DeepSeek wants to raise its first round of financing and copies the last thing that remained to be copied from the US: the economic model

Chinese AI startups appear to have surrendered to Silicon Valley capitalism. Both DeepSeek such as Moonshot AI (Kimi) have begun to raise investment rounds or are preparing to do so. It is a turning point in a race that is now becoming especially interesting and that also raises a clear question: will these companies continue betting on open models? The valuation is multiplied by two. DeepSeek had always avoided making that decision and it seemed almost a personal project of its founder, billionaire Liang Wenfeng. However, the company is now in talks to raise its first round of external investment, they assure in Financial Times. According to company data, Wenfeng has 89.5% of the stake in the company. There is talk of a round that would increase DeepSeek’s valuation from the current $20 billion to around $45 billion. Who is the “Big Fund”. Behind this investment round is above all the China Integrated Circuit Industry Investment Fund, also known as the “Big Fund”. This consortium, the most important of its segment in the field of semiconductors, is supported by the Chinese state, and has a “cash” of 47 billion dollars contributed by the Chinese Ministry of Economy, the local government and several state banks thanks to a third round that was carried out in 2024. At the moment the “Big Fund” has not invested in other Chinese AI startups, but it has in companies like SMIC or Yangtze. The war for talent. The reason behind this decision is not only the need for capital to have access to more computing capacity. According to sources close to the operation, Liang Wenfeng has been forced to open that option to stop talent theft and thus be able to keep their best researchers on the payroll. In a market as competitive as this one, DeepSeek needs to offer shares to its employees to compete with the aggressive recruitment of talent by its local and Western rivals. A promising pairing. The relevance of this investment goes beyond the AI ​​model as such. DeepSeek has been significantly optimized for be able to run on Huawei hardwareallowing China to have a platform that works without the need for Nvidia chips. This symbiosis between this efficient AI model and the Chinese hardware giant is quite a bet by the Chinese government to try to win this race despite Washington’s blockades. The forced bet on “national” chips. Seeking that support in Huawei chips is not only a technical choice, but a political necessity for survive NVIDIA GPU crash. The problem is that Chinese hardware is still struggling to close the raw performance gap against architectures like Blackwell’s. If DeepSeek’s software hits a ceiling and chips created in China do not evolve at the necessary pace, the laboratory could find itself trapped: it would not matter to be very efficient when they cannot compete in raw power. Moonshot signs up for the rounds. DeepSeek is not alone in this race to achieve huge valuations. Moonshot AI just got up 2 billion dollars from investors such as Meituan, raising its value above 20 billion. Meanwhile, other rivals such as MiniMax and Zhipu AI (GLM) already surpass the 30,000 million valuation in their stock market debuts. This trend is therefore following what was already experienced (and continues to be experienced) in the US with AI startups, and the capital bubble that exists in the North American country now seems to have its eastern version in China. Moonshot AI and exceeds $200 million in annual recurring revenue (ARR). The paradox of copying the economic model. It’s ironic that DeepSeek, which became famous for challenging the “brute force” of American spending, ends up adopting its same funding structure. The company has shown that efficiency could offer an alternative to those almost unlimited resources of venture capital accessed by OpenAI or Anthropic. However, market reality dictates that a very solid capital structure is still what is needed to survive in the long term. Either you have it, or you can’t continue training models, reserving computing capacity and, of course, retaining talent. Open models? Until now DeepSeek had been one of the heroes of open weight AI models. Thanks to this, platforms like Hugging Face allow you to download it and allow everyone to take advantage of its achievements in terms of efficiency. The entry of venture capital and state funds could change the rules of the game: investors do not usually inject billions of dollars so that the product ends up being “given away” even for its competitors. The company will probably face the dilemma of closing its next models to protect its valuation and generate exclusive income, or keep its philosophy open at the risk that its investors no longer trust that strategy. In Xataka | If at some point NVIDIA has to choose between giving its best chips to the US or China, its choice is very clear.

Chrome has always liked to gobble up RAM. Now download a multi-gigabyte AI model without warning

Chrome is part of the digital routine of millions of people to the point that we often stop wondering what exactly it does while we browse. We use it for almost everything, we trust it with sessions, extensions, passwords, searches and a good part of our life on the Internet. That is why it is so surprising to find a folder larger than 4 GB associated with an AI model downloaded by the browser itself. We are not talking about a minor update or a residual file, but rather a large component that many users probably did not expect to see there. The conversation began to take shape from a publication by Alexander Hanff in That Privacy Guy. Their finding, in essence, was simple to understand: according to its logs, Chrome had left a multi-gigabyte AI model on his computer without giving him a clear warning during the process. From that clue I did the checking on my own computer, used from Spain, and found the same folder that Hanff refers to: OptGuideOnDeviceModel, within Chrome’s internal files. In my case, macOS shows that folder as 4.27 GB in size, even though features like the Gemini sidebar are not yet available in this market. Gemini Nano downloaded to my computer Gemini Nano It does not work like a traditional download that we search for, accept and install manually. In the Chrome developer documentationthe company explains that the integrated AI capabilities are intended to be fluid and that model management is done automatically in the background. It also notes that the initial download can be triggered when an AI feature built into the browser needs to use the Gemini Nano for the first time. In other words: the model can reach the computer as part of Chrome’s internal workings, not necessarily through a clear and recognizable action for the user. An AI model that goes beyond an integrated chatbot The model is not limited to promoting a browser with a chatbot integrated within Chrome. Google has already described uses Gemini Nano on the device itself to detect technical support scams, a type of threat that often lasts a very short time online and can escape traditional tracking systems. In that scenario, Chrome can provide the model with content from the page the user is visiting to extract risk cues. AI, therefore, can also be part of the browser’s security layer. Gemini Nano also boosts security features in Chrome That’s where a good part of the discomfort lies. AI in the browser can have reasonable uses, from helping detect fraud to powering writing, translation or summarization functions, but the problem arises when the user does not fully understand what has been downloaded, why it is there and how they can manage it. Hanff sums it up with a very direct criticism: “Chrome didn’t ask. Chrome does not show it to the user. If the user deletes it, Chrome downloads it again.” There are also voices that reduce the seriousness of the case. On Reddit, a user defended that the model is only downloaded when someone tries to use an AI function that needs it and that it can also be disabled from the Chrome options. Hanff responded that his logs showed otherwise: the browser opened on schedule, stayed on a page for a few minutes without interaction, and still left a trace of the download. Beyond that specific discussion, Google’s own documentation points to a middle ground: the download can be triggered by built-in functions and continue in the background even if the tab that started it is closed. Chrome does offer controls to reduce the presence of some AI features, but it doesn’t concentrate everything in a single, easy-to-understand panel. From settings can be disabled or hide certain visible pieces, such as Gemini in the markets where it is available, typing assistance, search history or AI-powered search. To go deeper, however, We must enter more technical terrain, such as experimental options from chrome://flags. This jump changes the experience quite a bit: we are no longer talking about turning off a clear function, but rather touching internal parts that may also be linked to features that the user may want to keep. Firefox offers an easy way to disable AI features Firefox offers an interesting counterpoint because Mozilla has grouped its AI controls in its own section within the settings. Since Firefox 148, that section is now available as “AI controls” and allows you to block current and future improvements from a visible place, without having to chase options spread throughout the browser. It also separates specific sections, such as on-device AI, translations and chatbot providers in the sidebar. It is a more direct approach: the user not only sees that these functions exist, they also better understand what they can activate, block or leave available. The arrival of Gemini Nano to Chrome is part of a broader movement: browsers want to become more than just a window to the Internet and start executing AI tasks within the computer itself. That direction can have real advantages, especially if it serves to strengthen security or make some functions more agile. But the case also leaves a visible panorama. Some users won’t mind at all that Chrome downloads local models automatically; others, instead, they will want to knowunderstand what it is for and have room to decide. Images | Xataka with Grok | Screenshot In Xataka | It doesn’t speak, it doesn’t climb stairs and it doesn’t even always obey: this is the robot that the creator of the Roomba has been wanting to develop for 30 years

Mistral has a new AI model. The good news is that it is absolutely European; the bad one, which is absolutely mediocre

The French startup Mistral has just launched Mistral Medium 3.5an open-weight AI model that is the great European exponent in an industry absolutely dominated by China—which competes directly with this type of projects—and by the US. And if this is the best they can do, it seems Europe has a problem. Mediocre. This is a “dense” model with 128 billion parameters and a context window of 256,000 tokens. While models with Mixture-of-Experts (MoE) architecture only activate a subset of the total parameters to achieve enviable efficiency and capacity, Mistral activates them all. That makes it much less efficient, but theoretically it should make its performance promising. And that’s the problem. Which it is not. Benchmarks. Pedro Domingos, professor of deep learning at the University of Washington, he expressed it very well: “Mainstream AI companies brag about how their model is much better in benchmarks. Soo Mistral brags about how their model is much worse.” It is true that the models with which it is compared are larger in total number of parameters, but as we will see later, even taking that into account, they are cheaper and theoretically more efficient thanks to the use of that MoE architecture in many of them. The model, however, unifies the previous catalog and follows the market trend of being able to establish the desired level of reasoning (reasoning_effort) as a parameter. Bad results. And he is somewhat right: Mistral does not seem to have problems showing the results of various benchmarks in which it performs poorly, but it also performs poorly with models that are by no means the most recent or powerful on the market. Thus, it is compared with Claude Sonnet 4.5/4.6, with Kimi K2.5, with GLM-5.1 or with Qwen 3.5 397B. In almost all cases (except GLM 5.1) there are already more recent and powerful versions of all of them. Not so far from local models. In fact Medium 3.5 scored 77.6% in SWE-Bench Verified, a programming test in which Qwen3.6-27b It reaches 72.4% with a fundamental difference: you can run it “for free” (with the appropriate hardware, and you paying the electricity bill) with a relatively affordable machine. More expensive (and somewhat more restrictive). If we use it via API, Mistral Medium 3.5 costs $1.50 per million input tokens and $7.5 per million output tokens. GLM-5.1 costs 1.4/4.4 respectively, and Kimi K2.5 costs 0.5/2.8 respectively. Its recent successor, Kimi K2.6, costs 0.95/4, and it is significantly better than Mistral being cheaper. There is a curious fact: Mistral uses a “modified MIT license” instead of the traditional Apache 2.0, and indicates that this model can be used commercially or non-commercially except for “high-income” companies. Chasing Anthropic. In addition to the model itself, the company has presented the so-called remote scheduling agents using Mistral Vibe CLI to, for example, send pull requests to GitHub in an automated way. It also has the so-called “Work Mode” in LeChat, allowing multi-step tasks to be managed autonomously. These are tools clearly intended to strengthen Mistral’s role as a base for scheduling agents, which is the path that has worked fantastically for Anthropic. Your advantage: being European. The only great strength of this model is that it has been developed by a European startup, and that gives it clear visibility at a time when many EU countries they talk about digital sovereignty. It is the only Western model that seems to want to compete with China in the field of open weight models, which is good news, but the truth is that in terms of performance it does not seem that the Mistral Medium 3.5 is going to perform competitively. The geopolitical security network. That, together with the fact that it costs more than its competitors, makes the decision to use it difficult unless for those who prioritize clearly that European origin. That is Mistral’s ace in the hole, and they are taking advantage of perfectly. The company has recently obtained financing to create data centers in Europe, and is nourished and fed by this new obsession with minimize dependency of North American Big Tech. In Xataka | The CEO of Mistral sends a message to Europe: the end of being the technological vassal of the United States

The superapp model that dominates in China never caught on in the West. something is changing

Superapps are mobile applications that offer many unified services, from messaging to mobile payments and much more. In Asia, especially China, They are the default formula that has been successful for years with apps like WeChat, Meituan or AliPay. In the West we are more into specialized apps, but the market is beginning to show clear signs of approaching the Chinese model. The Uber case. Uber just announced the integration of hotel reservations in your app through its alliance with Expedia. In this way, in the same app we have car reservations, food delivery and hotel reservations, a solution that is quite similar to the model of a Chinese super app like WeChat, which integrates all types of services under one umbrella. Uber’s goal is that, by offering more services, the Uber One subscription will be more attractive to consumers and thus increase its income. An important detail: Uber CEO Dara Khosrowshahi was previously CEO of Expedia, so this alliance does not seem coincidental. TikTok Shop. Uber is not the only one that is following this strategy, there are other proposals that also point in the direction of consolidation. We have the clearest example with TikTok and the integration of the marketplace. ByteDance has managed to export a very Asian model: see a product in a video and buy it without leaving the app. TikTok Shop has been in Spain since the end of 2024 and, at the end of 2025, there is already a TikTok account more than 12,000 stores operating on its platform. The adoption data is positive, but the model is still very far from the penetration it has in China. There have been attempts. The creation of a super app that succeeds in the West was Elon Musk’s obsession when he bought Twitter. The bet did not work out and today X continues to be what Twitter was: a microblogging social network. PayPal also tried its superapp version integrating hotel reservations with little travel. Years ago there was talk that WhatsApp could be the WeChat of the West, but despite having been adding functions, it is still a messaging app. Looking to the future, we have the case of ChatGPT and its path to a super app that integrates the chatbot with the Atlas and Codex browser. Why in China yes and here no. It is not a question of simple preferences, but has a structural explanation: Internet penetration in China was much slower and, in some ways, skipped the era of the personal computer. While Western consumers came to the smartphone with already formed habits (a browser to search, an email program, an online store), the Chinese did so directly from the mobile phone. By not having already created habits, this made the creation of these “everything apps” much easier. Likewise, the penetration of credit and debit cards was also slow and many consumers switched from cash to mobile payments, hence apps like WeChat or AliPay have become the default standard for paying everywhere. Another factor that plays in favor of the adoption of these apps is that they had no competitors. With the entire Google and Facebook suite blocked by the Chinese government, these apps did not have to compete, but rather filled a void. And of course there is the regulatory issue and institutional support. in China you can pay taxes from WeChatapply for a business license or pay a traffic fine without leaving the app, because the Chinese government actively integrated its public services into these platforms. In the West, the merger between a private company and the State would generate immediate political and regulatory scrutiny. something is changing. On the one hand, the perception we have of China from the rest of the world has been changing in recent years. The success of TikTok, the Labubu, the popularity of electric cars… are symptoms that China has become a cultural reference and technological. This opens a new opportunity for success. On the other hand, there is a new variable: AI. The arrival of AI tools is already changing our information-seeking habits and has the potential to function as a layer on top of everything we already use, connecting services that previously lived separately. Image | IlgmyzinUnsplash In Xataka | The US has made an almost total commitment to enormous AI models. China is showing another way

Now you have an omnipotent model that reads, sees and listens. Everything at once

Eight years ago, when Nvidia was still a company that made graphics for video games, the company pointed out to something that is starting to enter the conversation: physical robotics. They are the robots with artificial intelligence integrated to behave autonomously. Like a ChatGPT with arms, ears and eyes. It has rained a lot since then and It’s now when we’re starting to enter that future. However, Nvidia has continued to experiment with that way of making the physical and digital worlds converge, and its latest product is the Nemotron 3 Nano Omni. An AI model that sees, hears and reads the physical world. Omni Models. These models are multimodalbut in a much stricter sense. While the models we use every day require separate channels to process and generate audio, text, image and video, an omni model is designed to be inherently multimodal. This implies that they use a unique neural network architecture trained end-to-end so that the interaction between models and stimuli is more natural, faster and capable of recognizing more nuances. An example is an AI that can “see” what a camera captures, analyze the entire situation and give feedback to the user more quickly than one that can do the same, but whose text model has to ask the video model what it has seen and then generate the content. In even fewer words: it better imitates the way humans perceive and respond to the stimuli of the world. Integration. And that’s what Nvidia affirms What Nemotron 3 Nano Omni can do. In the same architecture, it is a model that integrates vision, audio and language capabilities to eliminate the fragmented workflow of current AI agents. According to the company, it is built on a hybrid architecture of mixing experts (AIs trained in various subjects) with 30 billion parameters, of which 3 billion are for inference. It has been designed as a model that is nine times faster than separate models and has three times the performance of other open omni models, consuming 2.75 times less computing power in tasks such as reasoning from a video. Okay, but why?. That is the key question, beyond the numbers and the raw capabilities of this technology. The use cases detailed by the company are the following: Agents: power those agents that navigate graphical user interfaces, reasoning based on the content on the screen and understanding what they are seeing in real time and persistently. The native input resolution is 1920 x 1080 for that HD visual understanding. Documents– Interprets graphs, tables, documents, screenshots, and mixed media inputs. Comprehension audio and video: is able to understand what he sees and hears to maintain consistency in his interpretation instead of reasoning based on disconnected models. For professionals. What is clear is that Nemotron 3 Nano Omni is not something that is launched with the goal of being something for the masses like other AI models that we see every day. Nvidia focuses it on something business, a tool that can be accessed through platforms like Hugging Face and to be deployed on local systems such as DGX Spack or Jetson. That is, it is not something available to everyone. The interesting thing is that it is a technology that is strongly pushing the narrative of agents as omnipotent entities, and it fits with the speech latest from Jensen Huang, CEO of the company, that AI will not come to take away our jobs, but to ‘micromanage’ us. Image | Nvidia In Xataka | There is a company that has grown 3,000% in the stock market, even beating the performance of Nvidia: Sandisk

For generations, we Spaniards embraced the three-course menu. Now that model has entered into crisis

Christianity has its holy trinity. The theater has its classic structure in three acts, just like the traditional novel. Even life itself can be divided into three blocks: youth, adulthood and old age. For a while (centuries, actually) food also participated in this obsession with triads. When you sat down to eat, whether in your own home, that of a family member or in a bar, you expected to be served three courses: something light to start, like a soup or a salad, a heartier second and dessert to finish the job. Now that model has gone into a spin. Goodbye to three dishes? That is the reflection that left bouncing a few days ago The Country in its section on food: after generations and generations settled in homes and hospitality, meals structured in three courses (first, second and dessert) are in decline. He is not the first to point it out. More than a decade ago it already launched a similar warning Adam Liaw, a chef, presenter and author of gastronomic books who in 2015 warned in Guardian about the gradual “disappearance” of three-course menus. Even Dr. Nicolás Romero issued a warning in 2019, in an interview with The Basque Journal: “We should start by recovering a custom that we are abandoning in Spain, that of three dishes on the menu.” He was so convinced of this that he even encouraged transferring the same formula to dinner, “as the Mediterranean diet dictates”, opening the menu with vegetables and closing it with fruit. Is it really in crisis? It is difficult to find studies that confirm this, but, as Liaw signalif we do not look at our surroundings we will realize that the meal in ‘three acts’ seems to have “fallen from favor”. And that is something that can be transferred both to our homes and to restaurants. In fact there are those who now slide that menus with starters, main courses and desserts risk becoming something extraordinary, a luxury reserved for weddings, New Year’s Eve or other special occasions. Just like silverware or old wine. And why this change? The explanation varies a little depending on whether we are talking about what we do at home or what happens in the hospitality industry, although in both cases a common denominator can be seen: a change in consumer habits. In an increasingly busy society we are less willing to spend hours between the stoves, selecting fresh food…or even sitting in front of a plate, which explains the growing success of snacks. Cooked less? It seems so. In 2003, experts were already warning that, in a matter of a few years, we Spaniards had reduced three hours a week the time we spend cooking. Other surveys most recent show that 48% spend about 90 minutes cooking and 41% barely spend more than 60. There are still the majority of those who prepare their own food, but the Spaniards who barely set foot in the kitchen They are counted in millions. With less (or no) time between pots and pans, it is difficult to prepare meals divided into several dishes. Does everyone lose? “Households are spending less and less time cooking, reducing processes and complexity to optimize the time spent cooking. This implies that people are increasingly opting for single-course occasions, which are 71.3% of the time at dinner and 55.7% at lunchtime,” commented recently Eduardo Vieira, from Worldpanel by Numerator (Kantar), who pointed out that this represents an “opportunity” for the industry. Our tendency to spend fewer hours in the kitchen is giving wings to a business that has been growing for years: that of pre-cooked and ready-to-eat foods. The Spanish Association of Prepared Meal Manufacturers (Asefapre) estimates that in 2025 the consumption of precooked meals in the country’s homes grew by 3.8% and that sales exceeded 4.3 billion, with a growth of 5%. What happens in restaurants? There another extra factor comes into play: the economy. Although the menu of the day has been implemented for decades in Spain, where it is quite an ‘institution’, the formula is in crisis. And not only because of cultural changes or the snackficationa trend that leads us to spend less and less time on food. In recent years it has come under cost pressure. The rising cost of raw materials, energy, labor… has forced hoteliers to review their rates, increasing them by 19.5% between 2016 and 2024. The problem is that the sector assures that this increase is lower than the CPI, which makes it difficult for them to make their menus profitable. “It is in danger, fortunately because it is not a sustainable model,” recognize to The Country Paco Cruz, The Food Manager. Given this situation, it is necessary ‘reinvent’ the menucutting costs. As? Exactly: putting the scissors in and leaving it on a single plate. Do more factors influence? Yes. As if the above were not enough, the hoteliers have to deal with a new rival: the merchantssupermarkets that, like Mercadona, have a wide range of ready-to-eat dishes and spaces in which to consume them. Customers can often choose dishes and devour them in just a few minutes, putting pressure on traditional menus where a waiter serves starters, mains and dessert. Images Michael Clarke Stuff (Flickr), Diogo Brandao (Unsplash)F.arhad Ibrahimzade (Unsplash) In Xataka More and more Spanish bars refuse to let you pay at the table. Its objective is very simple: greater rotation

We already know what happens to the GPU hourly price when OpenAI or Anthropic launch a new model: it doubles

This week, an analyst named Tomasz Tunguz published in X two revealing graphs. They show the evolution of what it costs AI startups to access cloud computing, and there is bad news. The cost of renting the NVIDIA B200 GPUs with Blackwell architecture has gone from $2.31 per hour in early March to $4.95 per hour this week. It is an increase of 114% in just six weeks and it has a clear cause: the arrival of new models from Anthropic and OpenAI. What the graphs show clearly. Those charts focus on the price index of Ornna cloud computing trading marketplace. The first of them covers the price of renting the B200 chips from the end of 2025 until today, and there are vertical lines showing each release of the latest models from OpenAI and Anthropic. The correlation is almost perfect: GPT-5 Codex, Claude 4.5, GPT-5.3 Codex, Claude Opus 4.7 and GPT-5.5 coincide with a jump in price indices. Every time these companies announce a new version of their frontier models, demand skyrockets, and so does the cost. If you want the best, pay (much more). The second graph shows the price difference between renting the previous generation of chips, H200 with Hopper architecture, and the new B200. The historical average of that “spread” is $1.06, but now it stands at $2.09, practically double. That means buyers—startups and AI companies—are paying a record premium for the extra memory and superior computing power of Blackwell architecture chips. Accessing the latest of the latest was already expensive. Now it is even more so. This also makes the H200 in a second class option for the most demanding models of 2026. Action and reaction. There is overwhelming logic here. When OpenAI or Anthropic release a new model, there is an explosion in inference. Developers and companies want to test them as soon as possible and integrate these models into their products (or compete with them). To do this, they need computing quickly, and a simultaneous demand is caused that unbalances the available inventory in the market for renting AI chips by the hour. The problem is that the supply of B200 does not grow at the same rate. Some companies have wanted to anticipate, and we have the perfect example in Google. He has bought all the B200s he can, and that has made these GPUs around now the 500,000 dollars on the secondary market according to analyst Jack Minor. The irony of efficiency. The curious thing is that the more efficient these chips are – and the B200s are – the more companies want to rent them at the same time to take advantage of those efficiency advantages that should lead to cost savings. What actually happens is that the scarcity of these advanced chips cancels out any theoretical savings. Long term contracts. Startups and companies that think in the short term are especially harmed in this area, because they face price jumps that are increasingly difficult to assume. Companies that signed computer rental contracts at the price then can now operate at less than half the cost of their competitors. Thinking in the medium or long term seems reasonable, although once again those who win are the hyperscalers and those companies that have managed to get hold of many B200s. And who wins even more is of course NVIDIA, which cannot cope. Few alternatives. In other markets such as energy or metals there is usually room for maneuver, Tunguz points out, but the same is not happening at the moment in the AI ​​segment. In the oil market, for example, if the price rises 114% in six weeks, companies can buy futures, options or fixed-price supply contracts to protect their margins. In cloud computing rental, those options are much more limited. And the result is a much more volatile segment. This will go further. We are probably facing a peak in demand that will be followed by a correction: the new batch of B200 chips that arrive in the second half of 2026 are expected to cause a drop in current prices. However, that $4.95 is now the new floor, not a peak, because demand for AI computing will continue to grow faster than TSMC’s production capacity. In the absence of the supply of AI chips growing significantly – and there are certainly movements that are trying to achieve this, such as those of Google with its TPUsAmazon with its Trainium or Huawei with its Ascend—, the problem will still be there. In Xataka | Europe is taking its technological independence so seriously that it is aiming for the most ambitious goal: NVIDIA

In 1972, a Swedish model posed nude for ‘Playboy’. Years later, we have the JPEG format thanks to this

The one of Lena Sjööblom It is one of the most delirious races in the history of technology. To begin with, because when she made her mark in the sector she was not an engineer, nor a mathematician, nor a physicist, nor anything that resembled her in the slightest. Nor did it have any known “Eureka” moment nor did it contribute any discovery or invention. No. Sjööblom was a model. From a model she became what was then known as a “Playboy girl.” And from the pages of the nude magazine he jumped to the front-line research that today, half a century later, allows us to enjoy the JPEG image format. Let’s go in parts. In the early 70s, Sjööblom, a 21-year-old Swedish immigrant Recently landed in the US, she made a living as a model. To make her way and probably without the slightest idea of ​​the journey her image would end up taking, at the end of 1972 she agreed to pose nude for Playboya magazine that at that time sold millions of copies around the world. In one of the central photos that he took of him Dwight Hookerone of the most famous portrait painters of the city, appears from behind, in front of a mirror, with no clothes other than a hat, a red boa, stockings and heels. I liked his work. A lot. At least that’s what we can deduce if we take into account that the November 1972 issue, in which Sjööblom was the playmate main feature and Pamela Rawlings was on the cover, sold 7.16 million copiesmaking it the most successful in the magazine’s entire history. The pose became so famous that in 1973 Woody Allen He even snuck it into one of his movies. As often happens with fame, that sudden public interest came, swept away and, with it, evaporated. Sjööblom continued her modeling career and, once retired, returned to Sweden. Chances of life, one of those 7.16 million copies of the 1972 magazine ended up in the hands of a person linked to the Signal Image Processing Institute (SIPI) of the University of South Carolinaa laboratory in which, at that time, they worked on image processing and were laying the foundations of what would end up being the JPEG and MPEG standards. The coincidence would not be of greater interest if it were not for the fact that that reader took his Playboy to SIPI at the right time: just when They were looking for an image for their tests. The right place, at the right time Today it may seem crazy for someone to show up at the office with a nude magazine under their arm. Not in the 70s. As Lorena Fernández remembersof the University of Deustoin The Conversationnot only was it common for the staff to show themselves with their Playboy in teams that, like Carolina’s, were made up solely of men. It was even well seen, just like doing it today with The Times or the guide with the programming of La 2 documentaries. In that context, the arrival of Sjööblom’s photos was as well received as it was proverbial. Around June or July 1973, electrical engineering professor Alexander Swachuk, one of his graduate students, and the manager of SIPI were madly looking for a photo that they could scan and include in one of their presentations on image compression. They had their own stock, of course, but it was made up of files inherited from the boring and trite television standards of the early ’60s. The Swachuk Team I wanted a human face and an image that was also bright to guarantee a good output dynamic range. And what better option —they thought— that Sjööblom’s face? Skipping all the rules on property rights and decorum, the researchers used the image of Playboy. They kept only the top third of the magazine’s central poster and placed it under their muirhead scannerequipped with analog-digital converters and a minicomputer Hewlett Packard 2100. Jamie Hutchinson details To stay with a section of 512×512 pixels, they scanned 5.12 inches of the top of the photo, which in practice showed only Lena Sjööblom’s face, her shoulders and part of her bare back. The result showed a software error that forced the team to retouch it, but Swachuk’s team was working against the clock and decided to keep the distorted and altered image. The fact is that he liked it. Just as I had liked Sjööblom’s photo shoot in Playboy at the end of ’72. “They asked us for copies and we gave them to them so they could compare their image algorithms with ours on the same test image,” the professor himself recalled some time later. The final process At the SIPI they turned Sjööblom’s portrait into a test image for digital compression and transmission work. Arpanetthe precursor of the Internet. And that, with the passage of time, had an unpredictable result: the image of that model that everyone began to refer to as “Lena” or “Lenna” and whose origin began to blur became the standard used by other researchers who wanted to compress similar files with their algorithms. The face of that twenty-year-old Swedish woman, with a hat and a bare back, was replicated in books, conferences, articles, traveled through the “Atapuerca” of the Internet and helped lay the foundations for the JPEG image format. “Many researchers know the Lena image so well that they can easily evaluate any algorithm that runs on it. That’s why most people in the industry seem to believe that Lena has served well as a standard,” comments Hutchinson. In addition to being a “familiar image”, the photo combines shadows, highlights and blurred and sharp areas and details, a mixture that makes it “a tough test for an algorithm processing”. Perhaps the most curious thing about the entire story is that so much Playboy Like Lena Sjööblom herself, they spent decades without knowing the exorbitant fame—and the important role—of the 70s portrait. The first to … Read more

DeepSeek has just released a model that competes with Opus 4.6. It costs seven times less and runs on Chinese chips

They have passed 484 days since that “DeepSeek moment“, but the wait It seems to have been worth it, because we have the new DeepSeek V4 with us. We are facing an absolutely gigantic open weights model that once again promises to crack the foundations of the proprietary foundational models of Anthropic, OpenAI or Google. This is moving, gentlemen. Gigantic and open. DeepSeek v4 is an Open Source model and comes in two versions. The first is the Pro, with 1.6 trillion parameters (1.6T), of which it has 49,000 million active. The second is Flash, with 248,000 million parameters (248B, huge for a “Flash” model) of which 13,000 are active. More efficient than ever. Both versions they make use of a Mixture-of-Experts (MoE) architecture, which means that only a fraction of the parameters are activated in each inference. This allows the computational cost to be reduced significantly. Both versions support a context window of one million tokens—to include novels and novels at once as input—when in v3 it was 128,000 tokens. Furthermore, this model is much more efficient than its predecessor in computing per token: it requires only 27% of the operations per token and 10% of the KV cache compared to DeepSeek v3.2. Benchmarks promise. DeepSeek’s internal testing reveals that v4 Pro-Max (the best model with the highest reasoning ability) outperforms or is on par with Claude Opus 4.6 Max, GPT-5.4 xHigh, Gemini 3.1 Pro High, Kimi K2.6 and GLM 5.1. The results, however, are not independently verified, which means we should take them with caution. The numbers are still striking: in LiveCodeBench, a programming test, DeepSeek v4-Pro-Max achieves a 93.5% score compared to 88.8 for Opus 4.6 and 91.7% for Gemini 3.1 Pro. In other tests there is more variability, but at least on paper DeepSeek v4 Pro seems as good as Opus 4.7, which until now was the absolute benchmark. Much cheaper. But as happened with its previous version, the difference in price with those models from US companies is astonishing. As point the analyst Simon Willinson, the official prices of DeepSeek v4 Pro are 1.74 dollars per million input tokens and 3.48 dollars per million output tokens, up to almost seven times less than those of Opus 4.7 and up to almost 9 times less than those of the new GPT-5.5. With DeepSeek v4 Flash the cost is 0.14/0.28 dollars per million input/output tokens, when GPT-5.4 Mini costs up to 16 times more. The conclusion is obvious: if it really does what it says it does, the price is an absolute bargain. That is precisely the challenge: that real experience confirms what the benchmarks say. The hardware mystery. DeepSeek has not revealed what hardware has been used to train this version of its founding model. In the past they did admit that they had used NVIDIA’s H800s. Which yes it is known The thing is that the model has been developed to run on both NVIDIA and Huawei Ascend chips. This last has confirmed Baidu that its Ascend Supernode clusters based on the Ascend 950 will fully support DeepSeek v4 versions. Huawei support is “horrible” news for the US. In The Information they already commented that one of the reasons for the “delay” in the appearance of this model was to adapt it so that it worked without problems with Huawei chips. That support is according to Jensen Huang “horrible” news for the US, because it means that dependence on NVIDIA chips no longer exists or at least is reduced to a minimum. But. The launch comes at a difficult time for the company. Guo Daya, one of the people responsible for the v1 and v3 models, has signed for ByteDance to work on AI agents. Luo Fuli, who led the development of v2, joined Xiaomi last year. This launch also coincides with DeepSeek seeking external funding for the first time. They are expected to raise about $300 million and obtain a valuation of about $20 billion. according to The Wall Street Journal. From the surprise effect to the continuity effect. The launch of DeepSeek R1 in January 2025 was surprising because it demonstrated that China could train competitive models at a fraction of the cost of Western models. With DeepSeek v4 that surprise effect disappears to give way to the continuity effect. This model seems to maintain precisely what made the previous model famous: extraordinary power at a very low cost. Bad news for Anthropic. Such low prices are terrible news for Anthropic, which in recent weeks has been forced to execute a kind of “reduflation” of their new modelswhich are not more expensive but consume many more tokens. We’ll have to see if DeepSeek v4 Pro is as good as the company promises, but if it is, we’ll have another “DeepSeek moment” before us. Maybe not as notable as last year’s, but equally relevant. In Xataka | DeepSeek promised them happiness as the great Chinese AI. I didn’t count on a small detail: Kimi

Mythos will be the most dangerous AI model, but companies are already taking note of its security tips

Top AI companies are in the race to create the best artificial intelligence model. That race has been won by Anthropic with Mythos. At least, That’s what they claim (of course)with phrases like it is so powerful that they cannot make it public. There is reasons to take Anthropic’s words with a grain of salt, but what is evident is that Mythos is already working. Although the company has not released it, has already given access to certain technology partners. The decision is based on the company’s fear that the model will be used maliciously. They themselves have described as a threat to cybersecurity based on the number of zero-day vulnerabilities that Mythos would have found in both the main operating systems on the market and in browsers. And, just when the model is arousing opinions from some and others, Mozilla arrives to affirm that the latest version of Firefox 150 It has security fixes for 271 vulnerabilities that have been discovered thanks to this preliminary version of Claude Mythos. For its part, OpenAI does not believe anything at all. “Just as capable as a human” Mozilla it details in one of the latest posts on his blog. The company had been collaborating with Anthropic for some time and using the Claude Opus 4.6 model to find errors. In January, it found 22 vulnerabilities in a couple of weeks, 14 of them rated very serious. Of those 22 found by Opos 4.6, which is already a powerful model, we move on to the 271 discovered by Mythos. It is a huge leap and Mozilla wanted to continue investigating to see to what extent the new model surpasses Opus. Analyzing Firefox 147, Mythos generated 181 functional exploits. Opus 4.6? Just two. 90 times less. Those results have led Mozilla to write that Mythos Preview is “just as capable as the best human cybersecurity researchers”adding that they have not found any categories that humans can detect that Mythos cannot. This has another reading since, as the company itself states, seeing that the model is capable of finding so many errors in such a short time makes them wonder if it is possible to stay up to date in cybersecurity work when alternatives to Mythos are developed that do fall into hands not controlled by those responsible. There is always the fact that Mythos has not found any errors that Mozilla’s human ‘watchmen’ have not detected and that a tool like this will help to have a more secure system. All of this, in the end, pushing that narrative that Mythos is practically a technological miracle. a nuclear bomb The other side of the coin is that Sam Altman, head of OpenAI, doesn’t believe anything. Taking advantage of his recent participation in a podcast, he has qualified The entire Anthropic movement as a fear-based marketing ploy. He accuses Dario Amodei’s company (Altman’s public enemy) of wanting to restrict AI to a small number of people in a strategy that he has compared to having an atomic bomb, threatening to release it and making a living by selling bunkers to protect themselves from that same bomb. “It is evident that this is an extraordinarily powerful marketing strategy. We have created a bomb and we are going to drop it. You can buy a bunker from us for 100 million dollars” It is one more point in that historical rivalry in which both companies (and managers) have been involved for some time, but it comes just when Anthropic is having a greater role and OpenAI is being forced to release ballast in the form of services like Sora. Altman is not the only one who thinks that Anthropic is repeatedly using this discourse of “We have something so powerful that we cannot make it public” because it is a good strategy to obtain financing. There are already voices that they point that Mythos is not that big of a deal and, in fact, other models have proven to be able to do the same, finding the same errors and problems detected by Anthropic. But, above all, we must remember that, in 2019, someone already said that a model was too dangerous for public release. Who? OpenAI itself with GPT-2. Obviously, it wasn’t that dangerous. In Xataka | OpenAI and Anthropic have proposed the impossible: lose $85 billion in one year and survive

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