros if contracted for three months. If the increase is finally applied to the Spanish market, the change would mean paying 1 euro more in the monthly plan and 3 euros more in the quarterly plan. What we still don’t know is what will happen with the other levels of the service. PlayStation Plus Extra and PlayStation Plus Premium are the most expensive modalities and include additional benefits, such as Ubisoft+ Classics, a broader catalog of games, classics and title trials, depending on the contracted plan. For now, Sony has not given details about possible changes to these subscriptions. We’ve contacted Sony for more information about the extent of the upload and will update this article if we hear back. Images | sony In Xataka | Pluto is in Sagittarius and that can only mean one thing: the third trailer for ‘GTA VI’ will be out on May 14

publishing matters more than research

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During last March ICML (International Conference on Machine Learning), the academic conference dedicated to machine learning (machine learning) oldest in the world, rejected 497 scientific articles at once after detecting that 506 reviewers had resorted to the artificial intelligence (AI) to write your evaluations. They had violated a rule which they themselves had agreed to respect. This conference is organized by the International Machine Learning Society (IMLS), a non-profit organization, and has been held annually since 1980. Every year, researchers working in the field of AI submit their scientific papers in late January or early February to ICML. Those papers They are reviewed by a committee made up of other researchers in this field with the purpose of evaluating them and publishing them if they finally pass a thorough review that normally lasts several months. Decisions to accept or reject articles are usually communicated to authors during the month of May, and the ICML conference is usually held in July. Publish in ICML, NeurIPS (Conference and Workshop on Neural Information Processing Systems) or ICLR (International Conference on Learning Representations) is equivalent to what in other disciplines it would be to publish in the scientific journals Nature or Science. But ICML has a serious problem: its authority is being questioned in r/MachineLearninga Reddit community specialized in machine learning which has more than 2.5 million subscribers. A perversion where reviewers don’t have time to review Before moving forward, it is worth stopping at a very important milestone: the number of scientific articles received by ICML is growing overwhelmingly year after year. In 2023 it received 6,538 papersand in 2024 no less than 9,653 articles, which represents a growth of 48%. The root of the problem lies in the fact that the number of qualified reviewers is not increasing with the same rhythm as the number of scientific articles that need to be evaluated. As I mentioned a few lines above, ICML rules establish that reviewers cannot lightly resort to AI to carry out their evaluations because this procedure can introduce bias. In fact, a study carried out on ICLR 2024 has revealed that scientific articles evaluated with AI models They tend to receive higher scores than those reviewed with the conventional procedure. This is the problem. For the 2026 edition, ICML offered evaluators to choose between two policies: one that prohibited the use of AI and another that allowed it, but with conditions. Only those who chose the first option and failed to comply were sanctioned. Of the 506 offenders, only 398 were reciprocal evaluators who had submitted a ‘paper’ However, there is one relevant fact that is worth not overlooking: the 497 scientific articles that were rejected in March of this year were reviewed by offending reciprocal evaluators. This simply means that they are researchers who simultaneously act as authors and reviewers, so their scientific article was penalized due to their violation of the ICML rules of conduct. Of the 506 offenders, only 398 were reciprocal evaluators who had submitted a paper. Interestingly, the detection system that ICML has used consists of hiding specific instructions within the PDFs of articles pending review. Those instructions are invisible to a human reader, but any AI model processing the document interprets them and includes specific, trackable phrases in the evaluation. ICML has not used generic AI detectors. Of course, each case detected was manually verified to verify that a violation had actually been committed when preparing the evaluation. What is happening reflects an unappealable reality: the review system has failed and needs to be rebuilt. The reviewers can’t cope. Neither those of ICML, nor those of NeurIPS, nor those of ICLR. The number of qualified reviewers should grow at the same rate that the number of scientific articles that need to be evaluated, and it is not happening. Furthermore, this scenario has introduced another problem: acceptance or rejection decisions have acquired a random aspect that threatens the consistency and reliability of the evaluations. It is still not entirely clear what path should be followed to resolve this problem beyond the need to increase the number of qualified evaluators. One option is to improve the transparency of the review process publishing all evaluations. Even those of rejected articles. The evaluation process could also be transformed into a two-way procedure in which authors also evaluate the quality of the reviews they receive. In this way, the evaluators will have a history that will prove their good work. We will see what strategy the conferences finally implement. In 2027 we will clear up doubts. Image | Charlesdeluvio (Unsplash) More information | ICML In Xataka | With DeepSeek V4, China has gained more than just an AI model: it has unlocked the potential of its domestic chips

Kimi Code does 75% of what Claude Code does at 20% of its price. The question is whether that 25% that is missing is the one that matters.

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A few days ago, the Chinese company Moonshot AI launched Kimi K2.6its new LLM that competes with the Gemini, GPT and Claude model families and is also especially competitive in price. Weeks earlier, it had launched Kimi Code, a programming AI agent that in turn competes with Gemini Cli, Codex and Claude Code. The question is obvious: can the Kimi Code/Kimi K2.6 pairing really compete with the fashionable pairing, Claude Code/Opus 4.7? The answer is complicated. A great model (but not perfect). Kimi K2.6 is an open weights model with one trillion parameters in total (an American trillion), of which 32 billion parameters are active and which uses the well-known Mixture-of-Experts architecture. In it launch article Its performance is shown compared to that of GPT-5.4 and Opus 4.6 and the truth is that its numbers in these synthetic tests seem really excellent: Here Kimi K2.6 is compared to GPT-5.4, Claude Opus 4.6 and Gemini 3.1 Pro. Source: Moonshot AI. Up to 8 times cheaper than Opus 4.6. Has subscription plans Claude Pro or ChatGPT Plus style, but it can also be used via API. The price in that case is $0.60 per million input tokens (0.16 if cached) and $4 per million output tokens. Claude Opus 4.6 costs $5 per million input tokens and $25 per million output tokens, or up to eight times more. Claude Opus 4.7 It has the same price and is theoretically better in performance, but when Kimi K2.6 was announced this version had not yet appeared (nor GPT-5.5). The magic of the swarm of AI agents. Claude Code works sequentially. Analyze the problem, execute a step, check the result and decide how to proceed. In Kimi Code a different approach is used: a “master agent” divides or decomposes the task we ask of it into independent subtasks and from that division launches up to 300 “subagents” that run in parallel and are capable of coordinating up to 4,000 steps simultaneously. Are many working at the same time better than one? It is the so-called “swarm of agents” of Kimi K2.6 that is used to the fullest in Kimi Code and that we can also activate in its free version on its official website. In Kimi K2.5 up to 100 subagents and 1,500 steps could be launched, so the jump is significant. In internal tests, Moonshot showed how these swarms managed, for example, to “refactor” an open source financial engine, working 13 hours straight and making more than 1,000 tool calls with a 185% improvement in average performance. Of course, these were internal tests. Beyond benchmarks. Kilo.ai is a company that develops tools like Kilo Code or Kilo CLI—programming agents similar to Kimi Code—and its engineers wanted evaluate the performance of both combinations. They gave Claude Opus 4.7 and Kimi K2.6 the same 1,042-line prompt to create FlowGraph, a workflow orchestration API with directed graph validation or real-time event streaming. Both models ran on Kilo CLI because what they wanted to compare were the models without further ado. Kimi was cheaper, but he also failed more. Claude Opus 4.7 finished in 20 minutes and the final cost was $3.56. Kimi K2.6 took longer, partly because server availability was limited (the model had just been launched), but it cost $0.67. Five times less. Kimi K2.6 did it well at a ridiculous price. Claude did much better, but it also cost five times as much. Kimi did 75% of what Claude did at 19% of the cost. The problem is that both believed they had done everything right and did not detect if they had made mistakes. Further analysis revealed that Claude had committed one and that Kimi had committed six of varying importance. According to Kilo.ai analysts, the final score for both was 91 points out of 100 for Opus 4.7 and 68 points out of 100 for Kimi. Two ways to see the glass. That score seems to make it clear that Kimi is simply cheaper because he did a worse job. But Kilo engineers had another way of looking at it. They have been comparing open weight models of Chinese companies for some time and have noticed how the gap with the “frontier” models of Anthropic or OpenAI is becoming less and less pronounced. “With a price of $0.67 and a thorough review, Kimi K2.6 is now a viable option. With a price of $3.56 and fewer fixes needed, Claude Opus 4.7 is the safer option. The choice between the two options depends on the analysis. A year ago, this choice was practically non-existent at this level of complexity.” Review is mandatory. Or what is the same: if after the work of Kimi K2.6 one carried out a more in-depth review and correction, it is likely that all these errors would be detected and corrected, but if we had to trust both models and we could only execute “one pass” of AI execution, Opus 4.7 would win the game. The key is that: one should not trust the code of any model right away, and it is advisable to always review that code. The geopolitical factor. Kimi and Kimi Code come from China, and the startup Moonshot AI has financial backing from Alibaba. The code that is processed in these models passes through their servers, something that for an individual developer may be irrelevant. However, for a company with sensitive proprietary code, contracts that must comply with certain European or American regulations and projects in regulated sectors, this can be a significant obstacle. Kimi Code mitigates this problem by offering the possibility of running the model locally thanks to its open weights, but that requires very powerful machines and eliminates part of the cost advantage. What Kimi Code has that Claude Code doesn’t. The clearest difference between both programming AI agents is parallelism. As we said, the ability to launch up to 300 subagents to work simultaneously attacking the same problem at the same time is remarkable. For analysis of large repositories or generation … Read more

We have been obsessed with measuring deep sleep with a watch for years. Science says what matters is dreaming vividly

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The reality is that waking up feeling like you’ve fallen asleep like a dormouse is one of the greatest pleasures in life, since it makes you start the day in a very different way. Until now, sleep science has told us that to achieve that feeling of rest we had to maximize deep sleepbut now the rarity and the intensity of dreams They are also gaining a starring role here. A new study. A recent published research in the prestigious magazine PLOS Biology by an Italian team has revealed that vivid and immersive dreams are directly related to a greater subjective sense of deep sleep. And most fascinating: this occurs even when the brain’s electrical activity tells us that we are in a phase of light sleep. How they have done it. To reach this conclusion, the researchers did not settle for morning surveys, but rather They took 44 adults healthy people to a sleep laboratory for four consecutive nights. Here they simply had to be connected to a high-density electroencephalogram to monitor their brain activity in real time. The methodology used was quite methodical, since all of them were awakened repeatedly, reaching the figure of 1,900 awakenings in total throughout the entire study. But they were not waking them up at any time, but rather sleep phase N2 which is what belongs to non-REM sleep and is what is considered relatively light sleep, where the biological need to sleep usually decreases as the night progresses. But the important thing is that, after each awakening, the participants had to describe their previous mental experiences and rate, from 1 to 10, how deep they felt their sleep had been just before opening their eyes. The result. By crossing the data from the dream stories with the EEG activity and the subjective perception of the participants, the scientists found a pattern that indicated that when the participants reported vivid, strange dreams, with high emotional intensity or very visually rich, they reported having been immersed in a very deep sleep. In contrast, if the mental activity before waking up was abstract, vague, or the participants had “meta-awareness,” which is thinking about real problems or being aware that they were sleeping, they felt that their sleep had been very superficial. A change. In this way, this sensation of dreamlike depth challenged the electroencephalograms themselves. And the fact is that, although the EEG showed that the participant’s brain activity was dangerously close to wakefulness, if he was immersed in an intense dream plot, his brain interpreted that he was resting peacefully. Memory doesn’t matter. One of the most interesting details of the study points to a situation that can be frustrating: waking up knowing that you had an incredible dream, but unable to remember the entire plot. Here the scientific study demonstrates that narrative memory is not necessary for rest, since the participants continued to rate their sleep as deep and restorative despite not remembering it. In this way, the simple fact that the brain has been “disconnected” from the physical environment and immersed in its own virtual world seems to be enough to preserve the subjective perception of rest. What does it mean? This discovery opens the door to new treatments for sleep disorders, since, in the case of insomnia, the problem could not only be in the clinical architecture of sleep, but in an alteration of dream activity or a lack of mental disconnection from the environment. And this is precisely where science has to begin to investigate. Images | iam_os In Xataka | Waking up at 3 in the morning is totally normal: sleeping straight through is a modern invention, not an evolution

lose the market that matters

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Anthropic has closed a financing round of 30,000 million dollars that doubles its valuation to 380,000 millionjust four months after being valued at 183,000 million. The operation is led by the Singapore sovereign fund GIC and Coatue, with participation from NVIDIA and Microsoft. Bang. The company has already raised more than $57 billion since its founding in 2021. OpenAI continues to have the leadership in valuation with half a billion after its last round of 40 billion at the end of last year, but now it faces a threat that is growing faster than expected. Between the lines. The numbers explain an uncomfortable paradox for OpenAI: ChatGPT processes 2.5 billion queries daily and takes the consumer market by storm… …but Anthropic controls 32% of the LLM business market according to Menlo Ventures, compared to 25% for OpenAI. And in programming, the distance is even greater: 42% versus 21%. OpenAI has seen its enterprise share fall from 50% in 2023 to 25% todayjust when this segment is emerging as the most profitable and predictable. If the consumer chatbot doesn’t turn out to be the winning horse in this race, Sam Altman has a big problem. The contrast. Sarah Friar, chief financial officer of OpenAI, acknowledged in Davos that they have gone from 70/30 consumer-business to 60/40, with the expectation of reaching 50/50 this year. The transcript of the interview CNBC Bring all the details. Dario Amodei, CEO of Anthropic, boasts of maintaining an 80/20 business-consumer ratio from the beginning. Anthropic reports recurring revenues of more than 14 billion, with growth multiplying tenfold annually for three years. And customers spending more than $100,000 annually have increased sevenfold in 12 months. Yes, but. Neither of them is profitable yet: Anthropic projected gross margins of 40% by 2025, but lowered his expectations by 10 points due to inference costs 23% higher than expected. The servers rented from Google and Amazon weigh more than calculated. OpenAI faces the same problem as both turn to the market every few months to fund the next phase. That is why both are considering IPOs between this year and next. Unexpected twist. The launch of Claude Code in December has accelerated enterprise adoption in a way that perhaps no one anticipated. The tool has not only doubled users in a month, but has consolidated the perception of Claude as “the serious option” for companies compared to ChatGPT. If companies value something, even more than the end consumer, it is stability and predictability. And Anthropic has been able to capitalize on that demand. Missing? Temporal context: By the time Apple reached a valuation of 380 billion, it had already been in existence for almost four decades. He sold Macs, he sold iPods, he sold iPads. It was already going for the iPhone 5s and its annual profit was 50 billion dollars. Anthropic reaches the same figure without being profitable, compressing decades of value creation into just a few quarters. It is not necessarily wrong, especially with the recent good dynamics of Claude’s company, but it remains to be seen if these models can sustain those explosive revenues and convert them into profits before the market loses patience. In Xataka | Featured image | OpenAI, Anthropic

Science has been measuring whether size matters for years. A study with 3D simulation has the most complete answer

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It is probably one of the most recurring questions in the history of humanity and, yet, one of the ones that accumulates the most myths per square meter. Leaving aside popular culture and internet forums, scientific literature has been trying for years to quantify what is true about the importance penis size. Science to the rescue. A published study This year, PLOS Biology wanted to resolve a question that has undoubtedly generated many jokes and also some complexes in the male sex. And the truth is that the short answer to this question is that size does matterbut perhaps not for the reasons most men believe. The signal theory. Until now, many studies were based on simple surveys to answer this question. However, this study has gone one step further by using 343 3D figures to evaluate the response of more than 800 participants. The goal was to understand penis size not only as a reproductive tool, but as an evolutionary signaling trait. The results. In the investigationfemale participants rated men as more attractive, which combined three factors: greater height, a “V” shaped torso (wide shoulders and narrow hips) and a larger penis. But there is a very important nuance. Attraction doesn’t follow a line of “the more the merrier” ad infinitum. The study in this case detected diminishing returns, since after a certain size, attractiveness does not increase proportionally, but rather there is a ceiling. Competence. But men also went through this study to evaluate the size of other men. In this case, it was highlighted that they perceived those with larger genitals as more competitive rivals and with greater fighting capacity. This suggests that, evolutionarily, the size could have served as both sexual ornament and a signal of status or threat towards other males, similar to the antlers of a deer. What they prefer. If we move away from evolutionary theory and go to stated preference, the baseline study remains the one published by N. Prause in PLOS One in 2015. This work is key because it differentiated, for the first time with rigor, between the type of relationship sought. In this case, using 3D models on heterosexual women, a preference was specifically shown for a slightly larger size, averaging about 16.3 cm in length in an erect state and 12.7 cm in circumference. But in the case of stable couples, the preference dropped slightly to 16 cm and 12.2 cm in circumference. The key reading. The first point to note is that circumference matters more than length in visual choice. The second is that these measures are only “slightly” above the population average. A mechanical reality. This is where science busts most porn myths. A narrative review published in the Journal of Sexual Medicine in 2023 analyzed the existing literature To answer the million-dollar question: does a larger penis give more pleasure? The answer is a very nuanced ‘it depends’. Science points out in this case that there are few high-quality studies that manage to directly link size with the organism, and the results are heterogeneous. But if we draw a clear conclusion, the truth is that the quality of the relationship such as trust or communication correlates more strongly with sexual satisfaction than the size of the penis. Male anxiety. If female preferences are moderate and satisfaction depends more on technique than size, why is there still so much anxiety among society? The studies in this case They point out that there is a great disconnection between reality and male perception, since approximately 38% of men report some degree of dissatisfaction with their penis. However, the vast majority of couples have a positive view of their partners’ genitals. Images | Deon Black In Xataka | Desire in times of stress and screens: this is how the era of programmed sex was born

The Academy has discovered that being relevant matters

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The Hollywood Academy of Motion Picture Arts and Sciences has signed an agreement that, beyond the specific broadcast of its awards gala, marks a turning point in the entertainment industry: from 2029YouTube will broadcast the oscars exclusively and free for everyone. The story underlying this movement is not so much the demise of cable television (a phenomenon that in Spain we perceive from a certain distance) but the confirmation of a fundamental change: if the content is not available in a simple and instantaneous way, it does not exist for the majority. The deal. YouTube will obtain exclusive worldwide broadcasting rights from the 101st edition of the awards, scheduled for 2029. The deal extends until 2033 and the transmission will be free worldwide, including the main ceremony, the red carpet and exclusive material from the backstage and the Governors Ball. Until now, Disney paid $100 million each year for broadcast rights on ABC, a very tight economic dealsince this year, for example, it only earned 127 million from advertising during the broadcast. Little thing compared to YouTubewhich recorded advertising revenue of $36 billion in 2024. Worse and worse audiences. The break between ABC and the Academy comes from progressive worsening of audience data. The 2019 ceremony brought together 29.6 million viewers; In 2020, the figure dropped to 23.6. But the real collapse came with the 2021 edition in the middle of the pandemic, which sank to 10.4 million viewers. In 2025 there were signs of recovery with 19.69 million viewers, the highest audience in five years, thanks to simultaneous streaming on Hulu and the return of Conan O’Brien as host. Possible solutions. To improve the numbers (and the friction between ABC and the Academy) the network proposed changes inspired by the Grammys: moving technical categories out of the main broadcast, prioritizing musical performances and reducing the total duration. The Academy resisted, but in 2018 announced the creation of a category of Outstanding Achievement in Popular Filman idea so bad that it was canceled just 29 days later. Instead of actually cutting the Academy added two new categories (casting in 2025 and stunt coordination in 2028). It was seen coming. In fact, the jump to YouTube is the inevitable step that certifies the agony of cable. In fact, this is demonstrated by the Academy’s own decision to incorporate streaming simultaneous on Hulu this very 2025, despite a good number of technological difficulties. YouTube is the inevitable next stop: instant distribution, unrestricted global reach, and free (or, at most, dependent on a single subscription to the platform’s premium option). Taking into account the traditional difficulties in watching the ceremony, YouTube’s proposal has a certain radicality: from anywhere you can watch the ceremony without downloading applications or bypassing blocks. The lace There is one more detail that certifies that the grudges come from afar. In May 2024, YouTube hired Justin Connolly, a veteran who had spent a quarter of a century at Disney, to oversee the platform’s media and sports operations. The signing triggered a legal battle: Disney filed a lawsuit trying to block Connolly’s incorporation, in a dispute that was resolved through an out-of-court settlement. A former Disney executive, speaking to The Wrap, stressed: “Do not underestimate the importance of the hatred and resentment between Justin Connolly and Bob Iger. The dispute continues.” And we just saw the last blow. In Xataka | The “ghost” category of the Oscars: it exists but it is so demanding that there have never been films that compete for it

Not all of them serve the same purpose, and choosing well matters more than ever.

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Buy a smart watch It may not be as easy as it seems. We begin to review the options on the market and the eternal question arises: which one is the most suitable for us? Which device will be worth the investment? And, precisely, it is at this point where we will try to help you. In a new video from the Xataka YouTube ChannelAna Boria brings the 7 best smart watches of the year. This is a selection that derives directly from the finalists of the Xataka NordVPN 2025 Awards. Our partner gives us key data about each model to help us choose the best option. The seven best smartwatches of 2025 Google Pixel Watch 4. Google watches have evolved significantly in recent years, offering an increasingly solid bet. This generation arrives in identical versions in 45 and 41 mm cases. They also boast a screen AMOLED LTPO between 1 and 60 Hz. And it reaches 3,000 nits of peak brightness. “In terms of measurements, the latest generations of Pixel Watch have improved a lot and in fact it is capable of measuring everything with an acceptable precision for general use and fitness,” says Ana, but in the video published on YouTube she does not hesitate to mention some manufacturers that have more successful products. Xiaomi Watch S4. From the American brand we move on to the Chinese march. Yes, it has a huge product catalog, and that catalog includes smart watches. “The Xiaomi Watch S4, a big 47mm smartwatch and with aluminum frames,” says our colleague, and highlights its interchangeable bezel. Of course, not all are advantages. Like the Pixel, Xiaomi’s proposal also has its negative points. Ana reminds us which ones, which will allow us to continue with the purchase if it is not a priority for us or look for an alternative if not. Of course, the price is a highlight: 160 euros. Amazfit T-Rex 3 Pro. Among the finalists of the Xataka Awards was this proposal, which is available in two sizes (44 and 48 mm). At the display level, an AMOED screen reigns supreme with a peak brightness of 3,000 nits. “ANDIt’s a very sports-oriented watch since you can take it anywhere without anything happening to it,” says Ana. We are looking at a watch focused on sports, offering options not only for health monitoring but also for training performance. In the video that we have just published you will find some features that stand out if you are a user who values ​​sports and are thinking about buying this smart watch. Samsung Galaxy Watch 8. “For this generation, Samsung has released Wear OS 6 with One UI 8 for Watch and has included integration with Google Gemini, which allows us to use voice commands to request information and/or do things on the fly”, details our colleague. Do you want to have AI on your wrist? This may be your option. Garmin Fenix ​​8. Garmin’s journey in smart watches has been very interesting, developing a product as polished as the one we find in this selection. The Garmin Fénix 8 comes in three sizes (43, 47 and 51 mm), incorporates an AMOLED screen and if we must mention a strong point, it is resistance. Of course, Ana says: “The Garmin Fénix 8 is an expensive smartwatch.” And it is, with a recommended retail price of 950 eurosit is a choice that may be outside of some budgets. However, in the video you will find more details so you can evaluate if this watch is really worth the investment for you. Apple Watch Series 11. Among the finalists of this year’s Xataka Awards is Apple’s proposal in second position. It is a watch that has many benefits, many followers, but it is certainly not for everyone. It is available in 42 and 46 mm versions, and there is an option to purchase it with 5G. You may be wondering how it is different from the Apple Watch Series 10. Ana helps us find some differences. In addition, he mentions the important role that the health section occupies: “They place it as one of the best in terms of accuracy on the list“This proposal starts at 449 euros. Huawei Watch GT 6 Pro. At the end of the article, but at the top of the podium is the Huawei Watch GT 6 Pro, winner of the Xataka NordVPN 2025 Awards in the Best smartwatch category. Ana reminds us that, in addition, this product won the community award, the one you chose. “It is a large watch, 46mm, with an AMOLED screen that reaches 3,000 nits of maximum brightness and protected with sapphire crystal“explains Ana, who highlights its benefits in the sections of sport, health and autonomy. She does not miss the price, which starts at 379 euros, but can be obtained with discounts. Images | Xataka In Xataka | Apple Watch SE 3: a fantastic renewal that was worth waiting three years for

In AI, teraflops came first, then parameters. Now what matters are the ‘bragawatts’

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The technological conversation revolves around fashions, and there is nothing as fashionable as artificial intelligence. All the countries that want to be part of the conversation are developing their models and tools and it is interesting how geopolitics permeates everything: the US seeking to be sovereign while China wants to monetize now. But as interesting as the capabilities of one model or another, it is to talk about two concepts that are totally aligned: data centers that feed the enormous amount of calculation necessary to train artificial intelligence and, evidently, Where do they get that absurd amount of energy from?. And as a result of that conversation a fascinating term has been born: the one with the ‘bragawatts’. The ‘bragawatts’ as the bragging of AI Something common when companies like OpenAI or Google announce new data centers focused on AI is that they give a bombastic number about the amount of energy it will consume. RecentlyOpenAI announced a new campus in Michigan that, together with six other also recently revealedthey will need more than 8 GW to operate. They also talk about money: a plan launched in January of this year of 500 billion dollars and 10 GW of planned capacity. According to the company, it is “the infrastructure necessary to advance AI and reindustrialize the country.” In Financial Times They have done the math and, with the Michigan project, the company has 46 GW of computing power. As when talking about operations like the purchase of Activision-Blizzard by Microsoft for 75 billion dollars, context is needed because it is difficult to imagine such enormous numbers. If 1 GW is enough to power 800,000 homes in the United States (with what they spend on air conditioning at any time of the year), these OpenAI data centers would consume as much energy as more than 44 million homes. More context pointed out in the Financial Times: almost three times all the homes there are in California. And the fact that companies give this power data so happily has led to some coin the term ‘bragawatt’. This neologism is a sarcastic combination between ‘brag’, “to show off”, and ‘watts’, the unit of power. In Spanish it is difficult to find a name, but basically it is a boast, something that some companies use, publicly exaggerating the energy consumption capacities planned for their infrastructures. There are several reasons why this is done, but as with any type of announcement by companies that are ‘public’ -those listed on the stock exchange-, the objective is to attract the attention of both the press and the technology sector and, above all, investors. In the economic environment they comment that these bombastic figures are not always met, but beyond the marketing boastthere is a bottom to all this. OpenAI asked the US government to secure 100 GW annually to fuel the country’s different AI developments and NVIDIA explained quite well why estimating the demand for these centers is a problem. In a recent report, the company commented something very interesting: Unlike a traditional data center, which runs thousands of unrelated tasks, an AI “factory” operates as a single system. When training a large language model, or LLM, thousands of GPUs perform intensive calculation cycles, followed by periods of data exchange. Everything is done in perfect synchrony that generates an energy profile characterized by massive and rapid load variations. The electrical consumption of a rack can go from an “idle” state, around 30% utilization, to 100% and back again in a matter of milliseconds. This forces engineers to oversize components to support the maximum current, not the average, which increases costs and space requirements. When these oscillations are added across an entire data room – which can represent hundreds of megawatts rising and falling in seconds – they pose a significant threat to the stability of the electrical grid, making interconnection with the grid a key bottleneck for the expansion of AI. Therefore, beyond the aforementioned boasting, there is some substance in those enormous figures that companies give. And what Nvidia says is backed by data. The big technology companies in the United States are taking over important technology centers. nuclear electricity production or with contracts with oil and gas companies. The coal is re-emerging in full decarbonization to feed the ‘gluttons’ data centers and we are seeing that this focus on LLM is leading large oil companies to give a turn in their plans to adopt renewable energies. AI needs fast energy capable of supporting those performance peaks, and renewables don’t seem like the way to go at the moment. Since we are dealing with grandiose figures, esteem that, between now and 2029, the world will spend about 3 trillion dollars (“its” three trillion) on data centers. And to give more context, it is what France’s economy was worth in 2024. Yeah Are we talking about a bubble or not?is another topic, but there are those who think that these ‘fanfare’ are very difficult to believe. Also who point that AI will have more impact than technologies so far, including the Internet, so we may need all that energy. Only time will tell. Image | İsmail Enes Ayhan In Xataka | While Silicon Valley seeks electricity, China subsidizes it: this is how it wants to win the AI ​​war

While everyone criticized GPT-5, Openai was winning the war that really matters: that of companies

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He GPT-5 launch It has been, in broad strokes, disappointing. Openai needed this model with this model bigger in the history of AIbut we have encountered a model that improves, but not spectacularly. And yet, it is achieving something that is more important than it seems: to convince companies. Companies

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