Privacy and regulatory concerns have long hindered AI’s reliance on data, especially in sensitive fields like healthcare and life sciences. Apheris, a German startup co-founded by Robin Röhm, aims to solve this problem using federated computing—a decentralised approach that trains AI models without moving sensitive data.
The company’s approach is gaining traction among prominent clients like Roche and hospitals, and its technology is already being used in collaborative drug discovery efforts by pharmaceutical giants such as Johnson & Johnson and Sanofi. Apheris recently secured $8.25 million in Series A funding led by OTB Ventures and eCAPITAL, bringing its total funding to $20.8 million.
That follows a pivotal shift in 2023 to focus on the needs of data owners in the pharmaceutical and life sciences sectors. The pivot has paid off, quadrupling the company’s revenue since launching its redefined product, the Apheris Compute Gateway, which securely bridges local data and AI models.
With its new funding, Apheris plans to expand its team and refine its AI-driven solutions for complex challenges like protein prediction. By prioritising data security and privacy, the company aims to unlock previously inaccessible data for innovation, addressing a core barrier to AI’s transformative potential in life sciences.
In recent years, debates over AI have intensified, oscillating between catastrophic warnings and optimistic visions. Technologists, once at the forefront of calling for caution, have been overshadowed by the tech industry’s emphasis on generative AI’s lucrative potential.
Dismissed as ‘AI doomers,’ critics warn of existential threats—from mass harm to societal destabilisation—while Silicon Valley champions the transformative benefits of AI, urging fewer regulations to accelerate innovation. 2023 marked a pivotal year for AI awareness, with luminaries like Elon Musk and over 1,000 experts calling for a development pause, citing profound risks.
US President Biden’s AI executive order aimed to safeguard Americans, and regulatory discussions gained mainstream traction. However, 2024 saw this momentum falter as investment in AI skyrocketed and safety-focused voices dwindled.
High-profile debates, like California’s SB 1047—a bill addressing catastrophic AI risks—ended in a veto, highlighting resistance from powerful tech entities. Critics argued that such legislation stifled innovation, while proponents lamented the lack of long-term safety measures.
Amid this tug-of-war, optimistic narratives, like Marc Andreessen’s essay ‘Why AI Will Save the World,’ gained prominence. Advocating rapid, unregulated AI development, Andreessen and others argued this approach would bolster competitiveness and prevent monopolisation.
Yet, detractors questioned the ethics of prioritising profit over societal concerns, especially as cases like AI-driven child safety failures underscored emerging risks.
Why does it matter?
Looking ahead to 2025, the AI safety movement faces an uphill battle. Policymakers hint at revisiting stalled regulations, signalling hope for progress. However, with influential players opposing stringent oversight, the path to balanced AI governance remains uncertain. As society grapples with AI’s rapid evolution, the challenge lies in addressing its vast potential and inherent risks.
OpenAI has unveiled plans to transition its for-profit arm into a Delaware-based public benefit corporation (PBC). The move aims to attract substantial investment as the competition to develop advanced AI intensifies, and the proposed structure intends to prioritise societal interests alongside shareholder value, setting the company apart from traditional corporate models.
The shift marks a significant step for OpenAI, which started as a nonprofit in 2015 before establishing a for-profit division to fund high-cost AI development. Its latest funding round, valued at $157 billion, necessitated the structural change to eliminate a profit cap for investors, enabling greater financial backing. The nonprofit will retain a substantial stake in the restructured company, ensuring alignment with its original mission.
OpenAI faces criticism and legal challenges over the move. Elon Musk, a co-founder and vocal critic, has filed a lawsuit claiming the changes prioritise profit over public interest. Meta Platforms has also urged regulatory intervention. Legal experts suggest the PBC status offers limited enforcement of its mission-focused commitments, relying on shareholder influence to maintain the balance between profit and purpose.
By adopting this structure, OpenAI aims to align with competitors like Anthropic and xAI, which have similarly raised billions in funding. Analysts view the move as essential for securing the resources needed to remain a leader in the AI sector, though significant hurdles remain.
A humanoid robot named CUE6 has captivated audiences in Japan with its basketball prowess, achieving a Guinness World Record for the longest shot by a humanoid robot. Developed by Toyota engineers, the robot’s achievement highlights the potential of AI in mimicking human precision and adapting to complex tasks.
CUE6’s journey began in 2017 as an experimental project. Starting with LEGO-based prototypes, the team gradually refined the robot’s capabilities, culminating in its ability to dribble, handle balls, and adapt its movements based on real-time analysis. By 2019, the robot had already achieved a remarkable milestone: 2,020 consecutive free throws. The latest version, CUE6, demonstrated the power of AI by recalibrating its shot after a miss to secure the record on its second attempt.
Toyota engineers view CUE6 as more than a novelty. The project serves as a testing ground for AI systems capable of dynamic learning and adaptation. While the immediate goal of creating a robot that can dunk like Michael Jordan remains aspirational, the technologies developed for CUE6 in Japan have far-reaching implications beyond sports, from automation to healthcare.
The automation industry is evolving beyond Robotic Process Automation (RPA) to embrace multi-agentic AI systems capable of autonomous decision-making. Companies like Automation Anywhere are at the forefront, introducing AI Agent Studio to help enterprises streamline operations across finance, HR, IT, and customer service. These agents accelerate workflows, reduce costs, and enhance operational efficiency by up to 90%.
Automation Anywhere executives highlight how these advanced AI agents surpass earlier systems by integrating retrieval-augmented generation (RAG) with decision-making capabilities. Unlike static models, these agents learn from user interactions, ensuring adaptability and accuracy. Their blend of RAG and AI offers enterprises scalable, secure, and flexible solutions tailored to specific needs.
The shift from RPA to Agentic Process Automation (APA) marks a milestone, enabling automation of complex processes while fostering innovation and redefining job roles. With applications spanning healthcare, finance, retail, and manufacturing, AI agents are poised to disrupt industries and drive significant growth.
Jet.AI has introduced a cutting-edge AI model, ‘Ava,’ designed to revolutionise private jet bookings. Customers can communicate with Ava via phone or text to receive real-time pricing, aircraft availability, and personalised guidance. According to Jet.AI‘s founder Mike Winston, the agentic AI model offers a cost-efficient solution that enhances customer convenience while streamlining operations.
Updates to Jet.AI’s CharterGPT app accompany Ava’s launch, providing features like carbon tracking, enhanced notifications for travel updates, and deep linking for seamless access to trip details discussed with the AI. These upgrades prioritise eco-conscious decisions and user-friendly functionality.
Agentic AI, which operates autonomously to handle entire processes, is gaining momentum across industries. Intuit, for instance, plans to expand agentic AI across its platforms like TurboTax and QuickBooks through 2025, enhancing efficiency while maintaining human expert support when necessary.
Saudi Arabia’s Diriyah Gate Development Authority (DGDA) and Amazon collaborate to enhance cultural and technical engagement, focusing on enriching visitor experiences at Diriyah’s historical sites. That initiative integrates Amazon’s innovative technologies, including Alexa, to provide accessible and comprehensive cultural and historical content.
Aligned with Saudi Arabia’s Vision 2030, the collaboration aims to establish Diriyah as a global destination for culture and heritage while contributing to the tourism, cultural, and historical sectors. Additionally, the partnership supports community and economic growth by highlighting regional products, enabling local sellers to reach wider audiences through Amazon.sa, and fostering local content development.
Diriyah Gate Development Authority (DGDA) and Amazon are committed to preserving and celebrating the rich cultural heritage of Saudi Arabia. By connecting the Kingdom’s traditions and history with cutting-edge technology, they aim to create transformative experiences that resonate with visitors and strengthen Diriyah’s significance as a cultural hub on the global stage.
AI became a defining feature of the 2024 Paris Olympics. Athletes benefited from AI-driven tools like chatbots for cybersecurity and systems offering 360-degree performance replays. AI also enhanced event safety with software monitoring crowd dynamics and abandoned objects, paving the way for future global events.
Outside the Olympics, AI was integrated into consumer technology. Car manufacturers such as Volkswagen and XPeng introduced AI-assisted features, transforming vehicles into adaptive companions. Volkswagen’s ChatGPT integration enhanced in-car assistance, while XPeng’s AI-defined car promised autonomous decision-making.
Flying taxis generated excitement but failed to soar as anticipated. Despite setbacks, companies like Volocopter and Hyundai showcased designs, while Joby secured a UK license, with commercial flights expected by 2025. Tesla unveiled the Cybercab, relying solely on AI for navigation, with a 2026 market launch planned.
Smartphones and smart glasses underwent significant innovation. Bendable phones by Motorola and Lenovo offered new flexibility, and Samsung’s toughened foldable displays impressed. Meta’s neural-interface glasses broke ground but raised privacy concerns, demonstrating the balance between progress and ethics in technology.
Researchers at the University of Toronto claim to have demonstrated ‘negative time’ through quantum experiments involving light and matter interactions. The phenomenon, where events appear to occur before their cause, challenges conventional perceptions of time. While previous theories dismissed such results as illusions, the team measured interactions between photons and atoms, finding durations that defy standard understanding.
The experiments, led by Daniela Angulo, required precise laser calibration to ensure accuracy. Researchers clarified that ‘negative time’ does not imply time travel but highlights quantum mechanics’ probabilistic nature. The findings do not breach Einstein’s theory of relativity, as no information travelled faster than light.
The concept has sparked global debate. Some critics argue it reflects phase shifts in photons rather than true temporal reversal. Despite controversy, the researchers see their work as a step toward uncovering quantum mysteries, though practical applications remain speculative.
If AI was the buzzword for 2023 and 2024, quantum computing looks set to claim the spotlight in the years ahead. Despite growing interest, much remains unknown about this transformative technology, even as leading companies explore its immense potential.
Quantum computing and AI stand as two revolutionary technologies, each with distinct principles and goals. Quantum systems operate on the principles of quantum mechanics, using qubits capable of existing in multiple states simultaneously due to superposition. Such systems can address problems far beyond the reach of classical computers, including molecular simulations for medical research and complex optimisation challenges.
AI and quantum computing intersect in areas like machine learning, though AI still depends on classical computing infrastructure. Significant hurdles remain for quantum technology, including qubit errors and scalability. The extreme sensitivity of qubits to external factors, such as vibrations and temperature, complicates their control.
Experts suggest quantum computers could become practical within 10 to 20 years. Classical computers are unlikely to be replaced, as quantum systems will primarily focus on solving tasks beyond classical capabilities. Leading companies are working to shorten development timelines, with advancements poised to transform the way technology is utilised.
Huge investments in quantum computing
Investments in quantum computing have reached record levels, with start-ups raising $1.5 billion across 50 funding rounds in 2024. Figure like this one nearly doubles the $785 million raised the previous year, setting a new benchmark. The growth in AI is partly driving these investments, as quantum computing promises to handle AI’s significant computational demands more efficiently.
Quantum computing offers unmatched speed and energy efficiency, with some estimates suggesting energy use could be reduced by up to 100 times compared to traditional supercomputers. As the demand for faster, more sustainable computing grows, quantum technologies are emerging as a key solution.
Microsoft and Atom Computing announce breakthrough
In November 2024, Microsoft and Atom Computing achieved a milestone in quantum computing. Their system linked 24 logical qubits using just 80 physical qubits, setting a record in efficiency. This advancement could transform industries like blockchain and cryptography by enabling faster problem-solving and enhancing security protocols.
Despite the challenges of implementing such systems, both companies are aiming to release a 1,000-qubit quantum computer by 2025. The development could accelerate the adoption of quantum technologies across various sectors, paving the way for breakthroughs in areas such as machine learning and materials science.
Overcoming traditional computing’s limitations
Start-ups like BlueQubit are transforming quantum computing into a practical tool for industries. The San Francisco-based company has raised $10 million to launch its Quantum-Software-as-a-Service platform, enabling businesses to use quantum processors and emulators that perform tasks up to 100 times faster than conventional systems.
Industries such as finance and pharmaceuticals are already leveraging quantum optimisation. Specialised algorithms are addressing challenges like financial modelling and drug discovery, showcasing quantum computing’s potential to surpass traditional systems in tackling complex problems.
Google among giants pushing quantum computing
Google has recently introduced its cutting-edge quantum chip, Willow, capable of solving a computational problem in just five minutes. Traditional supercomputers would require approximately 10 septillion years for the same task.
The achievement has sparked discussions about quantum computing’s link to multiverse theories. Hartmut Neven, head of Google’s Quantum AI team, suggested the performance might hint at parallel universes influencing quantum calculations. Willow’s success marks significant advancements in cryptography, material science, and artificial intelligence.
Commercialisation is already underway
Global collaborations are fast-tracking quantum technology’s commercialisation. SDT, a Korean firm, and Finnish start-up SemiQon have signed an agreement to integrate SemiQon’s silicon-based quantum processing units into SDT’s precision measurement systems.
SemiQon’s processors, designed to work with existing semiconductor infrastructure, lower production costs and enhance scalability. These partnerships pave the way for more stable and cost-effective quantum systems, bringing their use closer to mainstream industries.
Quantum technologies aiding mobile networks
Telefonica Germany and AWS are exploring quantum applications in mobile networks. Their pilot project aims to optimise mobile tower placement, improve network security with quantum encryption, and prepare for future 6G networks.
Telefonica’s migration of millions of 5G users to AWS cloud infrastructure demonstrates how combining quantum and cloud technologies can enhance network efficiency. The project highlights the growing impact of quantum computing on telecommunications.
Addressing emerging risks
Chinese researchers at Shanghai University have exposed the potential threats quantum computing poses to existing encryption standards. Using a D-Wave quantum computer, they breached algorithms critical to modern cryptographic systems, including AES-256, commonly used for securing cryptocurrency wallets.
Although current quantum hardware faces environmental and technical limitations, researchers stress the urgent need for quantum-resistant cryptography. New encryption methods are essential to safeguard digital systems against future quantum-based vulnerabilities.
Quantum computing promises revolutionary capabilities but must overcome significant challenges in scaling and stability. Its progress depends on interdisciplinary collaboration in physics, engineering, and economics. While AI thrives on rapid commercial investment, quantum technology requires long-term support to fulfil its transformative potential.
