Efforts to strengthen technological competitiveness in Europe focus on advancing AI capabilities, developing new forms of talent and improving access to investment.
Discussions at the CTx Tech Experience in Seville highlighted a growing consensus that innovation must scale more effectively if the region is to compete globally.
Participants emphasised that Europe continues to face structural challenges, including fragmented markets, regulatory complexity and limited capital for high-growth companies.
These constraints have made it more difficult for startups to expand, prompting calls for stronger coordination between public institutions and private investors.
AI is increasingly viewed as the foundation of the transformation. Industry leaders pointed to the emergence of new business opportunities driven by AI, alongside the need to translate innovation into scalable commercial outcomes.
At the same time, labour market dynamics are shifting towards hybrid skillsets that combine technical expertise with business understanding and critical thinking.
In such a context, strengthening Europe’s innovation capacity is seen as essential to competing with global powers such as the US and China.
The Trump Administration unveiled a national AI framework to boost competitiveness, security, and benefits for Americans. The plan seeks to ensure that AI innovation supports all citizens while maintaining public trust in the technology.
Six key objectives form the foundation of the policy. These include protecting children online, empowering parents with tools to manage digital safety, strengthening communities and small businesses, respecting intellectual property, defending free speech, and fostering innovation.
The framework also prioritises workforce development to prepare Americans for AI-driven job opportunities.
Federal uniformity is considered critical to the plan’s success. The Administration warns that a patchwork of state regulations could stifle innovation and reduce the United States’ ability to lead globally.
Congress is encouraged to collaborate closely to implement the framework nationwide.
The Administration emphasises that the United States must lead the AI race, ensuring the benefits of AI reach all Americans while addressing challenges such as privacy, security, and equitable access to opportunities.
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Elon Musk unveiled his ambitious Terafab project in Austin, describing it as the ‘most epic chip-building exercise in history.’ The initiative, led by Tesla, xAI, and SpaceX, aims to produce 1 trillion watts of compute power annually, much of it intended for space applications.
The project will start with a state-of-the-art semiconductor manufacturing facility in Austin, supporting AI development, humanoid robotics, and space data centres. Musk highlighted current supply chain limitations, stating that building Terafab is essential to secure the chips his companies need.
Musk also shared his vision for a future shaped by ‘amazing abundance.’ Plans include launching satellites from the lunar surface and enabling civilian space travel to destinations such as Saturn, blending cutting-edge technology with long-term space ambitions.
Terafab represents a bold attempt to merge AI, robotics, and space exploration, positioning Musk’s companies at the forefront of next-generation technology and extraterrestrial innovation.
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Members of the European Parliament are calling for more rapid progress in implementing the bloc’s digital competition framework, with particular focus on the Digital Markets Act.
In a recent resolution, lawmakers urged the European Commission to ensure timely and effective enforcement of the rules designed to regulate large online platforms. The legislation aims to address concerns around market dominance and promote fair competition across the digital economy.
The discussions reflect ongoing concerns that delays in enforcement could undermine the framework’s effectiveness, particularly as major technology companies continue to expand their influence. Platforms such as Google, Apple and Meta are among those expected to comply with the new obligations.
At the same time, policymakers are balancing regulatory oversight with the need to maintain innovation and competitiveness. The debate forms part of a broader effort in the EU to strengthen digital governance and reinforce the region’s position in global technology markets.
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A growing number of individuals worldwide are participating in a new digital economy built around supplying data for AI systems.
Through platforms such as Kled AI and Silencio, users upload videos, audio recordings and personal interactions in exchange for payment, contributing to the development of increasingly sophisticated AI models.
Such a trend reflects a broader shift in the AI industry, where demand for high-quality human-generated data is rising as traditional web-based sources become more limited.
Researchers suggest that human data remains essential for improving system performance and modelling behaviour beyond existing datasets. As a result, data marketplaces have emerged as an alternative supply mechanism.
Economic considerations often shape participation. In regions facing limited employment opportunities or currency instability, earning income in global currencies can provide a meaningful financial incentive.
At the same time, similar practices are expanding in higher-income countries, where individuals seek supplementary income streams amid rising living costs.
However, the model introduces complex trade-offs.
Contributors may grant extensive usage rights over their data, sometimes on a long-term or irreversible basis. Experts note that such arrangements can reduce control over how personal information is reused, including in contexts not initially anticipated.
Concerns also extend to issues such as data security, transparency and the potential for misuse in areas including synthetic media and identity replication.
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AI is increasingly taking over the early stages of hiring, with 89% of UK recruiters planning to use it more in the recruitment process this year.
For graduates like Bhuvana Chilukuri, a third-year business student at Queen Mary University London who has applied for over 100 roles without a single offer, this means facing automatic CV screening and AI video interviews, with some rejections arriving in under two minutes.
The scale of the problem is significant on both sides. Denis Machuel, CEO of Adecco, one of the world’s largest recruitment specialists, noted that candidates now need to send an average of 200 applications to receive a single job offer.
Meanwhile, firms like law firm Mishcon de Reya received 5,000 applications for just 35 roles in its last hiring round, a volume driven in part by candidates using AI to write and mass-submit applications, prompting employers to deploy AI to filter them out.
Supporters of AI hiring tools argue they can reduce human bias and deliver more consistent decisions. But critics warn the process strips candidates of their personality and humanity, with applicants describing feeling ‘robotic’ and ‘monotone’ while recording answers into a screen with no human interaction.
Machuel acknowledged the tension, calling for AI and human judgement to be combined at the right moments in the process, arguing that balance is the only way to break what he described as a growing ‘arms race.’
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A global survey by Anthropic of over 80,000 Claude users across 159 countries highlights how AI is increasingly shaping work, learning, and everyday life. Respondents cite benefits in productivity, skill-building, and task management, with AI helping save time and reduce mental effort.
Users highlight AI’s role in learning and personal growth, helping them access knowledge, gain confidence, and pursue careers or entrepreneurship previously out of reach. The study also shows AI assisting in organisation, research synthesis, and emotional support.
Alongside these benefits, concerns remain widespread. Reliability issues, job disruption, cognitive dependence, and privacy risks are frequently cited.
Many users describe navigating both advantages and potential harms, reflecting Anthropic’s ‘light and shade’ concept: AI can empower, yet create new risks and expectations.
Regional views differ: South America, Africa, and parts of Asia see AI as an opportunity, while Europe and the US focus on complexity, workload, and economic impact.
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A new wave of AI development is increasingly relying on real-world human behaviour, with DoorDash moving to tap its gig workforce to generate training data for robotics systems.
DoorDash has launched a standalone app called Tasks, allowing couriers to earn money by recording themselves performing everyday activities such as folding clothes, washing dishes or making a bed. The collected data is used to train AI and robotics models to understand physical environments and human interactions better.
The move reflects a broader shift in AI training, where companies are seeking physical, real-world data rather than relying solely on text and images. Such data is essential for building systems capable of performing tasks in dynamic environments, including humanoid robots and autonomous machines.
Other companies are pursuing similar strategies. Uber and Instawork have tested gig-based data-collection models, while robotics startups are using wearable devices, such as gloves and head-mounted cameras, to capture detailed motion data for training.
The Tasks app is currently being rolled out as a pilot, with DoorDash planning to expand the types of available assignments over time. Some tasks may also be integrated into the main Dasher app, including activities that support navigation or assist autonomous delivery systems.
As competition intensifies, access to large-scale physical data is becoming a critical advantage. DoorDash’s approach highlights how gig-economy platforms are increasingly integrated into the development of next-generation AI systems.
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A US senator has introduced a draft framework to establish nationwide AI rules, with a focus on child safety and copyright protection. The proposal seeks to create a unified federal approach to replace state laws that differ.
The plan would require developers to implement safeguards for minors, including age verification, data protection and mechanisms to report harm. Companies could also face legal action over failures linked to AI system design.
Copyright measures include new standards for identifying AI-generated content and preventing tampering. Authorities would also develop cybersecurity guidelines to support the transparency and authenticity of content.
Debate over this in the US continues over the balance between regulation and innovation, with some stakeholders warning of legal and economic risks. Discussions between lawmakers and the administration are expected to shape a final framework.
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AI adoption is accelerating across UK businesses, with 78% now using the technology in some capacity, rising to 85% among mid-sized organisations. A further 14% are exploring or planning implementation by 2026, reflecting the continued momentum behind AI adoption.
Despite widespread use, tangible results remain limited. Just 31% of UK businesses report a positive return on investment, while 18% say their AI initiatives have failed to deliver expected benefits. Another 16% indicate it is still too early to assess outcomes, highlighting the long lead times often associated with AI deployments.
A major issue lies in defining success. Only 41% of organisations using AI say they have a clear understanding of what success looks like, suggesting that AI adoption is often not matched by clear strategic planning, even among mid-sized firms, the most active adopters; fewer than half can articulate measurable goals.
The findings suggest that rapid uptake has outpaced organisational readiness. Many businesses are deploying AI tools without defining how they fit into workflows, what decisions they are meant to support, or whether the goal is efficiency, cost reduction, or growth.
For AI adoption to translate into real business value, companies will need stronger governance, clearer objectives, and measurable success criteria. Without that foundation, AI risks remaining an expensive experiment rather than a driver of long-term transformation.
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