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In line with the rules of the International Truck of the Year (IToY) organisation, the title is awarded annually to the vehicle introduced in the previous 12 months that has made the greatest contribution to road transport efficiency. The assessment covers a range of criteria, including technological innovation, comfort, safety, drivability, energy efficiency, environmental performance and Total Cost of Ownership (TCO).

Praised for exceptional efficiency

The jury of 23 leading commercial vehicle journalists from across Europe praised DAF’s XD and XF Electric truck series for their exceptional energy efficiency, refined yet powerful driveline, and advanced technical architecture. Furthermore, the award-winning vehicles were recognized for their long driving ranges, advantageous LFP battery technology, and superior driver comfort.

“During extensive test drives, jury members praised the XD and XF Electric for the perfection of their drivelines and the almost imperceptible gear changes. The modular vehicle concept, offering a wide choice of battery and axle configurations provide operators an exceptional flexibility”, commented Florian Engel, chairman of the International Truck of the Year jury. “With the new XD and XF Electric, DAF Trucks demonstrates that the combination of a central electric motor and a traditional rear axle can be at least as energy-efficient as a driveline with an e-axle. Moreover, this DAF configuration provides perfect weight distribution, enabling virtually all use cases to be covered by a single technical platform.”

Zero emission range up to over 500 kilometers

The XD and XF Electric trucks are powered by PACCAR’s advanced EX-D1 and EX-D2 e-motors, delivering outputs from 170 kW (230 hp) to 350 kW (480 hp). With modular battery packs ranging from 210 to 525 kWh, the 4×2 and 6×2 tractor and rigid vehicles offer zero-emission ranges of over 500 kilometers on a single charge, and even over 1,000 kilometers per day thought optimal charging planning.

DAF’s XD and XF Electric trucks are designed for both city and regional distribution and long distance applications, combining excellent aerodynamics with a low cab position, ultra-low window beltlines, and advanced digital camera systems for superior safety and visibility.

‘A moment of pride’

“Winning the International Truck of the Year 2026 with our XD and XF Electric models is a moment of pride for all DAF employees”, said DAF president Harald Seidel. “This recognition underlines our commitment to drive the future of zero-emission transport through innovation, quality, and sustainability. We are thrilled that the jury of leading commercial vehicle journalists acknowledges the vehicles’ efficiency, safety, and exceptional driver comfort. Receiving the most prestigious award in the truck industry is another recognition of the hard work of the entire DAF organization to deliver first class products and services to our customers.”

Saudi Arabia is strengthening its ties with American AI companies — announcing a flurry of new joint ventures worth billions of dollars. The country seeks to make its mark in the AI industry as its de facto leader, Crown Prince Mohamed bin Salman, makes his first visit to the United States in years.

Humain — an AI company backed by Saudi Arabia’s sovereign wealth fund — announced a series of partnerships with prominent American tech companies, including xAI, Cisco, AMD and Qualcomm, during a US-Saudi investment forum in Washington on Wednesday.

Saudi Arabia is trying to further firm up ties with the United States and shift its economy away from oil. For American companies, the Middle Eastern country answers three urgent problems for AI expansion: funding, space and cheap energy.

Elon Musk announced at Wednesday’s event that xAI, his AI company, will develop a huge data center in Saudi Arabia alongside Humain. The planned 500-megawatt data center would be xAI’s first large-scale center outside of the United States, and the partnership will see xAI’s Grok chatbot deployed throughout Saudi Arabia.

“The future of intelligence will be engineered through massive and efficient compute combined with the most advanced AI models,” Musk said in a statement on Wednesday.

The center will be powered by chips from Nvidia, whose founder, Jensen Huang, sat alongside Musk and Saudi Arabian Minister of Communications and Information Technology Abdullah Alswaha at Wednesday’s panel. No further details about the partnership were revealed.

“This is how we walk the talk in the kingdom of Saudi Arabia in partnership with the US,” Alswaha said. “Yesterday, the president and his royal highness announced the AI strategic framework and partnership. Today we’re going big with Elon and Jensen, so thank you for those opportunities.”

Bloomberg reported on Wednesday that the United States is set to approve the first sales of advanced AI chips to Humain.

At the event, Alswaha announced a 100-megawatt data center for Amazon Web Services “with a gigawatt ambition” that also will be powered by Nvidia’s infrastructure. AWS said in a statement that it plans “to provide, deploy and manage up to 150,000 AI accelerators” in Riyadh, the capital of Saudi Arabia.

As AI companies expand, their huge data centers need space and massive energy sources. Many data centers are being built in the United States, including xAI’s Colossus in Memphis. However, there’s fear that China will beat out the United States when it comes to energy production to power AI systems. Saudi Arabia could help with that — it has much easier access to the space and energy needed to power these massive ventures.

The investment from Saudi Arabia also plays a major part in Prince bin Salman’s redemption in the United States, following being labeled a “pariah” by President Joe Biden for his role in the killing of Washington Post columnist Jamal Khashoggi.

During his Oval Office visit on Wednesday, Prince bin Salman claimed the country will be investing $1 trillion dollars in the United States, a substantial increase from the previously announced $600 billion investment in May. The comments surprised even President Donald Trump in the moment, although the timeline of the investments is not clear.

“You’re saying to me now that the $600 billion will be $1 trillion?” Trump said to Prince bin Salman in the Oval Office. “Good. I like that very much.”

One of the things that AI doesn’t have that humans have in abundance is fingerprints.

Researchers at Northeastern University used the unique fingerprints of human writing — word choice variety, complex sentences and inconsistent punctuation — to develop a tool to sniff out AI-generated text.

“Just like how everyone has a distinct way of speaking, we all have patterns in how we write,” says Sohni Rais, a graduate student in information systems at Northeastern and a researcher on the project. In order to distinguish between human writing and AI text, she says, “we just need to spot the telltale patterns in writing style.”

AI text detection typically requires substantial computer power in the form of neural network transformers, says Rais, because these approaches analyze every letter, word and phrase in extreme detail. But this level of analysis isn’t necessary to distinguish between human and AI-generated text, Northeastern researchers say. In fact, the technically “lightweight” tool Rais helped develop can run on a regular laptop and is 97 percent accurate.

“We are not the first in the world who develop detectors,” says Sergey Aityan, teaching professor in Northeastern’s Multidisciplinary Graduate Engineering Program on the Oakland campus. “But our solution requires between 20 and 100 times less computer power to do the same job.”

Existing AI-text detecting services, including ZeroGPT, Originality and AI Detector, train large language models to analyze each word. Text entered into these tools is analyzed by proprietary algorithms trained with large datasets powered by transformers.

The lightweight tool can be trained by the user and live on their laptop, offering security and customizing advantages.

“Either you don’t want your secret information to go somewhere beyond your laptop,” says Aityan, “or you are a professor and you want to catch your students cheating, so you train your own dataset based on specific texts.” 

Instead of using transformers, the lightweight approach uses 68 unique stylometric features — or “writing fingerprints,” as Rais calls them — that make each person’s writing unique. These features include sentence complexity.

While AI agents tend to write at a very consistent reading level, humans naturally vary, she says. 

“We might write simply when texting a friend but more formally in an email to our boss,” Rais says.

The tool also looks at word variety, which humans naturally mix up. 

“We might say ‘happy,’ then ‘glad,’ then ‘pleased,’” Rais says. “AI often gets stuck using the same words repeatedly despite knowing many synonyms.”

It also looks at how far apart related words are in a sentence, she says. For instance, in “the cat that I saw yesterday was orange,” the subject (cat) and the verb (was) are separated by five words. Sentences generated by AI, Rais says, maintain consistent distances of two or three words between subjects and verbs.

Instead of looking at every single word, the lightweight approach looks for the most relevant clues.

“It’s like taking a person’s vital signs at the doctor,” she says. “Instead of running every possible test, we measure key indicators like temperature, blood pressure and heart rate that tell us what we need to know.”

The work to develop ways of detecting AI-generated text isn’t over, says Aityan. It is the nature of AI-based systems, however, to learn and improve, he says. As soon as people developed the technology to generate AI text, he says, the technology to detect it followed. And shortly after that, he says, came so-called humanization algorithms to make AI-generated text sound more natural.

“It’s an ongoing battle,” he says.

One of the things that AI doesn’t have that humans have in abundance is fingerprints.

Researchers at Northeastern University used the unique fingerprints of human writing — word choice variety, complex sentences and inconsistent punctuation — to develop a tool to sniff out AI-generated text.

“Just like how everyone has a distinct way of speaking, we all have patterns in how we write,” says Sohni Rais, a graduate student in information systems at Northeastern and a researcher on the project. In order to distinguish between human writing and AI text, she says, “we just need to spot the telltale patterns in writing style.”

AI text detection typically requires substantial computer power in the form of neural network transformers, says Rais, because these approaches analyze every letter, word and phrase in extreme detail. But this level of analysis isn’t necessary to distinguish between human and AI-generated text, Northeastern researchers say. In fact, the technically “lightweight” tool Rais helped develop can run on a regular laptop and is 97 percent accurate.

“We are not the first in the world who develop detectors,” says Sergey Aityan, teaching professor in Northeastern’s Multidisciplinary Graduate Engineering Program on the Oakland campus. “But our solution requires between 20 and 100 times less computer power to do the same job.”

Existing AI-text detecting services, including ZeroGPT, Originality and AI Detector, train large language models to analyze each word. Text entered into these tools is analyzed by proprietary algorithms trained with large datasets powered by transformers.

The lightweight tool can be trained by the user and live on their laptop, offering security and customizing advantages.

“Either you don’t want your secret information to go somewhere beyond your laptop,” says Aityan, “or you are a professor and you want to catch your students cheating, so you train your own dataset based on specific texts.” 

Instead of using transformers, the lightweight approach uses 68 unique stylometric features — or “writing fingerprints,” as Rais calls them — that make each person’s writing unique. These features include sentence complexity.

While AI agents tend to write at a very consistent reading level, humans naturally vary, she says. 

“We might write simply when texting a friend but more formally in an email to our boss,” Rais says.

The tool also looks at word variety, which humans naturally mix up. 

“We might say ‘happy,’ then ‘glad,’ then ‘pleased,’” Rais says. “AI often gets stuck using the same words repeatedly despite knowing many synonyms.”

It also looks at how far apart related words are in a sentence, she says. For instance, in “the cat that I saw yesterday was orange,” the subject (cat) and the verb (was) are separated by five words. Sentences generated by AI, Rais says, maintain consistent distances of two or three words between subjects and verbs.

Instead of looking at every single word, the lightweight approach looks for the most relevant clues.

“It’s like taking a person’s vital signs at the doctor,” she says. “Instead of running every possible test, we measure key indicators like temperature, blood pressure and heart rate that tell us what we need to know.”

The work to develop ways of detecting AI-generated text isn’t over, says Aityan. It is the nature of AI-based systems, however, to learn and improve, he says. As soon as people developed the technology to generate AI text, he says, the technology to detect it followed. And shortly after that, he says, came so-called humanization algorithms to make AI-generated text sound more natural.

“It’s an ongoing battle,” he says.