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On Monday, Harry Kane and Alexia Putellas won the 2026 Ballon d’Or at a ceremony held in London—the first time the event has taken place outside of France since the award’s inception in 1956.
English forward Harry Kane received the award for the first time, finishing ahead of Frenchman Kylian Mbappé (second) and Spaniard Lamine Yamal (third) in the voting. The award recognizes the best footballer in the world for the season and cements Kane’s status as a leading figure in international football.
In the men’s rankings, Rodri placed fourth and Fabián Ruiz tenth, while Lionel Messi finished fifth.
Meanwhile, Spain’s Alexia Putellas claimed her third Women’s Ballon d’Or, following her previous wins in 2021 and 2022. The midfielder was recognized as the best female player in the world, further building her legacy in one of the most significant awards in women’s football.
Golden nights for Alexia Putellas and Harry Kane as they win the 2026 Ballon d’Or Féminin and Ballon d’Or Awards.
Putellas, who led FC Barcelona Femení to the quadruple last season, wins the Award for a record-equalling third time.
The Ballon d’Or is the most prestigious individual honor in world football, recognizing the best player on the planet each season. Its list of winners includes the sport’s greatest legends, topped by Lionel Messi—who holds the all-time record with eight awards—ahead of Cristiano Ronaldo, who has won five.
From a Spanish perspective, only Luis Suárez Miramontes, Alfredo Di Stéfano, and Rodri have managed to win the trophy—an award that, every year, captures the attention of the football world and fuels the debate over who delivered the best performance of the season.
A packed Monumental Stadium in Buenos Aires gathered to support the Argentine national team in its match against Benin and to share an emotional night as Lionel Messi bid farewell to the Argentine public. Thousands of fans gathered around the stadium and in the stands celebrated… pic.twitter.com/M5qEFV7IpY
Machenta cofounders Edward Johns and Vitalis Vosylius
Oscar May
Machenta raised $40 million in seed funding led by Creandum and Union Square Ventures.
It says robots can learn tasks from one demo using a model trained entirely in simulation.
The startup remains focused on research as it takes on multibillion-dollar rivals.
A London-based robotics startup is betting it can teach robots new tricks without collecting the mountains of real-world data its much bigger rivals are chasing.
Machenta, founded by researchers from Imperial College London, has raised a $40 million seed round to build an AI model that can teach robots new physical tasks after seeing them performed just once, the company exclusively told Business Insider. Venture capital firms Creandum and Union Square Ventures co-led the round. Machenta declined to disclose its valuation.
While rivals building general-purpose AI models for robots are scrambling to amass real-world data, Machenta is taking a contrarian approach. Edward Johns, the startup's CEO and cofounder, and an Imperial professor, said the company trains its model in computer-generated environments that mimic how objects move and interact, without using a "single pixel of real-world data."
Once trained, the model can direct a robot to perform a new task, such as tidying a table or putting objects into a box, after watching it done just once, without further training, Johns said. In a video shared with Business Insider, three robots pack and close a box on an assembly line, a task Johns said they learned this way.
The startup has 11 employees and remains focused on research, with no near-term plans to commercialize its technology. Its model can learn some tasks immediately, but more precise tasks and complex movements remain challenging.
Machenta faces rivals with multibillion-dollar valuations in one of robotics' most competitive and well-funded fields. Its California-based competitors include Physical Intelligence, valued at roughly $11 billion, and FieldAI, which is raising funding at a $10 billion valuation, Business Insider reported this month.
Another rival, Skild AI, which is valued at over $14 billion, is already selling technology with similar capabilities. In August, Skild unveiled a model that it said could perform previously unseen tasks lasting up to 10 minutes after a single video demonstration. Last month, it said that it had reached $100 million in annual recurring revenue from more than 60 customers.
From research lab to startup
Six years ago, Johns began investigating whether robots could learn new physical tasks as quickly as humans at his Imperial College lab.
In the lab's early experiments, researchers physically guided a robot through a task, such as picking up a spatula and scooping an object. The robot then spent about 30 minutes collecting additional data about the object and its surroundings before it could perform the task on its own.
Johns and former Ph.D. student Vitalis Vosylius spent the next few years working to eliminate that extra step, founding Machenta late last year after seeing early signs that their model could perform new tasks immediately after a demonstration.
Machenta's team
Oscar May
Companies are increasingly buying and collecting data to teach robots how to interact with the real world. But Johns believes simply collecting more data isn't the answer. It also remains unclear how much training data will be needed to make general-purpose robots a reality. Johns said that Machenta is taking on the harder challenge of designing algorithms that can generate synthetic data useful for teaching robots physical tasks.
Machenta plans to use the funding to expand its team and train its model with more computing power and simulated data, hoping to teach robots to perform more precise and complex tasks.
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