Interdisciplinary approaches

Legal sector urged to plan for cultural change around AI

A digital agency has released new guidance to help legal firms prepare for wider AI adoption. The report urges practitioners to assess cultural readiness before committing to major technology investment.

Sherwen Studios collected views from lawyers who raised ethical worries and practical concerns. Their experiences shaped recommendations intended to ensure AI serves real operational needs across the sector.

The agency argues that firms must invest in oversight, governance and staff capability. Leaders are encouraged to anticipate regulatory change and build multidisciplinary teams that blend legal and technical expertise.

Industry analysts expect AI to reshape client care and compliance frameworks over the coming years. Firms prepared for structural shifts are likely to benefit most from long-term transformation.

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EU opens antitrust probe into Meta’s WhatsApp AI rollout

Brussels has opened an antitrust inquiry into Meta over how AI features were added to WhatsApp, focusing on whether the updated access policies hinder market competition. Regulators say scrutiny is needed as integrated assistants become central to messaging platforms.

Meta AI has been built into WhatsApp across Europe since early 2025, prompting questions about whether external AI providers face unfair barriers. Meta rejects the accusations and argues that users can reach rival tools through other digital channels.

Italy launched a related proceeding in July and expanded it in November, examining claims that Meta curtailed access for competing chatbots. Authorities worry that dominance in messaging could influence the wider AI services market.

EU officials confirmed the case will proceed under standard antitrust rules rather than the Digital Markets Act. Investigators aim to understand how embedded assistants reshape competitive dynamics in services used by millions.

European regulators say outcomes could guide future oversight as generative AI becomes woven into essential communications. The case signals growing concern about concentrated power in fast-evolving AI ecosystems.

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AI model boosts accuracy in ranking harmful genetic variants

Researchers have unveiled a new AI model that ranks genetic variants based on their severity. The approach combines deep evolutionary signals with population data to highlight clinically relevant mutations.

The popEVE system integrates protein-scale models with constraints drawn from major genomic databases. Its combined scoring separates harmful missense variants more accurately than leading diagnostic tools.

Clinical tests showed strong performance in developmental disorder cohorts, where damaging mutations clustered clearly. The model also pinpointed likely causal variants in unsolved cases without parental genomes.

Researchers identified hundreds of credible candidate genes with structural and functional support. Findings suggest that AI could accelerate rare disease diagnoses and inform precision counselling worldwide.

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New findings reveal untrained AI can mirror human brain responses

Researchers at Johns Hopkins report that brain-inspired AI architectures can display human-like neural activity before any training. Structural design may provide stronger starting points than data-heavy methods. The findings challenge long-held views about how machine intelligence forms.

Researchers tested modified transformers, fully connected networks, and convolutional networks across multiple variants. They compared untrained model responses with neural data from humans and primates viewing identical images. The approach allowed a direct measure of architectural influence.

Transformers and fully connected networks showed limited change when scaled. Convolutional models, by contrast, produced patterns that aligned more closely with human brain activity. Architecture appears to be a decisive factor early in development.

Untrained convolutional models matched aspects of systems trained on millions of images. The results suggest brain-like structures could cut reliance on vast datasets and energy-intensive computation. The implications may reshape how advanced models are engineered.

Further research will examine simple, biologically inspired learning rules. The team plans to integrate these mechanisms into future AI frameworks. The goal is to combine architecture and biology to accelerate meaningful advances.

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Mistral AI unveils new open models with broader capabilities

Yesterday, Mistral AI introduced Mistral 3 as a new generation of open multimodal and multilingual models that aim to support developers and enterprises through broader access and improved efficiency.

The company presented both small dense models and a new mixture-of-experts system called Mistral Large 3, offering open-weight releases to encourage wider adoption across different sectors.

Developers are encouraged to build on models in compressed formats that reduce deployment costs, rather than relying on heavier, closed solutions.

The organisation highlighted that Large 3 was trained with extensive resources on NVIDIA hardware to improve performance in multilingual communication, image understanding and general instruction tasks.

Mistral AI underlined its cooperation with NVIDIA, Red Hat and vLLM to deliver faster inference and easier deployment, providing optimised support for data centres along with options suited for edge computing.

A partnership that introduced lower-precision execution and improved kernels to increase throughput for frontier-scale workloads.

Attention was also given to the Ministral 3 series, which includes models designed for local or edge settings in three sizes. Each version supports image understanding and multilingual tasks, with instruction and reasoning variants that aim to strike a balance between accuracy and cost efficiency.

Moreover, the company stated that these models produce fewer tokens in real-world use cases, rather than generating unnecessarily long outputs, a choice that aims to reduce operational burdens for enterprises.

Mistral AI continued by noting that all releases will be available through major platforms and cloud partners, offering both standard and custom training services. Organisations that require specialised performance are invited to adapt the models to domain-specific needs under the Apache 2.0 licence.

The company emphasised a long-term commitment to open development and encouraged developers to explore and customise the models to support new applications across different industries.

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NVIDIA platform lifts leading MoE models

Frontier developers are adopting a mixture-of-experts architecture as the foundation for their most advanced open-source models. Designers now rely on specialised experts that activate only when needed instead of forcing every parameter to work on each token.

Major models, such as DeepSeek-R1, Kimi K2 Thinking, and Mistral Large 3, rise to the top of the Artificial Analysis leaderboard by utilising this pattern to combine greater capability with lower computational strain.

Scaling the architecture has always been the main obstacle. Expert parallelism requires high-speed memory access and near-instant communication between multiple GPUs, yet traditional systems often create bottlenecks that slow down training and inference.

NVIDIA has shifted toward extreme hardware and software codesign to remove those constraints.

The GB200 NVL72 rack-scale system links seventy-two Blackwell GPUs via fast shared memory and a dense NVLink fabric, enabling experts to exchange information rapidly, rather than relying on slower network layers.

Model developers report significant improvements once they deploy MoE designs on NVL72. Performance leaps of up to ten times have been recorded for frontier systems, improving latency, energy efficiency and the overall cost of running large-scale inference.

Cloud providers integrate the platform to support customers in building agentic workflows and multimodal systems that route tasks between specialised components, rather than duplicating full models for each purpose.

Industry adoption signals a shift toward a future where efficiency and intelligence evolve together. MoE has become the preferred architecture for state-of-the-art reasoning, and NVL72 offers a practical route for enterprises seeking predictable performance gains.

NVIDIA positions its roadmap, including the forthcoming Vera Rubin architecture, as the next step in expanding the scale and capability of frontier AI.

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OpenAI faced questions after ChatGPT surfaced app prompts for paid users

ChatGPT users complained after the system surfaced an unexpected Peloton suggestion during an unrelated conversation. The prompt appeared for a Pro Plan subscriber and triggered questions about ad-like behaviour. Many asked why paid chats were showing promotional-style links.

OpenAI said the prompt was part of early app-discovery tests, not advertising. Staff acknowledged that the suggestion was irrelevant to the query. They said the system is still being adjusted to avoid confusing or misplaced prompts.

Users reported other recommendations, including music apps that contradicted their stated preferences. The lack of an option to turn off these suggestions fuelled irritation. Paid subscribers warned that such prompts undermine the service’s reliability.

OpenAI described the feature as a step toward integrating apps directly into conversations. The aim is to surface tools when genuinely helpful. Early trials, however, have demonstrated gaps between intended relevance and actual outcomes.

The tests remain limited to selected regions and are not active in parts of Europe. Critics argue intrusive prompts risk pushing users to competitors. OpenAI said refinements will continue to ensure suggestions feel helpful, not promotional.

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Regulators question transparency after Mixpanel data leak

Mixpanel is facing criticism after disclosing a security incident with minimal detail, providing only a brief note before the US Thanksgiving weekend. Analysts say the timing and lack of clarity set a poor example for transparency in breach reporting.

OpenAI later confirmed its own exposure, stating that analytics data linked to developer activity had been obtained from Mixpanel’s systems. It stressed that ChatGPT users were not affected and that it had halted its use of the service following the incident.

OpenAI said the stolen information included names, email addresses, coarse location data and browser details, raising concerns about phishing risks. It noted that no advertising identifiers were involved, limiting broader cross-platform tracking.

Security experts say the breach highlights long-standing concerns about analytics companies that collect detailed behavioural and device data across thousands of apps. Mixpanel’s session-replay tools can be sensitive, as they can inadvertently capture private information.

Regulators argue the case shows why analytics providers have become prime targets for attackers. They say that more transparent disclosure from Mixpanel is needed to assess the scale of exposure and the potential impact on companies and end-users.

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OpenAI expands investment in mental health safety research

Yesterday, OpenAI launched a new grant programme to support external research on the connection between AI and mental health.

An initiative that aims to expand independent inquiry into how people express distress, how AI interprets complex emotional signals and how different cultures shape the language used to discuss sensitive experiences.

OpenAI also hopes that broader participation will strengthen collective understanding, rather than keeping progress confined to internal studies.

The programme encourages interdisciplinary work that brings together technical specialists, mental health professionals and people with lived experience. OpenAI is seeking proposals that can offer clear outputs, such as datasets, evaluation methods, or practical insights, that improve safety and guidance.

Researchers may focus on patterns of distress in specific communities, the influence of slang and vernacular, or the challenges that appear when mental health symptoms manifest in ways that current systems fail to recognise.

The grants also aim to expand knowledge of how providers use AI within care settings, including where tools are practical, where limitations appear and where risks emerge for users.

Additional areas of interest include how young people respond to different tones or styles, how grief is expressed in language and how visual cues linked to body image concerns can be interpreted responsibly.

OpenAI emphasises that better evaluation frameworks, ethical datasets and annotated examples can support safer development across the field.

Applications are open until 19 December, with decisions expected by mid-January. The programme forms part of OpenAI’s broader effort to invest in well-being and safety research, offering financial support to independent teams working across diverse cultural and linguistic contexts.

The company argues that expanding evidence and perspectives will contribute to a more secure and supportive environment for future AI systems.

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Quantum money meets Bitcoin: Building unforgeable digital currency

Quantum money might sound like science fiction, yet it is rapidly emerging as one of the most compelling frontiers in modern digital finance. Initially a theoretical concept, it was far ahead of the technology of its time, making practical implementation impossible. Today, thanks to breakthroughs in quantum computing and quantum communication, scientists are reviving the idea, investigating how the principles of quantum physics could finally enable unforgeable quantum digital money. 

Comparisons between blockchain and quantum money are frequent and, on the surface, appear logical, yet can these two visions of new-generation cash genuinely be measured by the same yardstick? 

Origins of quantum money 

Quantum money was first proposed by physicist Stephen Wiesner in the late 1960s. Wiesner envisioned a system in which each banknote would carry quantum particles encoded in specific states, known only to the issuing bank, making the notes inherently secure. 

Due to the peculiarities of quantum mechanics, these quantum states could not be copied, offering a level of security fundamentally impossible with classical systems. At the time, however, quantum technologies were purely theoretical, and devices capable of creating, storing, and accurately measuring delicate quantum states simply did not exist. 

For decades, Wiesner’s idea remained a fascinating thought experiment. Today, the rise of functional quantum computers, advanced photonic systems, and reliable quantum communication networks is breathing new life into the concept, allowing researchers to explore practical applications of quantum money in ways that were once unimaginable.

A new battle for the digital throne is emerging as quantum money shifts from theory to possibility, challenging whether Bitcoin’s decentralised strength can hold its ground in a future shaped by quantum technology.

The no-cloning theorem: The physics that makes quantum money impossible to forge

At the heart of quantum money lies the no-cloning theorem, a cornerstone of quantum mechanics. The principle establishes that it is physically impossible to create an exact copy of an unknown quantum state. Any attempt to measure a quantum state inevitably alters it, meaning that copying or scanning a quantum banknote destroys the very information that ensures its authenticity. 

The unique property makes quantum money exceptionally secure: unlike blockchain, which relies on cryptographic algorithms and distributed consensus, quantum money derives its protection directly from the laws of physics. In theory, a quantum banknote cannot be counterfeited, even by an attacker with unlimited computing resources, which is why quantum money is considered one of the most promising approaches to unforgeable digital currency.

A new battle for the digital throne is emerging as quantum money shifts from theory to possibility, challenging whether Bitcoin’s decentralised strength can hold its ground in a future shaped by quantum technology.

How quantum money works in theory

Quantum money schemes are typically divided into two main types: private and public. 

In private quantum money systems, a central authority- such as a bank- creates quantum banknotes and remains the only entity capable of verifying them. Each note carries a classical serial number alongside a set of quantum states known solely to the issuer. The primary advantage of this approach is its absolute immunity to counterfeiting, as no one outside the issuing institution can replicate the banknote. However, such systems are fully centralised and rely entirely on the security and infrastructure of the issuing bank, which inherently limits scalability and accessibility.

Public quantum money, by contrast, pursues a more ambitious goal: allowing anyone to verify a quantum banknote without consulting a central authority. Developing this level of decentralisation has proven exceptionally difficult. Numerous proposed schemes have been broken by researchers who have managed to extract information without destroying the quantum states. Despite these challenges, public quantum money remains a major focus of quantum cryptography research, with scientists actively pursuing secure and scalable methods for open verification. 

Beyond theoretical appeal, quantum money faces substantial practical hurdles. Quantum states are inherently fragile and susceptible to decoherence, meaning they can lose their information when interacting with the surrounding environment. 

Maintaining stable quantum states demands highly specialised and costly equipment, including photonic processors, quantum memory modules, and sophisticated quantum error-correction systems. Any error or loss could render a quantum banknote completely worthless, and no reliable method currently exists to store these states over long periods. In essence, the concept of quantum money is groundbreaking, yet real-world implementation requires technological advances that are not yet mature enough for mass adoption. 

A new battle for the digital throne is emerging as quantum money shifts from theory to possibility, challenging whether Bitcoin’s decentralised strength can hold its ground in a future shaped by quantum technology.

Bitcoin solves the duplication problem differently

While quantum money relies on the laws of physics to prevent counterfeiting, Bitcoin tackles the duplication problem through cryptography and distributed consensus. Each transaction is verified across thousands of nodes, and SHA-256 hash functions secure the blockchain against double spending without the need for a central authority. 

Unlike elliptic curve cryptography, which could eventually be vulnerable to large-scale quantum attacks, SHA-256 has proven remarkably resilient; even quantum algorithms such as Grover’s offer only a marginal advantage, reducing the search space from 2256 to 2128– still far beyond any realistic brute-force attempt. 

Bitcoin’s security does not hinge on unbreakable mathematics alone but on a combination of decentralisation, network verification, and robust cryptographic design. Many experts therefore consider Bitcoin effectively quantum-proof, with most of the dramatic threats predicted from quantum computers likely to be impossible in practice. 

Software-based and globally accessible, Bitcoin operates independently of specialised hardware, allowing users to send, receive, and verify value anywhere in the world without the fragility and complexity inherent in quantum systems. Furthermore, the network can evolve to adopt post-quantum cryptographic algorithms, ensuring long-term resilience, making Bitcoin arguably the most battle-hardened digital financial instrument in existence. 

A new battle for the digital throne is emerging as quantum money shifts from theory to possibility, challenging whether Bitcoin’s decentralised strength can hold its ground in a future shaped by quantum technology.

Could quantum money be a threat to Bitcoin?

In reality, quantum money and Bitcoin address entirely different challenges, meaning the former is unlikely to replace the latter. Bitcoin operates as a global, decentralised monetary network with established economic rules and governance, while quantum money represents a technological approach to issuing physically unforgeable tokens. Bitcoin is not designed to be physically unclonable; its strength lies in verifiability, decentralisation, and network-wide trust.

However, SHA-256- the hashing algorithm that underpins Bitcoin mining and block creation- remains highly resistant to quantum threats. Quantum computers achieve only a quadratic speed-up through Grover’s algorithm, which is insufficient to break SHA-256 in practical terms. Bitcoin also retains the ability to adopt post-quantum cryptographic standards as they mature, whereas quantum money is limited by rigid physical constraints that are far harder to update.

Quantum money also remains too fragile, complex, and costly for widespread use. Its realistic applications are limited to state institutions, military networks, or highly secure financial environments rather than everyday payments. Bitcoin, by contrast, already benefits from extensive global infrastructure, strong market adoption, and deep liquidity, making it far more practical for daily transactions and long-term digital value transfer. 

A new battle for the digital throne is emerging as quantum money shifts from theory to possibility, challenging whether Bitcoin’s decentralised strength can hold its ground in a future shaped by quantum technology.

Where quantum money and blockchain could coexist

Although fundamentally different, quantum money and blockchain technologies have the potential to complement one another in meaningful ways. Quantum key distribution could strengthen the security of blockchain networks by protecting communication channels from advanced attacks, while quantum-generated randomness may enhance cryptographic protocols used in decentralised systems. 

Researchers have also explored the idea of using ‘quantum tokens’ to provide an additional privacy layer within specialised blockchain applications. Both technologies ultimately aim to deliver secure and verifiable forms of digital value. Their coexistence may offer the most resilient future framework for digital finance, combining the physics-based protection of quantum money with the decentralisation, transparency, and global reach of blockchain technology. 

A new battle for the digital throne is emerging as quantum money shifts from theory to possibility, challenging whether Bitcoin’s decentralised strength can hold its ground in a future shaped by quantum technology.

Quantum physics meets blockchain for the future of secure currency

Quantum money remains a remarkable concept, originally decades ahead of its time, and now revived by advances in quantum computing and quantum communication. Although it promises theoretically unforgeable digital currency, its fragility, technical complexity, and demanding infrastructure make it impractical for large-scale use. 

Bitcoin, by contrast, stands as the most resilient and widely adopted model of decentralised digital money, supported by a mature global network and robust cryptographic foundations. 

Quantum money and Bitcoin stand as twin engines of a new digital finance era, where quantum physics is reshaping value creation, powering blockchain innovation, and driving next-generation fintech solutions for secure and resilient digital currency. 

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