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Switzerland’s unique AI path: Blending innovation, governance, and local empowerment

In his recent blog post ‘Advancing Swiss AI Trinity: Zurich’s entrepreneurship, Geneva’s governance, and Communal subsidiarity,’ Jovan Kurbalija proposes a distinctive roadmap for Switzerland to navigate the rapidly evolving landscape of AI. Rather than mimicking the AI power plays of the United States or China, Kurbalija argues that Switzerland can lead by integrating three national strengths: Zurich’s thriving innovation ecosystem, Geneva’s global leadership in governance, and the country’s foundational principle of subsidiarity rooted in local decision-making.

Zurich, already a global tech hub, is positioned to drive cutting-edge development through its academic excellence and robust entrepreneurial culture. Institutions like ETH Zurich and the presence of major tech firms provide a fertile ground for collaborations that turn research into practical solutions.

With AI tools becoming increasingly accessible, Kurbalija emphasises that success now depends on how societies harness the interplay of human and machine intelligence—a field where Switzerland’s education and apprenticeship systems give it a competitive edge. Meanwhile, Geneva is called upon to spearhead balanced international governance and standard-setting for AI.

Kurbalija stresses that AI policy must go beyond abstract discussions and address real-world issues—health, education, the environment—by embedding AI tools in global institutions and negotiations. He notes that Geneva’s experience in multilateral diplomacy and technical standardisation offers a strong foundation for shaping ethical, inclusive AI frameworks.

The third pillar—subsidiarity—empowers Swiss cantons and communities to develop AI that reflects local values and needs. By supporting grassroots innovation through mini-grants, reimagining libraries as AI learning hubs, and embedding AI literacy from primary school to professional training, Switzerland can build an AI model that is democratic and inclusive.

Why does it matter?

Kurbalija’s call to action is clear: with its tools, talent, and traditions aligned, Switzerland must act now to chart a future where AI serves society, not the other way around.

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Google Gemini now summarizes PDFs with actionable prompts in Drive

Google is expanding Gemini’s capabilities by allowing the AI assistant to summarize PDF documents directly in Google Drive—and it’s doing more than just generating summaries.

Users will now see clickable suggestions like drafting proposals or creating interview questions based on resume content, making Gemini a more proactive productivity tool.

However, this update builds on earlier integrations of Gemini in Drive, which now surface pop-up summaries and action prompts when a PDF is opened.

Users with smart features and personalization turned on will notice a new preview window interface, eliminating the need to open a separate tab.

Gemini’s PDF summaries are now available in over 20 languages and will gradually roll out over the next two weeks.

The feature supports personal and business accounts, including Business Standard/Plus users, Enterprise tiers, Gemini Education, and Google AI Pro and Ultra plans.

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Nvidia’s Huang: ‘The new programming language is human’

Speaking at London Tech Week, Nvidia CEO Jensen Huang called AI ‘the great equaliser,’ explaining how AI has transformed who can access and control computing power.

In the past, computing was limited to a select few with technical skills in languages like C++ or Python. ‘We had to learn programming languages. We had to architect it. We had to design these computers that are very complicated,’ Huang said.

That’s no longer necessary, he explained. ‘Now, all of a sudden, there’s a new programming language. This new programming language is called ‘human’,’ Huang said, highlighting how AI now understands natural language commands. ‘Most people don’t know C++, very few people know Python, and everybody, as you know, knows human.’

He illustrated his point with an example: asking an AI to write a poem in the style of Shakespeare. The AI delivers, he said—and if you ask it to improve, it will reflect and try again, just like a human collaborator.

For Huang, this shift is not just technical but transformational. It makes the power of advanced computing accessible to billions, not just a trained few.

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Meta bets big on AI, partners with Scale AI in strategic move

Meta Platforms has made a major move in the AI space by investing $14.8 billion in Scale AI, acquiring a 49% stake and pushing the data-labelling startup’s valuation past $29 billion.

As part of the deal, Scale AI founder Alexandr Wang will join Meta’s leadership to head its new superintelligence unit, while continuing to serve on Scale AI’s board. The investment deepens Meta’s commercial ties with Scale and is seen as a strategic step to secure top-tier AI expertise.

Scale AI will use the funds to drive innovation and strengthen client partnerships, while also providing partial liquidity to shareholders and equity holders. Jason Droege, Scale’s Chief Strategy Officer and former Uber Eats executive, will serve as interim CEO.

‘This partnership is a testament to our team’s work and the scale of opportunity ahead,’ said Droege. Wang added, ‘Meta’s investment affirms the limitless path forward for AI and Scale’s role in bridging human values with transformative technologies.’

Scale will remain independent, continuing to support AI labs, corporations, and government agencies with data infrastructure as the race for AI dominance intensifies.

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Meta’s V-JEPA 2 teaches AI to think, plan, and act in 3D space

Meta has released V-JEPA 2, an open-source AI model designed to understand and predict real-world environments in 3D. Described as a ‘world model’, it enables machines to simulate physical spaces—offering a breakthrough for robotics, self-driving cars, and intelligent assistants.

Unlike traditional AI that relies on labelled data, V-JEPA 2 learns from unlabelled video clips, building an internal simulation of how the world works. However, now, AI can reason, plan, and act more like humans.

Based on Meta’s JEPA architecture and containing 1.2 billion parameters, the model improves significantly on action prediction and environmental modelling compared to its predecessor.

Meta says this approach mirrors how humans intuitively understand cause and effect—like predicting a ball’s motion or avoiding people in a crowd. V-JEPA 2 helps AI agents develop this same intuition, making them more adaptive in dynamic, unfamiliar situations.

Meta’s Chief AI Scientist Yann LeCun describes world models as ‘abstract digital twins of reality’—vital for machines to understand and predict what comes next. This effort aligns with Meta’s broader push into AI, including a planned $14 billion investment in Scale AI for data labelling.

V-JEPA 2 joins a growing wave of interest in world models. Google DeepMind is building its own called Genie, while AI researcher Fei-Fei Li recently raised $230 million for her startup World Labs, focused on similar goals.

Meta believes V-JEPA 2 brings us closer to machines that can learn, adapt, and operate in the physical world with far greater autonomy and intelligence.

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AI video tool Veo 3 Fast rolls out for Gemini and Flow users

Google has introduced Veo 3 Fast, a speedier version of its AI video-generation tool that promises to cut production time in half.

Now available to Gemini Pro and Flow Pro users, the updated model creates 720p videos more than twice as fast as its predecessor—marking a step forward in scaling Google’s video AI infrastructure.

Gemini Pro subscribers can now generate three Veo 3 Fast videos daily as part of their plan. Meanwhile, Flow Pro users can create videos using 20 credits per clip, significantly reducing costs compared to previous models. Gemini Ultra subscribers enjoy even more generous limits under their premium tier.

The upgrade is more than a performance boost. According to Google’s Josh Woodward, the improved infrastructure also paves the way for smoother playback and better subtitles—enhancements that aim to make video creation more seamless and accessible.

Google also tests voice prompt capabilities, allowing users to express video ideas and watch them materialise on-screen.

Although Veo 3 Fast is currently limited to 720p resolution, it encourages creativity through rapid iteration. Users can experiment with prompts and edits without perfecting their first try.

While the results won’t rival Hollywood, the model opens up new possibilities for businesses, creators, and filmmakers looking to prototype video ideas or produce content without traditional filming quickly.

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Meta launches AI to teach machines physical reasoning

Meta Platforms has unveiled V-JEPA 2, an open-source AI model designed to help machines understand and interact with the physical world more like humans do.

The technology allows AI agents, including delivery robots and autonomous vehicles, to observe object movement and predict how those objects may behave in response to actions.

The company explained that just as people intuitively understand that a ball tossed into the air will fall due to gravity, AI systems using V-JEPA 2 gain a similar ability to reason about cause and effect in the real world.

Trained using video data, the model recognises patterns in how humans and objects move and interact, helping machines learn to reach, grasp, and reposition items more naturally.

Meta described the tool as a step forward in building AI that can think ahead, plan actions and respond intelligently to dynamic environments. In lab tests, robots powered by V-JEPA 2 performed simple tasks that relied on spatial awareness and object handling.

The company, led by CEO Mark Zuckerberg, is ramping up its AI initiatives to compete with rivals like Microsoft, Google, and OpenAI. By improving machine reasoning through world models such as V-JEPA 2, Meta aims to accelerate its progress toward more advanced AI.

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Nvidia announces new AI lab in UK and supercomputing wins in Europe

What began as a company powering 3D games in the 1990s has evolved into the backbone of the global AI revolution. Nvidia, once best known for its Riva TNT2 chips in consumer graphics cards like the Elsa Erazor III, now sits at the centre of scientific computing, defence, and national-scale innovation.

While gaming remains part of its identity—with record revenue of $3.8 billion in Q1 FY2026—it now accounts for less than 9% of Nvidia’s $44.1 billion total revenue. The company’s trajectory reflects its founder Jensen Huang’s ambition to lead beyond the gaming space, targeting AI, supercomputing, and global infrastructure.

Recent announcements reinforce this shift. Huang joined UK Prime Minister Sir Keir Starmer to open London Tech Week, affirming Nvidia’s commitment to launch an AI lab in the UK, as the government commits £1 billion to AI compute by 2030.

Nvidia also revealed its Rubin-Vera superchip will power Germany’s ‘Blue Lion’ supercomputer, and its Grace Hopper platform is at the heart of Jupiter—Europe’s first exascale AI system, located at the Jülich Supercomputing Centre.

Nvidia’s presence now spans continents and disciplines, from powering national research to driving breakthroughs in climate modelling, quantum computing, and structural biology.

‘AI will supercharge scientific discovery and industrial innovation,’ said Huang. And with systems like Jupiter poised to run a quintillion operations per second, the company’s growth story is far from over.

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TechNext launches forecasting system to guide R&D strategy

Global R&D spending now exceeds $2 trillion a year, yet many companies still rely on intuition rather than evidence to shape innovation strategies—often at great cost.

TechNext, co-founded by Anuraag Singh and MIT’s Prof. Christopher L. Magee, aims to change that with a newly patented system that delivers data-driven forecasts for technology performance.

Built on large-scale empirical datasets and proprietary algorithms, the system enables organisations to anticipate which technologies are likely to improve most rapidly.

‘R&D has become one of the fastest-growing expenses for companies, yet most decisions still rely on intuition rather than data,’ said Singh. ‘We have been flying blind’

The tool has already drawn attention from major stakeholders, including the United States Air Force, multinational firms, VCs, and think tanks.

By quantifying the future of technologies—from autonomous vehicle perception systems to clean energy infrastructure—TechNext promises to help decision-makers avoid expensive dead ends and focus on long-term winners.

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UK government backs AI to help teachers and reduce admin

The UK government has unveiled new guidance for schools that promotes the use of AI to reduce teacher workloads and increase face-to-face time with pupils.

The Department for Education (DfE) says AI could take over time-consuming administrative tasks such as lesson planning, report writing, and email drafting—allowing educators to focus more on classroom teaching.

The guidance, aimed at schools and colleges in the UK, highlights how AI can assist with formative assessments like quizzes and low-stakes feedback, while stressing that teachers must verify outputs for accuracy and data safety.

It also recommends using only school-approved tools and limits AI use to tasks that support rather than replace teaching expertise.

Education unions welcomed the move but said investment is needed to make it work. Leaders from the NAHT and ASCL praised AI’s potential to ease pressure on staff and help address recruitment issues, but warned that schools require proper infrastructure and training.

The government has pledged £1 million to support AI tool development for marking and feedback.

Education Secretary Bridget Phillipson said the plan will free teachers to deliver more personalised support, adding: ‘We’re putting cutting-edge AI tools into the hands of our brilliant teachers to enhance how our children learn and develop.’

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