race

Samsung is tired of being second in the chip race. Now they are preparing to dethrone the titan of Taiwan

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When we talk about artificial intelligence, there are several proper names that star in the conversation. NVIDIA has become the foundation and cement of AI thanks both to their products as, above all, your money. But it’s impossible to leave Samsung out of the equation. Your HBM4 memories They are the ones that will allow NVIDIA and AMD manufacture their platforms new generation, but South Koreans do not want to stop there. They seek to be the largest advanced factory in the world and have launched a plan to wipe TSMC where it hurts the most. In the expansion throughout the United States. An x8 thanks to AI. 2025 was a transition year for Samsung. While its great rival in the memoir segment –SK Hynix– dominated the HBM chip marketSamsung is preparing to make the leap with HBM4 chips. This is the new generation of high-bandwidth memory designed to power the new AI platforms from both NVIDIA and Samsung. The effort paid off by overtaking SK and becoming the supplier of the two giants, and it is something that is already materializing. At least in estimates profit, of course. Now the company forecast profits of about 38 billion dollars for the first quarter of the year, something that destroys the profits of the same period last year, being eight times more. Texas. The company does not stop manufacturing the new HBM4 memory, but even so it cannot satisfy the enormous demand of its customers and there are already those who expect that the prices of these chips will increase by more than 50%. To meet demand, Samsung is moving, and The United States is key in its ambitious expansion. The South Korean company seeks to invest 37,000 million dollars in US soil, and 17,000 million of them they will stop to the Taylor, Texas plant. According to Korea Heraldthe company is finalizing hiring for this semiconductor plant where they hope to produce cutting-edge 2-nanometer chips. It is estimated that 1,500 people will be directly employed and the idea is to produce transistors with gate-all-around architecture. TSMC in the spotlight. Recent reports indicate that Samsung has already begun producing test units of chips in that lithography with the aim of beginning mass production by 2027. But this expansion is not only occurring in the United States. At the Pyeongtaek Campus, Samsung’s operations center, building a new factory for which Samsung has just ordered 20 EUV lithography machines valued at almost $8 billion. As it could not be otherwise, they are from ASML and it is estimated that the plant will have 70 units in total to support the production of HBM4 memory chips. And these two movements have one goal in mind: to dethrone the queen of semiconductors. Currently, TSMC takes the lead with NVIDIA and Apple as its best clientsbut Samsung is another industry giant that may not take the global throne, but is aiming for something more concrete: to be the one who leads the way in the United States. Both Samsung and TSMC are in full expansion throughout the United States, but if Samsung manages to start mass manufacturing of 2nm chips by 2027, it would overtake TSMC -focused on 2/3nm chips– in that development of advanced chips in the United States. It is still a vital race, since Tesla, Apple, NVIDIA or AMD are trying to get chips manufactured in the US and thus meet the demands of Donald Trump’s government. Trojan horse. In the end, it’s a move that Samsung can only win from. On the one hand, expand its HBM4 chip capacity to power AI platforms that do not seem to stop increasing in the short term. On the other hand, continuing to settle on American soil where it maintains a battle with the Taiwanese giant. But, also, Samsung is one of the founding members of the EPIC program of Applied Materials together with SK Hynix. They are positioning themselves to be the big player in semiconductors both as a factory and when it comes to designing machines and processes that allow for shorter development times for cutting-edge chips. and all this foreign companies are doing it on US soil when what the current government wanted was for were American companies those who will take the lead. In fact, Samsung’s plans are so ambitious that they are already looking for master 1nm chip production by 2030. In Xataka | ASML has discovered a way to further improve its SVU machines. This is terrible news for China and the US.

There is a much deeper and more important AI race in which China is crushing its competitors: human talent.

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The AI ​​race It’s about many things. Not only who makes the best AI modelswho has more and better data centers or who has more cheap energy to power this revolution. It’s also about something that right now China dominates with an iron fist: AI experts. China surpasses the US in talent. In The Economist have analyzed the evolution of the publication of studies at NeurIPS, one of the most important conferences in the world on AI. In the 2025 edition they have discovered a singular fact: for the first time in the history of this conference, China has surpassed the United States in studies presented, and that is the definitive sign of how the Asian giant has achieved a victory in a crucial area for the future of this technology. Alarming data. This data is not something isolated, but the result of a trend that began ten years ago. In 2019, 29% of researchers presenting their work at NeurIPS had started their careers in China. In 2025 that figure is 50%. Meanwhile, the proportion of quinees who began their careers in the US has increased from 20% in 2019 to 12% in 2025. The analysis is based on a sample of 600 articles written by almost 4,000 researchers (many studies have several researchers as authors). Chinese universities dominate. This analysis also served to analyze the origin of the researchers who published these studies. Nine of the ten institutions where the most NeurIPS 2025 researchers completed their studies are in China. Tsinghua University is, for example, the protagonist with 4% of all researchers. The prestigious MIT in the USA? Only 1% comes from there. Quantity matters, but also quality. It must be taken into account that this does not necessarily mean that China wins (or loses) in research quality, but it does in quantity. But this parameter is very relevant, because scale matters: when China manages to “produce” a huge number of AI graduates, its chances of those experts being responsible for new advances in this discipline increase. Not only that: it also makes these advances spread faster within the Chinese technological ecosystem. The US depends on Chinese talent. One of the most uncomfortable details of this study is where those who signed studies from US institutions were trained. Of all of them, 35% They graduated from Chinese universitiesthe same proportion as those who did so in US universities. Many leading AI companies in Silicon Valley are drawing on AI experts trained in China, which is increasingly the world’s largest pool of this type of engineers. Come home come back. What is worrying for the US is that the Chinese talent that US companies sign increasingly ends up returning to China. Chinese programs like Thousand Talents Plan They offer up to $100,000 annually plus subsidies for housing and research to attract that talent back. The United States government is also promoting just that, because the funding cutsthe uncertainty with visas and suspicions towards researchers of Chinese origin make working in the US no longer so attractive for these experts. Or what is the same: The US is shooting itself in the foot (again). From the American dream to the Chinese dream. In 2019, about a third of NeurIPS researchers who had graduated in China stayed in the country to work. In 2022 that proportion rose to 58%, and in 2025 the figure already reaches 65%. And as we mentioned, those who had left are returning: in 2019, only 12% of Chinese researchers who had completed postgraduate studies outside of China had returned, but in 2025 that figure has risen to 28%. The case of DeepSeek It is significant: none of its main contributors have a university degree outside of China: the talent who achieved that milestone He didn’t go through Stanford or MIT. The trend doesn’t lie. If we stick to the authors of studies published in NeurIPS as a metric, about 37% of the best researchers in the world now work in Chinese organizations, compared to 32% of those who do so in North American institutions. If this trend continuesin 2028, researchers working in China could outnumber those working in the US by two to one. Silicon Valley may continue to attract a lot of international talent, but the direction of the trend is clear, and that points to a worrying future for the United States. Image | Tommao Wang In Xataka | There is a city in China that goes head to head with Silicon Valley: welcome to Hangzhou, the home of the ‘Six Little Dragons’

OpenAI takes a step back in the AI ​​race to completely recalibrate

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OpenAI Sora has closed. His generative video AI that he has proudly shown on numerous occasions and which earned him a juicy $1 billion deal with Disney it no longer exists. The news fell like a bomb a few hours ago followed by the withdrawal of that billion-dollar Disney investment. Although there are those who point out that OpenAI is in trouble, those problems are not so much economic as lack of direction, and closing Sora seems only a step backwards in the long-distance race of OpenAI and AI. Go public this year and start harvesting after everything planted. In short. It’s the news of the day. Less than a year and a half after launching it, OpenAI says goodbye to Sora. In his day (February 2024, how time flies) we were amazed at what this generative AI could do. It was just 60 seconds of video and had some huge flaws, but it was one more step in the artificial intelligence race that positioned OpenAI at the forefront of the industry. Then other competing models arrived, culminating with a Seedance 2.0 that has consumed the entire Internet to plagiarize absolutely anything. Like all the others, wow. Issues. But although striking, Sora was a tool that didn’t seem to add up. While other services have integrated their generative AI models within an ecosystem or applications (the aforementioned Seedance 2.0 in suites AI or in the video editor CapCutfor example), Sora was there, away. The aforementioned contract with Disney was worth it, but it did not seem to be part of something larger, of a “creative suite” (if generative AI can be classified as such). He simply existed, and the worst thing was that others were passing him on the right. Eggs in many baskets. It was, in short, another product of an OpenAI that had eggs in many baskets. It was reaching dizzying numbers in different rounds of financing, setting up data centers, buying a lot from NVIDIA (depending a lot on NVIDIA, too) and launching products like crazy. OpenAI wanted to touch all the keys: And there are some other products, as well as a super app to integrate all that that was not being integrated into other sites. The philosophy was simple: if we are in everything, something will work, but the result has been the opposite and, as my colleague Javier Pastor said a few days ago, wanting to be the bride at the wedding and the dead man at the funeral It is having consequences. The competition tightens. While OpenAI diversified and allocated resources to touch all suits, Anthropic (which is not just a rival, it is a public enemy) was dedicated to two things. It’s not that Anthropic doesn’t have a browser or a video generator: it’s that they don’t even have an image generator. In exchange, what they do have They are functional, precise models and that they do things very well, especially in the field of amateur development with the vibe coding. Focusing on one thing and doing it very well is something that the market is seeing valueto the point that Anthropic is raising a lot of money in different recent financing rounds. In a short time, it has gone from being valued at 183,000 million to arrive up to 380,000 million, and that has had all the fuss with the United States government and the loss of contract with the Department of Defense. Money, too. And money moves everything, and while ChatGPT sweeps the consumer segment with more than 2.5 billion daily queries, you have to wonder how many paying users there are. Where the money really is, which is in business use, Anthropic controls the market with 32% compared to OpenAI’s 25%. And in programming, the distance is astronomical: 42% compared to 21%. In fact, OpenAI has seen how your business share has fallen from 50% in 2023 to just 25% today. As we say, this is where the greatest potential for growth and commercial performance is, and OpenAI is realizing that being focused on so many fields has caused them to be distracted. Or what is the same: they have covered more than they could bite off. Public company. The closure of Sora responds to a multitude of factors, but in the background there is something more important. NVIDIA has already said that the millionaire mega-rounds are overand it has done so just before the expected IPO of both OpenAI and Anthropic. When both go on the stock market, they will have to face another financing model. They will need products that generate profits to attract investors to buy shares, and right now, the one that is best positioned is Anthropic. OpenAI has a lot, but nothing makes it complete. Anthropic has less, but it is very efficient, and getting rid of Sora seems like a move to release ballast before becoming a “public” company (in the American concept). They have to focus their shooting, focus their teams (something they themselves have recognized) and stop wanting to be too much at once without having a clear strategy. Because they are becoming another example of being a pioneer It doesn’t always mean you’re the best. and that, if you don’t get your act together, competitors who have a clearer roadmap will eat your toast. Only time will tell if the strategy works, but at the rate things are going, it won’t take too long to find out. In Xataka | The worrying thing is not that AI is going to take your job in the future: it’s that it is preventing you from finding one now

Your bet in the AI ​​race is to bring together several functions in a single model

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The artificial intelligence race is often told as a competition to see who builds the most powerful model or the one that dominates the most benchmarks. In the middle of that board, the French startup Mistral AI has just presented Mistral Small 4a proposal that tries to occupy a different place in that conversation. It is not presented as a model limited to a single function, but as one that, according to the company, seeks to bring together several advanced capabilities within the same tool. What exactly is Small 4. The company presents it as the new great iteration of its Mistral Small family and, above all, as the first model of the house that brings together capabilities that were previously distributed among several lines. Specifically, it integrates functions associated with Magistral, Pixtral and Devstral along with those of the Small series itself. Fewer models, more features. One of the central ideas of the announcement is to concentrate tasks that are normally solved with different tools in a single system. According to Mistral, the goal is that the same model can be used to converse, analyze complex information, work with images or assist in programming without having to switch between several specialized systems. The numbers behind Small 4. The model is based on a Mixture of Experts architecture, a design that distributes processing between different specialized submodels and that today appears in several artificial intelligence systems. In the case of Small 4, Mistral indicates that the system has 128 experts and that only four participate in each generated token. According to the company, the model reaches 119B total parameters, with 6B assets per token, and offers a context window of up to 256k. Who is this model intended for?. Beyond its architecture, Mistral also describes quite clearly the scenarios in which it imagines the use of Small 4. Let’s see. Developers: Automate programming tasks, explore code bases, and code agent workflows Businesses: conversational assistants, document understanding and multimodal analysis Research: mathematics, complex analysis and reasoning tasks The underlying idea is that the model can move between quite different needs without forcing you to change the system depending on the type of work. The graphics. In the material accompanying the announcement, Mistral includes several graphs where it compares Small 4 with other models in different benchmarks. These comparisons are not limited to the score obtained in each test. They also show the average length of the responses each system generates, a data the company uses to illustrate how much text each model needs to produce to achieve certain results. One of the graphs in the advertisement corresponds to the AA LCR benchmark, where Mistral compares the scores of various models and the average length of the responses they generate to solve the same tasks. The data published by the company are the following: • Mistral Small 4: 0.72 score with 1,600 characters• GPT-OSS 120B: 0.51 with 2,500 characters• Claude Haiku: 0.80 with 2,700 characters• Qwen3-next 80B: 0.75 with 5,800 characters• Qwen3.5 122B: 0.84 with 5,700 characters The comparison. Small 4 is not the highest scoring model. Both Claude Haiku and the Qwen models appear higher in that indicator. However, Mistral highlights another aspect of the comparison: the length of the responses. According to the company, its model achieves this combination of score and output length by generating significantly less text than several of its competitors, something it relates to lower latency and lower inference cost. The short answer trick. A shorter answer is not better simply because it takes up less space. It is only if it manages to solve the task with a level of quality comparable to that of a longer answer. This is where Mistral tries to put the focus: if a model achieves a competitive result by generating less text, it can respond faster, consume fewer resources and reduce the cost of inference. In other words, the advantage is not in being more concise, but in needing less output to reach a useful result. How to access the new model. Small 4 can not only be used via API and AI Studio. Being published under license Apache 2.0is also proposed as an open model that can be downloaded, adjusted and deployed in your own environments. The company adds that it can be tried for free at build.nvidia.com, in addition to offering it for production as NVIDIA NIM. Images | Mistral In Xataka | OpenAI has been wanting to be the bride at the wedding and the dead man at the funeral for years: now it has finally defined its priority

Anthropic is winning the enterprise AI race, so OpenAI has a new plan: become Anthropic

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OpenAI has thrown out everything that moved in AI. They have been launching everything: a video generatora web browser with AI, an image generator with Studio Ghibli styletools e-commerceetc. The logic was simple: whoever tries everything has more chances to get something right, but the result has ended up being the opposite. While OpenAI seemed to be everywhere, Anthropic was focused on a single site and It has managed to eat the land where it mattered most. Enough of trying everything. Fidji Simo, the board that Altman signed last summer, recently called upon employees to give them a message that is rarely heard in a company with the growth of OpenAI: their main rival was teaching them a lesson. What Anthropic is doing, Simo explained, should be a wake-up call for OpenAI, which has lost leadership among software developers and enterprise customers. “We cannot waste this moment because we are distracted by parallel projects,” he stressed. The hidden cost of doing a little of everything. The problem with shooting at everything that moves is not only the focus, but the resources that this implies. In companies that develop foundational models, the key resource is computing capacity, and at OpenAI that resource jumped from one team to another depending on the priorities of the day. The Sora team, for example, was integrated into the research division despite being one of the company’s most visible products. OpenAI was growing fast in too many directions, and that also created internal tensions over which project should be prioritized. Anthropic focused on one thing. As OpenAI diversified, Its main rival adopted a completely opposite strategy: few products, a lot of depth. Claude does not generate images or video, does not have his own browser and is not trying to create his own chips (at the moment). It is dedicated to creating foundational models and offering them both in web service mode and especially through APIs for companies and developers. Claude Code, its flagship product for programming, became a viral phenomenon among software engineers last fall, and has ended up consolidating itself as the reference tool among amateur developers—vibe coding is still going strong—and of course among technical teams in all types of companies. OpenAI strikes back. The response has not been long in coming: OpenAI launched last month a new version of Codexhis programming tool, and accompanied it with new GPT-5.4 which is precisely much more oriented towards professional environments. According to Simo itself, Codex already exceeds two million weekly active users, almost four times more than at the beginning of the year. To drive usage of its product, OpenAI is deploying engineers to consulting firms and business partners to accelerate adoption of these products. IPO on the horizon. Both OpenAI and Anthropic are taking clear steps towards an IPO which in fact could occur this year. That makes gaining share in the corporate market—which is the one that really pays, the one that signs contracts, and the one that justifies valuations—absolutely essential for these IPOs to be successful. The initial share price and real valuation of these companies will depend on how well positioned they are, and at OpenAI they want to recover the lost ground in the enterprise market. In the meeting with the staff Simo explained that “we are acting as if this were a code red.” The paradox of being the pioneer. OpenAI unleashed the AI ​​fever with the launch of ChatGPT in November 2022 and made generative AI an almost everyday phenomenon. However, being the first usually has a trap, because it forces you to explore and diversify to maintain your reference position and that is very expensive. Anthropic came along later, saw where the real money was, and focused specifically on that sector. The student has surpassed the teacher, it seems, and at OpenAI they want to correct the strategy. What will happen to so much product?. It remains to be seen how this OpenAI strategy affects its entire product catalog. If you start focusing on developers and enterprise solutions, what will happen to your imager, Sora or Atlas? The structural tension between being a “research laboratory” and being a “product company” can pose a challenge for a company that naturally did not stop exploring new ideas to apply AI to them. Image | TechCrunch | Wikimedia Commons In Xataka | Sam Altman says he’s terrified of a world where AI companies believe themselves to be more powerful than the government. It’s just what you’re building

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

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

A Xiaomi SU7 has humiliated an entire Ferrari SF90 in an acceleration race. And that means absolutely nothing

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If in recent days you have wandered through social networks (and something tells me that is very likely) perhaps you have seen a video in which a Xiaomi SU7 Ultra makes a fool of an entire Ferrari SF90 XX Stradale in an acceleration race. “A Ferrari worth a million euros losing against a phone manufacturer” reads the tweet from accounts like that of @kinglinzhui who regularly posts information or videos proselytizing Chinese technology and culture. The tweet, in fact, has also been replicated by high-ranking figures in the State, as the Chinese ambassador to Colombia. In the video you can actually see how The Chinese car passes over the Ferrari. He Xiaomi SU7 Ultra It is the most advanced electric car from the Chinese manufacturer. It has 1,548 HP of power available and is limited to 350 km/h. He Ferrari SF90 XX Stradale It is also the most radical version of one of the most advanced sports cars that Ferrari has launched in recent years. In this case it is a plug-in hybrid with 1,030 HP of power with a V8 engine that generates up to 797 HP of power and is supported by three other electric motors to give the best of itself. Although there are some details to understand why the Xiaomi SU7 Ultra is faster, both Twitter accounts have focused on the inevitable: the most emblematic Western firm that puts a million-euro car on the market. (actually it is a limited edition of 790 units sold starting at 770,000 euros) is crushed by an electric supercar from a company that has just been born in the automobile market and that opened reserves for just over 100,000 euros at direct exchange. The problem is that it doesn’t mean much. Or, directly, it doesn’t mean anything. Click on the image to go to the original tweet The problem is the aura How important is technique in the debate? Everything and nothing really. And the first thing to keep in mind is that the comparison does not hold up. An electric car with more than 1,500 HP of power will always be faster in a straight line race than a car with a combustion engine. All its difficulty (and it is not a little, mind you) lies in being able to lower the power to the ground in the most effective way and launch the car forward as quickly as possible. In this case, it doesn’t matter if we are comparing a Ferrari with a Xiaomi or any other high-performance electric car. It is also not the first time we have seen comparisons of this type. And it is that carwow has already demonstrated the potential of the electric car facing a Kia EV6 GT against a Ferrari Purosangue. The power and sound of the naturally aspirated V12 against a general electric sports car. The result was the same again, with the Ferrari crushed. In the case of the Xiaomi SU7 Ultra and the Ferrari SF90 XX Stradale things change a little because those from Maranello have in this case an electrified car on their hands. All in all, although it certifies 0 to 100 km/h in just over two seconds, it is not enough to defeat the Chinese electric car. The problem for Xiaomi is that it sweeps the purely technical section but there is something it cannot offer right now compared to one of Ferrari’s most advanced cars in recent years: aura. When you spend more than 770,000 euros on a Ferrari (as if you were spending a million euros) it is not because you want to buy the fastest car. Or, at least, not only for that. First, you have to understand that the Ferrari SF90 XX Stradale is a circuit car, designed to perform at its best when linking curves. Something in which, of course, Also the Xiaomi SU7 Ultra has proven to be among the best. The case of this Ferrari is special because the “Program XX” It is designed to sell to a very specific group of customers a car that is not approved for the street, that can only be driven on a track. In fact, Ferrari takes the car wherever you want and maintains it when you have it stopped. It is a service typical of a pilot. However, this time, Ferrari has made the necessary adjustments to be able to drive it wherever the client wants. That exclusivity, that treatment of the customer is what a Ferrari SF90 XX Stradale customer buys when they get one of these limited units. The customer of this type of car is not concerned that a Xiaomi SU7 Ultra is faster in a straight line. I would dare say that few even care that it is faster on a track. Building a car brand from scratch has this problem. And it is even more complicated when it comes to an electric car. Chinese brands face a major obstacle. In many cases they are technically better than Westerners but they lack history. My colleague Javier Lacort explains it well in the podcast Infinite Loop. It is no coincidence that Xiaomi partners with Leica on its mobile phones. Nor that TCL has done the same with Sony for its televisions. Building a brand from scratch and having specific recognition as a firm that makes premium products worldwide is very complicated. The Volkswagen Group needed shovelfuls of marketing money for more than two decades to ensure that Audi was perceived as a German premium at the level of Mercedes or BMW. And the higher you aim, the more difficult it is to achieve that recognition. But Xiaomi also has another challenge: creating a story around its electric devices. When we tested the Porsche Macan We said that the car was great, a sporty electric SUV for traveling at extraordinary speed. And yet, it lacks soul. Because that same car previously had a V6 engine that generated sensations that were impossible to replicate by an electric car. It … Read more

Google is once again leading the AI ​​race and has something that no rival can match

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Google has launched Gemini 3.1 Proan incremental update of its flagship model that comes loaded with surprises. And according to its benchmarks, the model has much more to say than it seems. In abstract reasoning, Google wants to start setting the pace on Anthropic and OpenAI. But their ace in the hole is not just that, because they have something that other startups cannot replicate: their entire ecosystem and how they are integrating AI into it. What just happened. Just three months after launching Gemini 3 ProGoogle has published Gemini 3.1 Pro. The curious thing is that the jump is much more impressive than it may seem if we only looked at that “.1” in front of it. According to the company, the new model significantly improves the reasoning of the previous one and represents the intelligence base that already fed the Gemini 3 Deep Think update, presented last week. It is available today in the Gemini app, in NotebookLM (for Pro and Ultra subscribers), in the API through AI Studio, and in enterprise environments through Vertex AI. Data. In the ARC-AGI-2 benchmark, designed to evaluate the ability to solve completely new logical patterns, without the possibility of having seen them during training, Gemini 3.1 Pro has achieved 77.1%. To put it in context: Gemini 3 Pro stayed at 31.1%, while Claude Sonnet 4.6 marked 58.3% and Opus 4.6 68.8%. That is, Google has not only closed the gap, but has gone over it. It should be noted that never before has a mid-term review of its models recorded such a pronounced advance in reasoning. What the numbers say in the rest of the benchmarks. In the comparative table that accompanies the advertisementGemini 3.1 Pro tops the majority of categories evaluated: it obtains the best result in Humanity’s Last Exam without tools (44.4%), it leads in GPQA Diamond with 94.3% in scientific knowledge, and it doubles the previous model in APEX-Agents, the benchmark for long-term tasks. It also excels in MCP Atlas (multistep workflows), BrowseComp (agent search) and MMMLU (multilingual question and answer). It should be noted that, according to these benchmarks, it is not better in everything: in GDPval-AA Elo, which evaluates tasks in real-world work environments, Claude Sonnet 4.6 surpasses Gemini 3.1 Pro with 1,633 points compared to 1,317. And in SWE-Bench Verified, the programming test with agents, Opus 4.6 scores 80.8% compared to Google’s 80.6%. However, in the global calculation, the balance clearly favors Google’s new model. In Arena Leaderboard (the classification based on user votes) still places Claude Opus 4.6 ahead in text and code, although here “the sensations” of each user take more prominence when it comes to rating, than anything else. A clear competitive advantage. The strongest argument in favor of Google does not even have to do with the power of its latest model. The company doesn’t need to convince you to use its AI: it’s already where you are. Search, Gmail, YouTube, Android, Docs, Drive, Google Photos, Maps… Its AI does not depend on you opening a specific application, but is integrated into the ecosystem that millions of people already use daily. For the rest of the startups (OpenAI, Anthropic…), they need you to use their models in specific environments (ChatGPT, Claude). Google is simply already there. It’s a moat that perhaps not even the best model in the world could sweep right now. And then there’s the price. Gemini 3.1 Pro comes to users with a subscription to Google AI Plus, Pro and Ultra, although you can also try it on a limited basis in the free plan. It should be noted that it is currently in a preliminary version. The narrative that Google wants us to have in our heads is that, for a modest price, you have access to that model, plus everything the company offers in its ecosystem, including storage. That, right now, is very difficult to overcome. Additionally, for developers, the API is also offered at a very competitive price. So, from a practical point of view and from the pocket, Google is giving everything so that all its users continue using its ecosystem, with or without the best AI. The “.1”. The AI ​​race has been at a frenetic pace for months. And the most interesting of all is that Google, which arrived late for the racehas had a hell of a year in which he has structured all the mess he had with his AI. The jump from Gemini 3 to 3.1 in reasoning is greater than what many rivals have achieved between full versions. And it has done so while maintaining the advantage of being the company that controls the most relevant entry points to the Internet. It remains to be seen how they solve monetizing your artificial intelligencebut they have certainly put in the work. Cover image | Alex Dudar and Google In Xataka | The scientist who made the AI ​​we know today possible has just raised 1 billion. His new goal is to teach him to see space

Big Tech is paying up to $600,000 to influencers to promote their AI. Now the race is about perception

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Big technology companies are deploying their heavy artillery to attract users for their artificial intelligence services. Just like they count From CNBC, Microsoft and Google have found their new battlefield in influencers, with contracts that reach six-digit figures. The dimension of the phenomenon. According to data from Sensor Tower, generative AI platforms spent more than $1 billion on digital advertising in the United States during 2025, an increase of 126% compared to the previous year. That large companies promote their products through influencers is nothing new, and it is also a business that is very profitable for them, since by investing a small fraction of their budget they can get an avalanche of new users. According to CNBC, in order to attract new users for their AI services, Microsoft, Google, Anthropic and Meta They are hiring content creators to promote your tools on social networks. Figures. Microsoft and Google are paying between $400,000 and $600,000 to content creators for multi-month collaborations, according to sources close to the media. These contracts are not limited to specific publications, since according to the medium, they seek to ensure that influencers integrate AI tools into their usual content, tutorials and workflows. “We’re seeing a massive increase in creator spending from these AI brands. We’re getting a lot more interest from AI brands every month,” counted to AJ Eckstein, founder of Creator Match (an agency that connects brands with creators). How these agreements work. Collaborations range from LinkedIn posts explaining how to use Claude Code even videos on Instagram showing functions of Microsoft Copilot or the assistant Comet by Perplexity. Megan Lieu, AI and technology content creator with nearly 400,000 followers, explains told CNBC that his sponsored deals typically range from $5,000 to $30,000 depending on the campaign. Its most important collaboration to date has been with Anthropic to promote products from Claudealthough he did not specify the exact figure to the media. Some influencers can charge up to $100,000 per post, according to Eckstein. The other side of the coin. Despite the astronomical numbers, not all content creators are willing to jump on the AI ​​bandwagon. Jack Lepiarz, known as Jack the Whipper and with more than 7 million followers between YouTube, TikTok and Instagram, account to the medium that systematically rejects any agreement related to artificial intelligence. “I cannot with a clear conscience support something that is going to make it difficult for normal people to earn a living,” he declared to the outlet. Lepiarz previously turned down a $20,000 contract to promote AI imaging tools and says even $100,000 or $500,000 wouldn’t change his mind. Perception with Copilot. For Microsoft, these influencer campaigns can be especially key. And despite its large user base in Microsoft 365 services, only 3.3% pay for Copilotas told from Windows Central. The company needs its AI assistant, integrated into Windows, Microsoft 365 and Edge, to be perceived as a natural tool in daily work, and at the moment it is being especially difficult for them to achieve that. It’s public time. Big Tech hiring influencers occurs precisely at a time when companies are investing more than ever in advertising their AI tools. A few days ago we told precisely the case of Anthropic, which spent a million on ads during the Super Bowl. Separately, Google and Microsoft increased their digital advertising spending to promote AI products by approximately 495% last month compared to the previous year, according to Sensor Tower. The media also says that OpenAI multiplied its advertising investment tenfold in 2025. After years of making its tools known, it is now time to shape our perception of them. Cover image | aerps and Hillary Black In Xataka | The person who is earning the most money on Twitch by broadcasting 24 hours a day is not a person: it is an AI

The wildest race on the Olympic tracks in Cortina was in 1981. A man launched himself dodging bullets and assassins on a motorcycle

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There are places that seem calm until someone decides to take them beyond reason. Scenarios conceived for precision and discipline that end up becoming, through a combination of ambition and audacity, within the framework of feats that border on the impossible and they leave a mark that is difficult to erase. The slopes of Cortina, in Italy, have seen all kinds of sporting feats, but few like the one that occurred in 1981. Return with the aroma of cinema. When the Winter Games They return to Cortina d’Ampezzothe tracks not only recover their sporting history, but also one of the sequences more wild and brutal never shot in the snow. The scene in question turned these mountains into the scene of impossible chases, shootings adrenaline in full descent and suicidal jumps that were etched in the collective memory long before he was once again at the center of the Olympic calendar, or even before Tom Cruise himself will amplify the scene in his Mission Impossible saga. The wildest chase. The story took place in 1981, during the filming of For Your Eyes Only which led to James Bond himself (then played by Roger Moore) to flee skiing of armed killers, motorcycles and even a biathlete who shot him while he was descending at full speed. In fact, the brutal sequence culminated with a maneuver as absurd as it was legendary: sliding down an Olympic bobsleigh track at more than 80 kilometers per hour and be thrown into the void as if it were a ramp. It was an extreme scene even for the saga, which came from sending the agent into spacebut which found in the Italian Alps a new limit for its formula of constant danger. Six weeks on the brink of disaster. The sequence in question required more than a month of filming, expert drivers inherited from The Italian Jobpiano wires, cameras mounted on bobsleighs and snow transported by trucks in the middle of the drought. Not only that. The team continued despite injuries from Roger Moore himselfburning bobsleighs and a level of risk so extreme that it was necessary to check every screw on the cameras before launching across the ice. Bogner and the men who did know how to ski. Behind the camera was Willy Bogner Jr.former Olympian and pioneer of ski filming, who decided roll the action back and designed double-tip skis to survive the challenge. Around them, specialists as John Eavesworld champion freestyle skier, learned to bobsled down the slopes again and again, while some actors struggled simply to stay upright on skis. Curtain, specialists and memory. Another of the key names was in the figure by Giovanni Dibonaa local specialist recruited to test whether it was possible to ski in and out of the ice channel, a feat that defined the entire final sequence. Decades later, The Wall Street Journal said that Dibona barely remembers why they were chasing Bond, but he remembers the titanic effort involved in filming in those conditions, an experience that made him understand that action cinema was not very different from extreme sports. Between glamor and tragedy. Plus: the filming was also marked by death. During a break for the 1981 world bobsleigh championships, an American athlete died in competition and, on the last day of filming, a young Italian stuntman He died when his sleigh overturned. All of this contrasted with the glamorous premiere of the film, a grand premiere attended by the then Prince Charles and Diana of Wales. Bond got off his skis, Cortina didn’t. The truth is that, over the years, the character of James Bond left the snow behind for other purposes such as hanging of trains and helicoptersbut Cortina remained a temple of vertigo, one shared by cinema and sport. There, those who lived through that filming know that the Bond films and the Olympic Games have something essential in common: they both look elegant from the outside, but they hide a hardness that only those who have ever gone downhill understand (or above) without network. Image | United In Xataka | One of the best comedies in history turned this simple scene into the most expensive. 9/11 and a highway were to blame In Xataka | In 1987 a death was filmed so savage that people had to cover themselves. The trick to achieve it turned RoboCop into a cult work

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