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We had been wondering for years why the Chernobyl wild boars were so radioactive. The answer was not in the accident

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Four decades after the accident at the nuclear power plant located in Prypiat, the animals of Chernobyl they continue generating fascination. These survivors in one of the most contaminated regions in Europe they surprise us in many ways, but if there is an enigmatic species in this place it is the wild boar. One of the most radioactive species from Chernobyl. Solving the mystery. In 2023 it appeared a new trackrevealed by a team of researchers, about these animals: we finally know why their radioactivity is greater than that of other species. The answer has less to do with the nuclear accident itself than with something that happened long before. More radioactive? There is very little we still know about the animals of Chernobyl. One of the most curious enigmas was that of wild boars. To understand why we have to talk about one of the most polluting radioactive isotopes, caesium 137 (Cs137). The half-life of this isotope (the time in which half of the atoms we have of the material will have disintegrated) is just over 30 years. The concentration of cesium in the food chain should in principle be reduced even further since the atoms tend to leach into the soil or be carried away by water into rivers. Going down. That is why the level of radioactivity in animals such as deer or roe deer has decreased significantly in the area. Not only has this situation not occurred in wild boar populations: their radiation levels have remained almost constant, that is, the decrease is not even in line with what the semi-disintegration of Cs137 would imply. It is the “wild boar paradox”. Nuclear tests and radioactive truffles. The answer comes from cesium 135. The team that solved this mystery did so by focusing not on the radiation levels but on its origin. They verified that it was this other isotope of cesium that was behind this phenomenon. Cs135 has a much longer half-life, which explains why the reduction had been smaller. This also makes it more difficult to detect the presence of Cs135. As explains the responsible team From the study, each type of nuclear incident has its own “signature.” It is estimated that 90% of the Cs137 present in Europe was released by the Chernobyl accident, but this is not the case for Cs135. The origin of this is 68% in the nuclear tests carried out in the context of the cold war. Just the right depth. The diet of wild boars has also been one of the key factors when it comes to understanding the reason for their radiation levels. These animals feed on a type of truffle (Elaphomyces) that grows in the subsoil, at depths of between 20 and 40 centimeters. As we pointed out before, part of the radioactive cesium It was seeping year after year into the soil of the area. At the rate of a few millimeters a year, cesium (both from nuclear tests and from the accident) has been advancing towards these depths, contaminating these mushrooms, a source of food for wild boars. From Chernobyl to Bavaria. The study that clarified this mystery was carried out by analyzing a population of 48 wild boars in the state of Bavariasouthern Germany. The analysis details were published in the magazine Environmental Science & Technology. In the long term. The results of the study invite us to think that the situation will not change in the short term. That is, it is unlikely that the radioactivity levels of wild boars will begin to decline in the coming years until they are equal to those of other similar animals such as deer or roe deer. The greater radiation present in these animals has made hunters resist their capture. This implies that the populations of these wild boars will go increasing in the future. Perhaps their expansion through central Europe will cause the radiation levels of these animals to decline generation after generation but, from what we have seen, this process could still continue for decades. In Xataka | When Chernobyl exploded in 1986, Spain was freed from the radioactive cloud. AEMET has now discovered that it did it for very little In Xataka | Some Spanish scientists are recreating the Chernobyl accident in Seville. Objective: see how it affects biodiversity Image | Joachim Reddemann / Кирилл Пурин *An earlier version of this article was published in July 2024

TikTok now has an answer for those who don’t want to see ads: check out

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Consuming social networks for free and without any type of advertisement is something that has been disappearing for years. Bombarding with advertising to later launch a payment model is something that applications like Instagram learned very well, and now TikTok is beginning to follow in its footsteps. TikTok Ad-Free. TikTok began testing a payment model back in 2023 in the United Statesan idea that did not spread beyond American territory. The company now makes official TikTok Ad-Free in UKopening the ban to expand it to the rest of the regions. How it works. The company has announced that, “in the coming months”, users over 18 years of age will be able to gradually subscribe to the new advertising-free option, TikTok Ad-Free. Those who continue using the free version will see no changes, and will see personalized ads. The price is £3.99 per month, in exchange for not seeing a single ad on TikTok and our data not being used for advertising purposes. It’s something that sounds familiar to us. Instagram Vibes. In 2024, Meta gave his ultimatum: either it was checked out or our information would be used for advertising purposes to show us the relevant advertisements. On Instagram they went a step further, since paying users not only got rid of ads: they got a verification badge and got more “love” from Instagram in terms of the visibility of their own account if they were a content creator. Why is it important. TikTok is in the crosshairs of the European Commissionas you consider your ad library to be non-compliant the Digital Services Law. The social network will have to be especially careful when implementing measures related to ads and data collection, even more so if billing is involved. In the same way, the fact that TikTok has given free rein to its subscription monetization model (although its application is not immediate), closes a circle of services that we use on a daily basis and that, whether we like it or not, force us to checkout if we do not want to see ads. And if not, Tell them to the paid version of WhatsApp. The big question. If you’re wondering when TikTok Ad-Free will arrive in Europe, the answer is that we don’t know yet. What seems inevitable is that this ends up happening, after the test in the United States and its progressive implementation in the United Kingdom. In Xataka | TikTok’s infinite scroll has just entered the EU’s crosshairs: Brussels marks it as “addictive design” and demands change

We had been searching for the origin of the most massive black holes for years. The answer is a cosmic carom of extreme violence

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All black holes They are the fruit of a very violent activity. However, there are some for which the known processes are insufficient. Now, an international team of scientists has discovered how the most massive black holes in the Universe form. It is a process so violent that it needs a huge star cluster to support it. Two groups of black holes. This team of scientists has analyzed the LIGO–Virgo–KAGRA Gravitational Wave Transient Catalog (GWTC4), which includes 153 detections of black hole mergers through gravitational waves. By analyzing all the available data focusing on the spin of black holes, they have seen that all of them can be divided into two large groups. On the one hand, black holes of lower mass, which arose from an ordinary stellar collapse. On the other hand, very massive black holes, arising from secondary mergers in the environment of dense star clusters. Okay, now that you understand. Generally, black holes are formed when a very massive star that has already run out of fuel collapses. This gives rise to an explosion in which the outer layers of the star are expelled, leaving only a very dense core. It is so dense that it generates a great gravitational pull and nothing can escape from it. On the other hand, there are such massive holes that do not fit with this process. They are believed to be second generation black holes. That is, two black holes they merge and then the result merges with another black hole, becoming much more immense. That would be the second group that has been detected in the GWTC4 catalog. Something doesn’t add up. This black hole merger process is so violent that, as soon as the first merger occurs, the result would fly away like a rocket For it to stay in place and merge with a third black hole, something is needed to retain it. These scientists have discovered that these are densely populated star clusters. There are so many stars in them that the gravitational attraction of all of them keeps the black hole still in place. And what does spin have to do with it? Spin is a parameter that refers to the spin of black holes. When formed in the conventional way, the spin is predictable and perfectly aligned with the star that gave rise to the black hole. On the other hand, when they are formed by a process as violent as these consecutive fusions, the spin takes a random direction, but a value predictable from the sum of the spins of the rest of the black holes. These scientists, therefore, saw that all the data coincided with that hypothesis: consecutive mergers in the environment of a very populated star cluster. A forbidden zone. On the other hand, these scientists found a forbidden strip of stellar size in which black holes could not form. There are small or huge ones, but not medium ones. Although this is something that was intuited, the complete set of data they have obtained gives a twist to what is known about the formation of black holes. Relationship with nuclear physics. As explained by these scientists, this detected mass limit seems to be related to a series of nuclear reactions that take place inside stars. Stellar nuclear reactions are nuclear fusion. Humans have learned to control nuclear fission, but it poses risks that would be solved if we also mastered nuclear fusion. Until now It is being a complicated challengebut perhaps these new findings, obtained thanks to gravitational wave analysis, could shed a little more light on this research. Everything adds up. Image | NASA, ESA, STScI and A. Sarajedini (University of Florida)/NASA, ESA, CSA, Ralf Crawford (STScI) In Xataka | What happens if you fall into a black hole, explained simply in an overwhelming NASA simulation

If the question is whether using ChatGPT or Claude in English is more efficient and saves tokens, the answer is: yes

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You may not have stopped to think about it, but there is a striking reality in the world of chatbots: It is more expensive to speak in Spanish with AI than to do so in English. The reason is simple: AI does not understand words, it understands tokens. And when you talk to GPT, Gemini, Claude or any other LLM, you talk to him in a language, but to understand you he first “translates” what you are telling him and converts it into tokens. And the problem is precisely that: that not all languages ​​”cost” the same in terms of tokens. There is a very simple example that we can analyze thanks to tools like ClaudeTokenizer: the word “developer”, which in English is “developer” costs few or many tokens depending on the language in which we write it and also (importantly) the version of the AI ​​model used. In the image it is clearly seen, but just in case, we summarize: For ChatGPT (GPT-4o and GPT-5) the word “developer” has three tokens (des-developer-ador), but the word “developer” only costs one. For Claude (Opus 4.7) the word “developer” costs no less than 9 tokens (2 in Opus 4.6), but “developer” costs “only” 6 (1 in Opus 4.6). What is happening here? Well then each language model uses its own “tokenizer”your “translator” from a conventional language to the token language that the language model understands. And those tokenizers favor precisely the languages ​​in which these models are created. This is how AI understands how we speak. Each word is divided into tokens, and English is understood much better. “developer” only costs one token in GPT-5, but “developer” breaks down into three. Bad news for Spanish speakers. In fact, English has become the official language of artificial intelligence, whether we want it or not. The reason is not cultural, but architectural: 95% of the training data of the frontier models (GPT-5, Gemini 3.1, Claude Opus/Sonnet 4.7…) are in that language. That makes the rest of the languages ​​”foreign languages”, and that makes it necessary to pay extra when using them, an almost invisible toll on every interaction. In practical terms, what happens when we use Spanish to talk to an AI model is simple: we use more tokens, and therefore using Spanish is simply more expensive than using English when working with a large language model. If you want to save tokens, better use English The question, of course, is how much more does it cost us to speak in Spanish than in English with ChatGPT (GPT 5.x) or with Claude Opus 4.7? It is difficult to say because each word and each phrase is a world, but the truth is that English is almost always the most “economical”. We have used one of the first sentences in this article to compare that token consumption, and by translating the sentence into different languages ​​and querying that token consumption for different models, the data is clear. It is important to highlight that these results are not conclusive, but they do make the trend clear: English is the most efficient language in terms of token consumption, but be careful, because Spanish is not that bad, and is usually the second most efficient. It is even more efficient than English in Gemini, at least according to the tool consulted. But on average, it is normal that there is a significant extra cost when using different models. A conversation with Claude Opus 4.7 is already “expensive” because it is one of the most expensive models currently, but in Spanish it is almost 30% more expensive, not to mention in Arabic, 76.3% more expensive. In fact, according to this example, the difference between Claude or GPT-4o in terms of efficiency is clear: OpenAI tokenizer is “cheaper”and although there may be differences with GPT-5.x, what seems clear is that Anthropic has preferred to “spend more” to obtain better results (or that is the objective). Gemini is even more thrifty according to these tests, and that may also have a lot to do with the quality of the answers, although that question is for another topic. We have used one of the paragraphs of this article in Spanish and translated it with Deepl into English, Arabic, Norwegian, French and Chinese to find out how many tokens the phrase “cost” in each language. English is undoubtedly the most efficient Tokenizers advance and evolve. Sometimes they do it to save us tokens, as happened with the GPT-4o tokenizer: at that time OpenAI explained how that tool used 1.1 times fewer tokens when speaking to her in Spanish but up to 2.9 times fewer in Hindi or 3.5 times fewer in Telugu. With Claude Opus 4.7, just the opposite has happened: the tokenizer has been redesigned and consumes more tokens (up to 1.35 times more, they admitted) with the aim of better processing and understanding the text. Your chatbot thinks (and programs) in English Here we must also highlight something important: although we can talk to our favorite chatbot in any language and it will answer us in that language (unless we ask otherwise), AI models “think in English”. That is to say: when you talk to them what they do is translate what you tell them and then reason in English and finally they translate their response into the language in which you were speaking to them. This consumes additional reasoning tokens, but also has some impact on latency (how long it takes to start thinking or answer the model). In complex tasks, this can clearly influence response times for the simple reason that the AI ​​model does not stop translating from “its official language” (English) to our language. This preference for English is also noticeable in the benchmarks: in the Humanity’s Last Examin which the models are asked all kinds of general knowledge questions with several options to answer, it is reasonable to think that the models They answer better in English because that exam is designed in that language. … Read more

If the question is how long do we have to use AI to become lazy, the answer is: a sigh

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Ten minutes. It is the time it takes for AI to have a negative effect on our ability to reason and solve problems, or at least that is what they have concluded in a new study in which they have measured how the use of AI assistants not only improves immediate performance, but also reduces persistence and worsens performance when we do not have access to AI. The study. Researchers from Carnegie Mellon, MIT, UCLA, and Oxford have published a randomized, controlled experiment that measures the impact of using AI on the ability to solve problems independently. In total, more than 1,200 people participated in three different experiments. The researchers’ conclusion goes in the direction of what we have seen in other previous studies: using AI enhances our productivity, but It has a cognitive cost. The experiment. A first experiment was carried out with 354 participants in which they had to solve twelve simple fractions. Some of the participants had a side panel with an AI assistant (GPT 5) that they could use to solve the operations. The curious thing came when their access to the chatbot was removed and they had to answer three more questions without the help of the AI. The result was that people who had used AI made more mistakes in their answers than the control group. The gray part of the graph was when the AI ​​assistant was retired. Fountain: AI Project Confirming results. The researchers did a second experiment in which they duplicated the participants (667) and did a pretest to measure the level. In addition, they added a “placebo” side panel (without AI) to the control group participants, so that there were no interface differences. The results again showed that people who used AI failed more than the control group. There was a third experiment in which reading comprehension problems were asked with 201 participants and the same thing happened again: when AI was removed, that group performed the worst. The key nuance. There is an important detail of the study and that is that they measured how the participants used AI. 61% used it to give them answers directly, while others used it to give them clues or clarifications. The results of this second group were more similar to those of the control group. On the other hand, those who asked for AI solutions as they were failed much more when it was withdrawn. This suggests what we have said above: the negative effect of AI on our cognition. It depends largely on how we use it. Copying answers without questioning is not the same as using them as support in the cognitive process. The new silly box. The fear that technology makes us stupid is not something that has arisen with AI, it happened with the calculator, it has happened with television, with video games and it is happening with cell phones. Although there are studies that point in that direction, there is no clear evidence that technology damages our cognition. However, it is also true that until now we had not had access to technology to which we could delegate all our thinking. Cover image | Xataka In Xataka | Young programmers no longer know how to program: AI is now causing the same thing that the calculator did half a century ago

In Norway they have asked themselves which are the best electric cars at -30ºC. And the answer is clear: Chinese cars

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A test that has already become indispensable for the industry. The Norwegian Automobile Club has been carrying out a simple test since 2020: they take the most representative electric cars on the market, fully recharge them and put them to the test. All at the same time and along the same route. Objective: discover if someone is lying. A simple test in theory. But it provides a lot of information for the buyer of an electric car. And although the WLTP cycles have been improved and now they show consumption in urban cycles and outside of it, the truth is that the buyer of the electric car needs one piece of information: the consumption on the road at the maximum legal speed allowed. And in the city, the consumption of electric cars is usually very low. Furthermore, the impact of total autonomy is less relevant because either the car is charged at night or access to the chargers is easier than in the middle of a road. That’s why he test carried out by the Norwegian Automobile Clubthe NAF for its acronym in the local language, is so important because they get the cars moving and take them on the road on a route that begins in Oslo and extends for more than 400 kilometers. The final intention is to glimpse what real autonomy these cars have and its difference with the figure recorded by the WLTP cycle. “They lie”. We will put it in quotes. And when companies design their cars, they obviously think about the consumption that a car will have in real situations but, of course, They take into account how the approval tests are carried out to get the best possible result. He Dieselgatewhere Volkswagen and other brands in the group used specific software when homologating their cars to achieve better consumption figures on paper that were then not met in practice, is the best-known case. But without cheating, it may pay off for a manufacturer to prioritize the lowest possible consumption in the city even if it later suffers from a slightly higher consumption on the highway. Or that the car behaves worse in extreme cold conditions, as is usually found in these tests. This very low urban consumption can lower the final average figure and distort the car’s real mileage, which is why these real road tests are interesting. How are they tested? In the test, the Norwegians examine the car’s behavior on a route that starts from Oslo towards the north of the country and which almost always runs on national roads. On the route, which you can see in this linkstarts at sea level and ends at about 750 meters above sea level. Along the way there are two large studs. In the first one you exceed 500 meters in height, then you descend slightly and climb again until you exceed 1,000 meters in height. Subsequently, you descend until you stay at the aforementioned 750 meters high. The test is also done in winter and summer conditions to get even more information from the cars. The driver stops when it detects a loss of power in the car but it doesn’t drain the battery all the way. This seeks to know to what extent the car is capable of moving at full capacity. In a year like this with very low temperatures, the first driver who abandoned noticed a loss of power when the car still had 11% autonomy left. And among the data published, the association also includes the weather along the route, specifying the minimum and maximum temperature or whether the sky remained clear or it snowed. This time record temperatures were reached, the warmest occurred in Oslo where the thermometer read -8ºC and the coldest was recorded while passing through Høyeste with -32ºC. The best. With this way of working, this Norwegian association has published its data. They take into account the deviation from the declared WLTP figure but also the percentage (doing 500 kilometers and deviating by 100 km from the expected range is not the same as doing 300 kilometers and deviating those same 100 km). Taking this into account, their data says that the best cars were the Hyundai Inster and the MG IM6, which performed 29% less than the expected range. The cars that deviated the least from the expected figure were the following: Hyundai Inster: distance traveled 256 km, WLTP distance 360 ​​km, difference 104 km KGM Musso EV: distance traveled 263 km, WLTP distance 379 km, difference 116 km Voyah Courage: distance traveled 300 km, WLTP distance 440 km, difference 140 km Changan Deepal S05: traveled distance 293 km, WLTP distance 445 km, difference 152 km MG IM6: traveled distance 352 km, WLTP distance 505 km, difference 153 km The worst. The data tells us one thing but it is also important to contextualize it. For example, they point out that the Lucid Air was the electric car that deviated the most from its expected autonomy (49%) but it was also the one that traveled the most kilometers (520 kilometers) so it was exposed the longest to temperatures below -30ºC. In fact, This same car was one of those that obtained the best figures in the last summer test. Last year, the organizers point out, the Polestar 3 broke the record in a winter test, stopping at 537 kilometers. However, they point out that in that same mountain pass where freezing temperatures have been reached this year, the thermometer that time marked a much more pleasant temperature of 8ºC. With all this, the cars that deviated the most from the expected figure were the following: BMW iX: distance traveled 388 km, WLTP distance 641 km, difference 253 km Tesla Model Y: distance traveled 359 km, WLTP distance 629 km, difference 270 km Volvo EX90: distance traveled 339 km, WLTP distance 611 km, difference 272 km Mercedes CLA: distance traveled 421 km, WLTP distance 709 km, difference 288 km Lucid Air: distance traveled … Read more

If the question is how much an employee would have to work to earn the same as a manager, we have the answer: a century

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The wealth gap between the richest and the poorest is skyrocketing around the world. There are people whose salary in a single year far exceeds what any other average job could earn by working their entire life. It’s not an exaggeration: it’s what the numbers show. A report of Oxfam Intermón and the International Trade Union Confederation (ITUC), analyzes the salary data of 1,500 large companies in 33 countries and quantifies the difference between what an average worker earns and what he earns a senior manager in Spain: That gap is no longer measured in years of salary, it is measured in centuries. A century of work to earn the same. According to data from the Oxfam report, in Spain, the general directors of the 12 largest companies in the country earned an equivalent average remuneration in 2025 98 times the national average salary. That means that an employee in Spain with an average gross salary in Spain of between 27,300 and 31,600 euros would have to work almost an entire century to accumulate what one of those senior managers earns in a single year. The data in the report is in line with what was included in the fourteenth edition of the remuneration report that published The Countrywhich stated that the annual salary received by the managers of Ibex 35 companies was 103 times higher than that of their employees. A gap that has become an abyss. The problem is that the gap is not only enormous, but it is widening every year and risks becoming an unbridgeable abyss. The average compensation of CEOs grew by 16% in the last year, while the average salary of workers in Spain it only increased 3.6% in 2025. At a global level the figure is not much better, since the real salary of workers globally fell by 12% between 2019 and 2025 due to inflation and wage stagnation. What happens in the rest of the world. The data from the report shows that, on a global scale, the situation is not very different, and the 1,500 highest-paid CEOs in the world earned an average of 8.4 million dollars in 2025, compared to 7.6 million the previous year. That represents an increase of 11% in real terms. For an average worker to accumulate that same salary, they would need to work 490 years non-stop. Meanwhile, the real salary what the worker receives average taking inflation into account, barely rose 0.5% between 2024 and 2025. That means that the highest paid executives improved their income 20 times faster than your employees. Real salaries, in free fall since 2019. The data on workers is worrying in itself, regardless of any comparison. Since 2019, workers’ real salaries have fallen by 12% worldwide, which is equivalent to having worked 108 days without pay between 2019 and 2025, 31 of them in the last year alone. Although the study shows that productivity per worker has grown by 51% since 2004, the part of GDP that goes to salaries has been reduced by 2 percentage points in that same period. Miguel Alba, head of Inequality and the Private Sector at Oxfam Intermón, pointed out that: “The remuneration of senior managers in large companies reaches exorbitant dimensions, very far from what ordinary people earn to cover living expenses.” Extreme wealth and a demand for change. The report also points to the growth of large fortunes as part of the same phenomenon. In Spain, the billionaire wealth It increased by 29.5% in the last year, representing 13.8% of GDP, distributed among 44 billionaires. In contrast, the average net wealth of Spanish households only grew by 3% between the end of 2022 and the end of 2024, according to data from the Bank of Spain collected in the report. On a global scale, among the largest beneficiaries of dividends in 2025 are Bernard Arnault, owner of LVMH, with $3.8 billion, and Amancio Ortega with 3.7 billion dollars (3,234 million euros). Faced with this scenario of extreme differences, Oxfam Intermón and the ITUC call on governments to limit the remuneration of senior managers, to tax the richest more fairly and to guarantee that minimum salaries are updated in line with inflation to ensure that employees do not lose purchasing power. In Xataka | Low salaries have ruined the job satisfaction of Spaniards: only 28.7% are satisfied with their job Image | Unsplash (Muhammad Sultan Ali, Ruthson Zimmerman)

The ocean fooled scientists with this “alien egg.” Almost three years later, we have the answer

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Although we try to learn a lot of information about the space that surrounds us, the reality is that there is still a lot to know here on Earth. This is what we evidenced in August 2023 when the Seascape Alaska 5 expedition, at more than 3,200 meters deep in the Gulf of Alaska, found a shiny golden hemisphere and with a hole in the center. And the question was clear: how did he get there? Many questions. When these findings were seen live, the researchers themselves joked that it looked like the beginning of a horror movie, and social networks did not hesitate to dub it the “alien egg.” The problem here is that the scientific community had no idea what that artifact was doing attached to a rock on the seabed. But three years later this mystery has been solved. It’s not alien. After being extracted from the seabed, the enigmatic specimen was sent to the laboratories of the Smithsonian’s National Museum of Natural History, where a research team set to work to determine what it was. And to the disappointment of many, it is not a specimen that came from outside our planet. The results, published a few days agorelate how the researchers decided to extract and sequence the mitochondrial DNA from the tissue and, from this, they crossed it with the large databases of genomes that are already known and in this way they ruled out that it was not a marine sponge, a bacterial biofilm and it was not an egg either. What was it? Here the genetic code pointed directly to a species that was cataloged in 2006 as Relicanthus daphneae and of which, if we look for a photograph, we will be surprised to see a kind of giant anemone of the depths with tentacles that can measure more than two meters. And this makes us wonder: why did the Alaskan specimen look like a smooth, golden sphere? And here the research team points out that the golden orb found in the deep sea was not an animal itself, but a “cuticular relic.” What exactly is it? In other words, these are the remains of the base or “foot” that this anemone uses to anchor itself to the rocks of the seabed, resisting the strong abyssal currents. In this way, when the anemone dies, it detaches or moves; this fleshy and resistant base is left behind. And the hole? This was a point that greatly worried researchers in 2023, but the reality is that it was not the hatching mark of a creature, but rather it is simply a natural tear in this residual tissue. The curious thing here is that this find also fits with another similar specimen collected in 2021, confirming that this golden “mold” is a common trace of the species after its death. Images | NOAA In Xataka | We have drilled the seabed at a depth of 2,500 meters. And we have found things we didn’t think were possible

If the question is how many websites has AI generated, the answer begins to explain the new internet

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Creating a website has never been just one thing. For years, for many users it meant choosing between fighting with tools like FrontPage, hiring someone who knew how to design, or settling for other types of solutions. Later, templates and visual editors began to gain ground, lowering the barrier to entry. Now we are witnessing a new change thanks to tools such as Lovable either Vercel v0which promise to turn a description into something publishable in just a few minutes. The AI ​​leap. The intuition that AI is gaining weight in the new web already has a concrete figure on the table. This is what the study points out “The impact of AI-generated text on the Internet“, signed by researchers from Stanford, Imperial College London and Internet Archive. The work places the percentage of new websites analyzed classified as generated or assisted by AI at around 35% by mid-2025. Before the launch of ChatGPTat the end of 2022, that percentage was zero in the study sample. The speed of change, rather than the isolated data, is what makes it relevant. How they measured it. To arrive at that figure, researchers worked with the Internet Archive and analyzed monthly samples of sites between August 2022 and May 2025. In each case they searched for the oldest archived copy available on the Wayback Machine, downloaded the HTML, and extracted the text for processing separately. They then tested several detection tools and chose Pangram v3which was the one that offered the highest detection rate in its tests. Some of the pages published by the Lovable community The result. The research found a website with “a decrease in semantic diversity and an increase in positive sentiment.” Do you mean that all this is positive? You can depend on the angle at which you look at it. The same text warns that “as AI text becomes more common on the Internet, the range of unique ideas and diverse points of view is reduced.” An expanding industry. What the study shows has not appeared out of nowhere. An industry of its own is being consolidated around this promise of creating a website with less friction, with tools designed for very different users: from those who need a simple page for a business to those who want to prototype an idea quickly. Wise Guy Reports Data They place the market for tools to create websites with AI at 3.1 billion dollars in 2024 and project it to reach 25 billion in 2035. The direction of travel seems clear: publishing is becoming increasingly accessible. What’s coming. In web creation, AI is already moving pieces, and professional design does not seem to be immune to that change. That doesn’t mean it’s going to put an end to web designers or that all projects can be solved with generative tools. There are products, brands, stores and services that will continue to need criteria, architecture, design, maintenance that is less semantically diverse and more positive overall, and a technical layer that is not so easily resolved. However, it makes sense to think that professionals will also end up relying on these AI tools to speed up parts of the process. Images | campaign In Xataka | Kimi Code is eight times cheaper than Claude Code and does 75% of your work. The question is whether it is enough

If the question is whether the rich are born or made, the answer is condensed in a graph that shows that Spain is different

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Globally, the distribution of wealth is not only measured by how much money the richest have, but also by the economic flow and what it is like. the architecture of success that each country has built. The balance between “own merit” and “cradle” defines the identity of an economy: while in some countries they function as innovation laboratories where fortunes emerge from nothing, in others they function as a kind of safe deposit box where heritage is transmitted from generation to generation like a modern noble title. This chart from the German economic data analysis platform DataPulse and is made from Forbes data for June 2025. At that time, the business magazine counted 2,838 billionaires around the world. Forbes ranks each using its own scoring system (Self-Made score), which ranges from 1 to 10 according to the weight of the inheritance versus one’s own merit. The overall result is clear: two out of every three millionaires are millionaires because they “made themselves.” But this statement hides abysmal differences that reflect how economic power works in each society. By the way, a global fact that the graph itself highlights: between 2024 and 2025 the total wealth of all the billionaires in the world grew by 13.4%. According to the UBS Billionaire Ambitions Report 2025that growth pushed aggregate wealth to an all-time high of $15.8 trillion. Wealth: Self-made vs. inheritances. Data Pulse with data from Forbes Where does the fortune of the world’s richest come from: inheritance or self-made? The upper area of ​​the graph is where those countries are located where it is easier to get rich on your own and is led by Russia and China: both appear with 97% of billionaires self-madethe highest percentage in the world. They may be entrepreneurial countries, but the true differential feature must be found in their history: their respective revolutions of the 20th century They destroyed any inheritable private capital (the Bolshevik in 1917 and the Maoist in 1949). So technically, their fortunes are first generation because they couldn’t be from any other. However, this small print also includes Forbes’ conception of Self-made: In the Russian case, the main oligarchs accumulated their wealth in the 90s by taking advantage of Yeltsin’s savage privatizations. He Harvard’s Wilson Center says it loud and clear: It was one of the largest transfers of public wealth into private hands in modern history. Calling it self-made is at least generous. Although the United States is the country with the most millionaires in number with almost 924 people and according to the UBS Billionaire Ambitions Report 2025 74% of them are self-made, not the one that appears higher in the graph. The United Kingdom, Canada and Israel stand out there. What they all have in common are economies with developed capital markets, active venture capital ecosystems and legal frameworks that facilitate the creation and scaling of companies. In Germany, France or Spain inheritance rules. The Western European bloc is the area where inherited wealth weighs the most, with Germany as an extreme case: only 25% of its rich people are so because they built their own fortune. Family Capital explains it quite well: the ten largest German assets are all linked to family businesses. There are no great new generation technological fortunes. What there are are “old-fashioned” names, such as the Quandts at BMW, the Albrechts behind Aldi or the Würths: post-war industrial dynasties that have passed down their empires from generation to generation. Spain and France embrace a similar logic: they have legal frameworks that strongly protect intergenerational wealth transmission, scarcity and/or weakness of a technological ecosystem comparable to that which exists in the Anglo-Saxon or Asian ecosystem, and a business culture where family control of capital is considered a value in itself. Just above Germany is Spain, which has second place in the world in percentage of inherited wealth, with 74% of its billionaires in that category and only 26% self-made. Although there is the occasional green shoot of a modernized economy, it is residual: Spanish wealth is historically concentrated in a very small number of families with dominant positions in sectors with little competition. In short, generally In Spain wealth comes from dad. As in Germany, the names in the Spanish state are great classics: the Ortega family with Inditex, the Del Pino with Ferrovial, the March, the Entrecanales or the Lara. They are fortunes built for the most part during the Franco regime or the transition, in a context of little competition, privileged access to credit and close relations with political power. The result is what the graph shows: a country where becoming a billionaire from scratch is statistically almost an anomaly. In Xataka | We thought that millionaires had their fortune rain down from the sky without the slightest effort: Spain is different In Xataka | The “Great Transfer of Wealth” is not only a thing for the rich: demographic change will concentrate wealth among the youngest Cover | DataPulse

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