Reportedly, the crime group accessed more than 350GB of stolen data related to data dumps of mail servers, databases, confidential documents, contracts and other sensitive material.

The extortion group ShinyHunters has been linked to the recent (24 March) breach of the European Commission’s Europa.eu platform, in which a reported 350GB of data, across multiple databases, was accessed and stolen. 

In a statement issued after the incident (27 March), the European Commission stated that their early findings suggest that private data has been accessed and Union entities affected by the attack will be contacted. The Commission’s internal systems are not believed to have been affected.

The Commission explained it will continue to monitor the situation, taking the necessary precautions to ensure the security of its systems and data, as well as work to analyse what happened so it can use the results to improve its cybersecurity capabilities.

While the Commission has not shared further details on the incident, alleged data dumps uploaded to ShinyHunters’ Tor data leak site are said to include content from mail servers, internal communications systems, databases, confidential documents, contracts and additional sensitive material. 90GB of information allegedly stolen from the European Commission’s compromised cloud network has already been shared. 

ShinyHunters are an extortion group established around 2020, who have carried out a number of high-profile, financially-motivated attacks on groups such as Salesforce, Allianz Life, SoundCloud and Ticketmaster. The criminal organisation also claimed responsibility for an attack on Match Group, which owns Tinder, Hinge, Meetic, Match.com and OkCupid. 

In July 2024, AT&T paid a member of the ShinyHunters hacking group $370,000 to delete the data of millions of customers following a massive data breach of its systems. Reportedly, the stolen data exposed the calls and texts of nearly all of the platform’s 110m cellular customers after ShinyHunters stole the information from the cloud data giant Snowflake.

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The White House did not respond to a request for comment about the meetings, but an official who was not authorized to speak on the record, told WIRED at the time: “The White House does not comment on mysterious meetings with unnamed staffers.”

Simultaneously, Trump has also sought to absolve officials of any wrongdoing in the wake of the 2020 election. Last year, Trump gave “full, complete and unconditional” pardons to a slate of people who had tried, and failed, to help him overturn the 2020 election results. In recent months, Trump has pressured Colorado governor Jared Polis to release Tina Peters, the former county clerk in Mesa County, Colorado, who became a hero for the right’s election deniers when she facilitated a security breach during a software update of her county’s election management system.

Peters was found guilty of four felonies, but Trump has been mounting a campaign in recent months to get her released, even going so far as to say he “pardoned” her, even though he has no power to do so given she was convicted on state charges.

Election Day Interference

While Trump has not announced specific plans to deploy troops to polling locations or seize voting machines, he and his administration have certainly been suggesting that such action is not off the table.

In January, Trump lamented not having the National Guard seize certain voting machines after the 2020 election. In early February, White House press secretary Karoline Leavitt told reporters that while she hasn’t specifically heard Trump discussing the possibility, she couldn’t “guarantee that an ICE agent won’t be around a polling location in November.” (The question was in response to former White House adviser Steve Bannon stating: “We’re going to have ICE surround the polls come November. We’re not going to sit here and allow you to steal the country again … We will never again allow an election to be stolen.”)

Earlier this month, during his confirmation hearing to head up the Department of Homeland Security, Senator Markwayne Mullin said he would be willing to deploy ICE to polling locations to address “a specific threat.”

The result of the Trump administration’s drip feed of threats and dog whistles is that those who are running elections in states across the country are already war-gaming what happens if ICE or the National Guard show up at their voting locations.

Michael McNulty, the policy director at Issue One, a nonprofit that tracks the impact of money in politics, also points to the fact that the Department of Justice sent monitors to oversee elections in November in New Jersey and California, despite no federal elections being held. “The concern is that this could become a massive deployment of, quote unquote, observers by the DOJ in 2026 who might do something more, whether it’s intimidation, whether it’s interfering with local election officials, to get data to confirm conspiracy theories,” McNulty tells WIRED.

FBI Raids

On January 28, the FBI raided the election office in Fulton County, Georgia, executing a search warrant that allowed it to seize ballots, ballot images, tabulator tapes, and the voter rolls related to the 2020 election. The search warrant affidavit, unsealed a few weeks ago, shows that the FBI relied on the work of Kurt Olsen, a lawyer who was appointed by the administration to investigate election security in October and who has a long history of working with some of the country’s biggest election deniers, including Patrick Byrne, Mike Lindell, and Kari Lake. Olsen’s claims are based on debunked and previously investigated conspiracy theories about the 2020 election.

The raid was also notable for the presence of Tulsi Gabbard, the director of national intelligence, who is, according to The Guardian, running a parallel investigation into the 2020 election with the apparent tacit approval of Trump.

This article is part of the collection: Teaching Tech: Navigating Learning and AI in the Industrial Revolution.

A little over a decade ago, schools were swept into what many described as a movement to prepare students for the future of work. That work was coding — “Hello, world!”

Districts introduced new courses, nonprofits expanded access to computer science education and a growing ecosystem of programs promised to teach students the skills needed to enter the tech workforce. For many, it felt like a necessary correction to a rapidly digitizing world. But over time, a more complicated picture emerged.

While access to computer science education expanded, the relationship between early coding exposure and long-term workforce outcomes became uneven. The “learn to code” movement raised an important question that still lingers today: Which skills actually endure when technologies change? That question has resurfaced in a new form.

Today, generative AI is driving a similar wave of urgency. Schools are once again being encouraged to adapt quickly, often with the same underlying rationale that teachers must prepare students for a future shaped by emerging technologies.

But if the instructional role of AI remains unclear, and if the tools themselves are likely to evolve rapidly, the more persistent challenge may lie elsewhere.

After conducting a two-year research project alongside teachers, who are adapting and are open to integrating AI, we found that uptake is still minimal. Most of our participants, including those who are engineering or computer science teachers, still struggle to identify a clear or universal instructional use case for widespread AI integration.

So, what should students learn to help them adapt to whatever comes next?

A growing body of research suggests that the answer may lie not in teaching students how to use a particular AI system, but in helping them understand the computational ideas that make those systems possible.

The Limits of Teaching the Tool

In recent years, many discussions about AI education have centered on teaching students how to use generative tools effectively. Prompt engineering, for example, has become a common topic in professional development workshops and online tutorials.

Yet, focusing heavily on tool-specific skills can create a familiar educational problem, because technology changes faster than curricula.

Teaching students how to interact with a specific interface risks becoming the equivalent of teaching to standardized tests, rather than teaching students important lessons that don’t appear on state exams.

The history of computing education offers a useful example. In the early 2010s, a wave of coding initiatives encouraged schools to teach programming skills broadly. While many of those programs expanded access to computer science education, subsequent analysis showed that workforce pipelines in technology remained uneven, and many students learned tool-specific skills without developing deeper computational reasoning abilities.

That experience offers a cautionary lesson for the current AI moment. If the goal of integrating AI into education is long-term preparation for technological change, focusing narrowly on how to use today’s tools may not be the most durable strategy.

The Skill That Outlasts the Tool

A growing body of research suggests that computational thinking is a more durable educational objective.

Computational thinking refers to a set of problem-solving practices used in computer science and other analytical disciplines. These include:

• breaking complex problems into smaller components
• recognizing patterns
• designing step-by-step processes
• evaluating the outputs of automated systems

These skills apply not only to programming but also to fields ranging from engineering to public policy.

Importantly, they also help students understand how algorithmic systems operate.

When students learn computational thinking, they gain the ability to analyze how technologies like AI produce results rather than simply accepting those results as authoritative.

In this sense, computational thinking provides a conceptual bridge between traditional academic skills and emerging digital systems.

What Teachers Are Already Doing

Many teachers in our study were already moving in this direction, often without using the term computational thinking.

When teachers asked students to analyze chatbot errors, they were encouraging students to examine how algorithmic systems produce outputs. When they designed exercises comparing training data and algorithms to everyday processes, they were helping students reason about how automated systems work.

These approaches do not require students to rely heavily on AI tools themselves. Instead, they position AI as a case study for examining how technology shapes information.

That framing aligns with longstanding educational goals around critical thinking, media literacy and problem-solving.

Implications for Educators

If the instructional use case for generative AI remains uncertain, educators may benefit from focusing on skills that remain valuable regardless of which tools dominate in the future.

Several practical approaches are already emerging in classrooms. Teachers can use AI systems as objects of analysis, asking students to evaluate outputs, identify errors and investigate how models generate responses.

Lessons can connect AI to broader topics such as data quality, algorithmic bias and information reliability.

Assignments that emphasize reasoning, structured problem solving and evidence evaluation continue to support the kinds of cognitive work that remain central to learning.

These approaches allow students to engage with AI without allowing the technology to replace the thinking process itself.

Implications for EdTech Developers

The experiences teachers described also highlight an opportunity for edtech companies.

Many current AI tools were developed as general-purpose language systems and later introduced into education contexts. As a result, teachers are often left to determine whether and how those tools align with classroom learning goals. Future products may benefit from deeper collaboration with educators during the design process.

Teachers in our conversations were already experimenting with small classroom applications, designing AI literacy lessons and building course-specific chatbots.

These experiments resemble early-stage product development.

Partnerships between educators, edtech developers and product managers could help identify instructional problems that AI systems could realistically address.

The Next Phase of the Research

The conversations described in this series represent an early attempt to document how teachers are navigating the arrival of generative AI.

As schools continue experimenting with these tools, the next challenge will be to develop governance frameworks that help educators evaluate when and how AI should be used in learning environments.

Our research team is beginning the next phase of this work by partnering with school districts to develop guidance for AI governance and inviting edtech companies interested in exploring these questions collaboratively.

Rather than assuming that AI will inevitably transform classrooms, this phase of the project will focus on identifying the conditions under which AI tools actually support teaching and learning and how to reduce harm when they don’t.

The fourth grade teacher’s question remains a useful guide: What can I actually use this for in math?

Until the answer becomes clearer, many teachers will likely continue doing what professionals in any field do when new technologies appear: experimenting cautiously, adopting what works and relying on their judgment to decide where or if the tool belongs.

If your school, district, organization, or edtech company is interested in learning more about joining our next project on AI governance, contact our research team at research@edsurge.com.

The Paris-based company is building out ‘cutting-edge’ European data centres with a total capacity ambition of 200MW by 2027.

French AI start-up Mistral has raised $830m in its first debt financing, for the purposes of funding its data centre near Paris.

The company said the deal, supported by a consortium of seven “top-tier” global banks, would pay for Nvidia Grace Blackwell infrastructure with 13,800 Nvidia GB300 GPUs at the “cutting-edge” centre, bringing powered capacity to 44MW.

The data centre at Bruyères-le-Châtel, scheduled to be operational in the first half of this year, was previously earmarked to train AI models belonging to Mistral and its customers, while also “delivering high-performance inference services”, according to the company.

Last month, Mistral said it would spend over $1.4bn in Sweden on digital infrastructure, including a data centre, building towards its stated goal of 200MW of capacity across Europe by 2027.

“Scaling our infrastructure in Europe is critical to empower our customers and to ensure AI innovation and autonomy remain at the heart of Europe,” said Arthur Mensch, CEO of Mistral AI.

“We will continue to invest in this area, given the surging and sustained demand from governments, enterprises and research institutions seeking to build their own customised AI environment, rather than depend on third-party cloud providers.”

Mistral said it is building a “vertically integrated AI company” comprising “frontier open-weight models, deep enterprise integration, production deployments and its own compute infrastructure”.

It counts organisations in the tech, retail, logistics and public sectors among its customers, and has already partnered with the likes of AMSL, Ericsson and the European Space Agency to train models on their proprietary data.

Earlier this month, Mistral launched both ‘Small 4’, the newest model in its fully open-source ‘Small’ series with an aim of consolidating capabilities of its flagship models, and ‘Forge’, a platform that lets enterprises build custom models trained on their own data.

Last September, the 2023-founded French AI darling announced a Series C raise of around $2bn at a post-money valuation of more than $13bn, led by Dutch chipmaker ASML. Existing investors DST Global, Andreessen Horowitz, Bpifrance, General Catalyst, Index Ventures, Lightspeed and Nvidia took part.

Although a frontrunner in the European AI space, Mistral is far behind US competitors such as OpenAI and Anthropic in terms of funding levels and valuations.

Mistral is a founding member of the Nvidia Nemotron Coalition. As part of the initiative, Mistral and Nvidia plan to co-develop frontier open-source AI models.

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NASA has awarded Intuitive Machines a $180.4 million contract to deliver seven science payloads to a carefully chosen site near the lunar south pole. The Houston based company will use one of its larger lander configurations for the mission, designated IM-5, with a target landing date of around 2030 at Mons Malapert. The location was selected for good reason. The ridge maintains fairly consistent line of sight with Earth, receives relatively steady sunlight, and sits close to permanently shadowed regions that may hold water ice, a resource that could prove critical to sustaining long term human operations on the Moon.

The lander arrives loaded with instruments ready to start collecting data from the moment it touches down. A stereo camera package developed at NASA’s Langley Research Center, called the Stereo Cameras for Lunar Plume Surface Studies, will capture how the descent engines disturb the fine lunar soil, information that will help engineers design landing systems that cause less disruption to the surface. A near infrared spectrometer mounted on a small rover from Honeybee Robotics, led by NASA’s Ames Research Center, will then scan for minerals and potential ice deposits while also measuring surface temperatures and mapping how the soil composition varies across the landing area.

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A mass spectrometer called MSolo, built at NASA’s Kennedy Space Center, will analyze gases present at the landing site immediately after touchdown, focusing on lightweight molecules that could prove useful for future lunar explorers. Radiation monitoring is handled by a set of four detectors developed by the Korea Astronomy and Space Science Institute, measuring surface exposure levels to assess risks for both equipment and future crew while also providing insight into the geological history of the surrounding area.

A set of small sensors aboard the Australian Space Agency’s Roo-ver will track how landing plumes interact with surface materials across varying distances over time, part of NASA Goddard Space Flight Center’s Multifunctional Nanosensor Platform. The Roo-ver will also demonstrate its ability to navigate and move independently across uneven lunar terrain. A Laser Retroreflector Array, also out of Goddard, rounds out the payload with a compact set of mirrors designed to bounce laser signals back to orbiting spacecraft, improving navigation accuracy for future missions passing overhead or coming in to land nearby and helping establish reliable reference points across the lunar surface.

Rounding out the cargo is Sanctuary on the Moon, a time capsule developed in France containing information about human civilization, science, technology, culture, and the human genome, etched onto 24 durable synthetic sapphire discs. It is built to last, and designed to be found.
[Source]

• Google TurboQuant reduces memory strain while maintaining accuracy across demanding workloads
• Vector compression reaches new efficiency levels without additional training requirements
• Key-value cache bottlenecks remain central to AI system performance limits

Large language models (LLMs) depend heavily on internal memory structures that store intermediate data for rapid reuse during processing.

One of the most critical components is the key-value cache, described as a “high-speed digital cheat sheet” that avoids repeated computation.