Netflix’s AI-driven VFX marks industry milestone

Making a significant leap into generative AI, Netflix has incorporated it into the post-production of its original series The Eternaut, which marks the first time the streaming giant has used AI-generated content in a final scene.

The sequence in question, a dramatic depiction of a building collapsing in Buenos Aires, was created using generative AI, allowing for a rapid and cost-effective production process.

Co-CEO Ted Sarandos emphasised that the AI-generated sequence was completed 10 times faster and more affordably than traditional visual effects methods.

He noted that AI enabled the production team to achieve high-quality visual effects that would have been unfeasible within the show’s budget constraints.

However, this development highlights Netflix’s commitment to exploring innovative technologies to enhance its content creation processes.

The company aims to streamline production workflows and expand creative possibilities by integrating generative AI, and the move like this one also raises questions about the implications of AI in the entertainment industry, particularly concerning the potential impact on jobs and the authenticity of creative work.

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Alibaba reveals Quark AI glasses to rival Meta and Xiaomi

Alibaba entered the wearable tech scene at the World Artificial Intelligence Conference in Shanghai by unveiling its first smart glasses, Quark AI Glasses, powered by its proprietary Qwen large language model and the Quark assistant.

The glasses are designed for professional and consumer use and feature hands-free calling, live transcription and translation, music playback, and a built-in camera.

The AR-type eyewear runs on a dual-chip platform, featuring Qualcomm’s Snapdragon AR1 and a dedicated low-power chip. It uses a hybrid operating system setup to balance interactivity and battery life.

Integration with Alibaba’s ecosystem lets users navigate via Amap’s near-eye maps, scan Taobao products for price comparison, make purchases via Alipay, and receive notifications from Ali platforms—all through voice and gesture commands.

Set for release in China by the end of 2025, Quark AI Glasses aim to compete directly with Meta’s Ray-Ban smart eyewear and Xiaomi’s AI glasses.

While product pricing and global availability remain unannounced, Alibaba’s ecosystem depth and hardware‑software integration signal a strategic push into wearable intelligence.

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Democratising clean energy through digital tokens

Tokenisation can remove barriers to green energy investment, allowing more involvement beyond institutional players, says Mete Al, Co-founder of ICB Labs.

Individuals could invest smaller sums and earn passive income by turning assets like solar farms into fractional digital tokens. It allows them to support renewable energy without owning physical infrastructure.

High costs and trust issues limit access to sustainable projects, but blockchain tools can boost confidence and ensure fair rewards.

ICB Labs is already working on a tokenised solar project for 2026. Al emphasises that strong governance and flexible regulation, including regulatory sandboxes, are essential to support innovation in decentralised climate finance.

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AI startup Daydream revolutionises online fashion search

Online shopping for specific items like bridesmaid dresses can be challenging due to overwhelming choices. A new tech startup, Daydream, aims to simplify this. It uses AI to let users search for products by describing them in natural language, making the process easier and more intuitive.

For instance, a user could ask for a ‘revenge dress to wear to a party in Sicily in July,’ or ‘a summer bag to carry to work and cocktails after.’

Daydream, with staff based in New York and San Francisco, represents the latest venture in a growing trend of tech companies utilising AI to streamline and personalise online retail.

Consumer demand for such tools is evident: an Adobe Analytics survey of 5,000 US consumers revealed that 39% had used a generative AI tool for online shopping last year, with 53% planning to do so this year. Daydream faces competition from tech giants already active in this space.

Meta employs AI to facilitate seller listings and to target users with more relevant product advertisements. OpenAI has launched an AI agent capable of shopping across the web for users, and Amazon is trialling a similar feature.

Google has also introduced various AI shopping tools, including automated price tracking, a ‘circle to search’ function for identifying products in photos, and virtual try-on options for clothing.

Despite the formidable competition, Daydream’s CEO, Julie Bornstein, believes her company possesses a deeper understanding of the fashion and retail industries.

Bornstein’s extensive background includes helping build Nordstrom’s website as its vice president of e-commerce in the early 2000s and holding C-suite positions at Sephora and Stitch Fix. In 2018, she co-founded her first AI-powered shopping startup, The Yes, which was sold to Pinterest in 2022.

Bornstein asserts, ‘They don’t have the people, the mindset, the passion to do what needs to be done to make a category like fashion work for AI recommendations.’ She added, ‘Because I’ve been in this space my whole career, I know that having the catalogue with everything and being able to show the right person the right stuff makes shopping easier.’

Daydream has already secured $50 million in its initial funding round, attracting investors such as Google Ventures and model Karlie Kloss, founder of Kode With Klossy. The platform operates as a free, digital personal stylist.

Users can input their desired products using natural language, eliminating the need for complex Boolean search terms, thanks to its AI text recognition technology, or upload an inspiration photo.

Daydream then presents recommendations from over 8,000 brand partners, ranging from budget-friendly Uniqlo to luxury brand Gucci. Users can further refine their search through a chat interface, for example, by requesting more casual or less expensive alternatives.

As users interact more with the platform, it progressively tailors recommendations based on their search history, clicks, and saved items.

When customers are ready to purchase, they are redirected to the respective brand’s website to complete the transaction, with Daydream receiving a 20% commission on the sale.

Unlike many other major e-commerce players, Bornstein is deliberately avoiding ad-based rankings. She aims for products to appear on recommendation pages purely because they are a suitable match for the customer, not due to paid placements.

Bornstein stated, ‘As soon as Amazon started doing paid sponsorships, I’m like, ‘How can I find the real good product?’ She emphasised, ‘We want this to be a thing where we get paid when we show the customer the right thing.’

A recent CNN test of Daydream yielded mixed results. A search for a ‘white, fitted button-up shirt for the office with no pockets’ successfully returned a $145 cotton long-sleeve shirt from Theory that perfectly matched the description.

However, recommendations are not always flawless. A query for a ‘mother of the bride dress for a summer wedding in California’ presented several slinky slip dresses, some in white, alongside more formal styles, appearing more suitable for a bachelorette party.

Bornstein confirmed that the company continuously refined its AI models and gathered user feedback. She noted, ‘We want data on what people are doing so we can focus and learn where we do well and where we don’t.’

Part of this ongoing development involves training the AI to understand nuanced contextual cues, such as the implications of a ‘dress for a trip to Greece in August’ (suggesting hot weather) or an outfit for a ‘black-tie wedding’ (implying formality).

Daydream’s web version launched publicly last month, and it is currently in beta testing, with plans for an app release in the autumn. Bornstein envisions a future where AI extends beyond shopping, assisting with broader fashion needs like pairing new purchases with existing wardrobe items.

She concluded, ‘This was one of my earliest ideas, but I didn’t know the term (generative AI) and I didn’t know a large language model would be the unlock.’

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Delta Air Lines rolls out AI for personalised airfare

Delta Air Lines is shifting the landscape of airfare by leveraging AI to personalise ticket prices. Moving beyond fixed fares, Delta aims to tailor prices closely to each traveller.

Instead of static prices, the system now analyses customer habits, booking history, and even the time of day to predict an individual’s potential willingness to pay. By the end of the current year, Delta aims to set 20% of its ticket prices using AI dynamically.

The goal represents a significant, sevenfold increase from just twelve months prior. Such a high-tech approach could result in more advantageous deals or elevated costs, depending on a passenger’s unique circumstances and shopping behaviour.

It is crucial to understand how this system operates, Delta’s motivations, and its implications for consumer finances. Traditional ticket pricing has long relied on ‘fare buckets,’ where customers are categorised based on their booking method and timing.

Delta’s new AI ticket pricing system fundamentally shifts away from these static rates. It analyses real-time information to calculate precisely what a specific customer will likely spend on a seat for any given flight.

Glen Hauenstein, Delta’s President, describes this as a complete re-engineering of pricing. He characterises AI as a ‘super analyst’ working continuously, 24/7, to identify the optimal price for every traveller, every time.

The airline has collaborated with Fetcherr, which provides the underlying technological infrastructure and supports other global airlines. Airlines do not adopt advanced, high-tech pricing systems to reduce revenue.

Delta reports that initial results from its AI-driven pricing indicate ‘amazingly favourable’ revenues. The airline believes AI will maximise profits by more accurately aligning fares with each passenger’s willingness to pay.

However, this is determined by a vast array of data inputs, ranging from individual booking history to prevailing market trends. Delta’s core strategy is straightforward: to offer a price available for a specific flight, at a particular time, to you, the individual consumer.

Consumers who have previously observed frequent fluctuations in airfare should now anticipate even greater volatility. Delta’s new system could present a different price to one person compared to another for the same seat, with the calculation performed in real-time by the AI.

Passengers might receive special offers or early discounts if the AI identifies a need to fill seats quickly. However, discerning whether one is securing a ‘fair’ deal becomes significantly more challenging. The displayed price is now a function of what the AI believes an individual will pay, rather than a universal rate applicable to all.

The shift has prompted concerns among some privacy advocates. They worry that such personalised pricing could disadvantage customers who lack the resources or time to search extensively for the most favourable deals.

Consequently, those less able to shop around may be charged the highest prices. Delta has been approached for comment, and a spokesperson stated: ‘There is no fare product Delta has ever used, is testing, or plans to use that targets customers with individualised offers based on personal information or otherwise.

Various market forces have driven the dynamic pricing model used in the global industry for decades, with new tech streamlining this process. Delta always complies with regulations around pricing and disclosures.’

Delta’s openness regarding this significant policy change has attracted considerable national attention. Other airlines are already trialling their AI fare systems, and industry experts widely anticipate that the rest of the sector will soon follow suit.

Nevertheless, privacy advocates and several lawmakers are vocalising strong objections. Critics contend that allowing AI to determine pricing behind the scenes is akin to airlines ‘hacking our brains’ to ascertain the maximum price a customer will accept, as described by Consumer Watchdog.

The legal ramifications of this approach are still unfolding. While price variation based on demand or timing is not novel, the use of AI for ultra-personalised pricing raises uncomfortable questions about potential discrimination and fairness, particularly given prior research suggesting that economically disadvantaged customers frequently receive less favourable deals.

Delta’s AI pricing system personalises every airfare, making each search and price specific to the user. Universal ticket prices are fading as AI analyses booking habits and market conditions. This technology can quickly offer special deals to fill seats or raise prices if demand is detected.

Conversely, the price can increase if the system senses a greater willingness to pay. Shopping around is now an absolute necessity. Utilising a VPN can help outsmart the system by masking location and IP address, which prevents airlines from tracking searches and adjusting prices based on geographic region.

Making quick decisions might result in savings, but procrastination could lead to a price increase. Privacy is paramount; the airline gains insights into a user’s habits with every search. A digital footprint directly influences fares. In essence, consumers now possess both increased power and greater responsibility.

Being astute, flexible, and constantly comparing before purchasing is vital. Delta’s transition to AI-driven ticket pricing significantly shifts how consumers purchase flight tickets.

While offering potential for enhanced flexibility and efficiency, it simultaneously raises substantial questions concerning fairness, privacy, and transparency.

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AI chatbot captures veteran workers’ knowledge to support UK care teams

Peterborough City Council has turned the knowledge of veteran therapy practitioner Geraldine Jinks into an AI chatbot to support adult social care workers.

After 35 years of experience, colleagues frequently approached Jinks seeking advice, leading to time pressures despite her willingness to help.

In response, the council developed a digital assistant called Hey Geraldine, built on the My AskAI platform, which mimics her direct and friendly communication style to provide instant support to staff.

Developed in 2023, the chatbot offers practical answers to everyday care-related questions, such as how to support patients with memory issues or discharge planning. Jinks collaborated with the tech team to train the AI, writing all the responses herself to ensure consistency and clarity.

Thanks to its natural tone and humanlike advice, some colleagues even mistook the chatbot for the honest Geraldine.

The council hopes Hey Geraldine will reduce hospital discharge delays and improve patient access to assistive technology. Councillor Shabina Qayyum, who also works as a GP, said the tool empowers staff to help patients regain independence instead of facing unnecessary delays.

The chatbot is seen as preserving valuable institutional knowledge while improving frontline efficiency.

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Crypto hacks hit $3.1 billion by mid-2025

Cyberattacks and scams have already cost the crypto sector more than $3.1 billion in 2025, marking one of the most damaging years. Hacken’s mid-year report reveals that access control failures and social engineering tactics remain the primary culprits.

The most significant single incident occurred in Q1, when Bybit suffered a $1.5 billion breach, accounting for 83% of all Q1 losses. Access control weaknesses were responsible for around $1.83 billion, or 59% of funds lost across both DeFi and CeFi platforms.

Decentralised finance projects were hit particularly hard, with $300 million drained in Q2 alone. Smart contract vulnerabilities contributed to $263 million in losses, including a $223 million hit in the Cetus exploit.

Meanwhile, phishing scams reached new heights, with one incident in April involving a $330 million Bitcoin theft.

Q2 had fewer access breaches than Q1, but single leaks caused rapid, large-scale losses. Hacken’s report concludes that improved cybersecurity is essential for building trust and protecting innovation in the growing blockchain space.

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Z.ai unveils cheaper, advanced AI model GLM-4.5

Chinese AI startup Z.ai, formerly Zhipu, is increasing pressure on global competitors with its latest model, GLM-4.5. The company has adopted an aggressive open-source strategy to attract developers. Anyone can download and use the model without licensing fees or platform restrictions.

GLM-4.5 is designed with agentic AI, breaking tasks into smaller components for improved performance. By approaching problems step by step, the model delivers more accurate and efficient outcomes. Z.ai aims to stand out through both technical sophistication and affordability.

CEO Zhang Peng says the model runs on only eight Nvidia H20 chips, while DeepSeek’s model needs sixteen. Nvidia developed the H20 to comply with US export controls aimed at China. Reducing chip demand significantly lowers the model’s operational footprint.

Zhang said the company has enough computing power and is not seeking further hardware now. Z.ai plans to charge 11 cents per million input tokens, undercutting DeepSeek R1’s 14 cents. Output tokens will cost 28 cents per million, compared to DeepSeek’s 2.19 dollars.

Such pricing could reshape large language model deployment expectations, especially in resource-limited environments. High costs have long been a barrier to broader AI adoption. Z.ai appears to be positioning itself as a more accessible alternative.

Founded in 2019, Z.ai has raised more than 1.5 billion dollars from investors including Alibaba, Tencent, and Qiming Venture Partners. It has grown quickly from a research-focused lab to one of China’s most prominent AI contenders. A public listing in Greater China is reportedly being prepared.

OpenAI recently named Zhipu among the Chinese firms it considers strategically significant in global AI development. US authorities responded by restricting American companies from working with Z.ai. The startup has nonetheless continued to expand its model lineup and partnerships.

Chinese firms increasingly invest in open-source models, often with domestic hardware compatibility in mind. Moonshot, another Alibaba-backed company, released the Kimi K2 model. Kimi K2 has received praise for its performance in coding and mathematical tasks.

Tencent has joined the race with its HunyuanWorld-1.0 model, which is built to generate immersive 3D environments. The HunyuanWorld-1.0 can accelerate game development, virtual reality design, and simulation work. Cutting-edge features are being paired with highly efficient architectures.

Alibaba also introduced its Qwen3-Coder model to assist in code generation and debugging. Such AI tools are seeing increasing use in software engineering and education. Chinese developers are positioning themselves to compete with Western offerings such as OpenAI’s Codex and Anthropic’s Claude.

The momentum within China’s AI sector is accelerating despite geopolitical and trade restrictions. A clear shift is underway from imitation to innovation, with local startups advancing independent research. Many models are trained on China-specific datasets to optimise relevance and performance.

Z.ai’s strategy combines cost reduction, efficient chip use, and broad availability. The company can build community trust and encourage ecosystem growth by open-sourcing its tools. At the same time, pricing undercuts major rivals and could disrupt the market.

Global AI development is increasingly decentralised, with Chinese firms no longer just playing catch-up. Large-scale funding and state support are helping to close gaps in hardware and training infrastructure. Z.ai is one of several firms pushing toward greater technological autonomy.

Open-source AI development is also helping Chinese companies win favour with developers outside their borders. Many international teams are experimenting with Chinese models to diversify risk and reduce reliance on US tech. Z.ai’s GLM-4.5 is among the models gaining traction globally.

By offering a powerful, lightweight, and affordable model, Z.ai is setting a new benchmark in the industry. The combination of technical refinement and strategic pricing draws attention from investors and users. A new era of AI competition is emerging.

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Huawei challenges Nvidia with AI super server

Huawei has unveiled its most powerful AI server, the CloudMatrix 384, to challenge Nvidia’s grip on the high-performance AI infrastructure market.

The system, launched at the World AI Conference in Shanghai, uses 384 Ascend 910C chips, significantly outnumbering Nvidia’s 72 B200 GPUs in the GB200 NVL72.

Although Nvidia’s GPUs remain more powerful individually, Huawei’s design relies on stacking and high-speed chip interconnection to boost overall performance.

The company claims the CloudMatrix 384 can deliver 300 petaflops of computing power, well above Nvidia’s 180 petaflops, though it consumes nearly four times more energy.

The US recently reversed its ban on Nvidia’s H20 chip exports to China, seeking to curb Huawei’s momentum. However, ongoing reports of smuggled Nvidia GPUs raise doubts over the effectiveness of these restrictions.

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Guess AI model sparks fashion world debate

A striking new ‘supermodel’ has appeared in the August print edition of Vogue, featuring in a Guess advert for their summer collection. Uniquely, the flawless blonde model is not real, as a small disclaimer reveals she was created using AI.

While Vogue clarifies the AI model’s inclusion was an advertising decision, not editorial, it marks a significant first for the magazine and has ignited widespread controversy.

The development raises serious questions for real models, who have long campaigned for greater diversity, and consumers, particularly young people, are already grappling with unrealistic beauty standards.

Seraphinne Vallora, the company behind the controversial Guess advert, comprises founders Valentina Gonzalez and Andreea Petrescu. They told the BBC that Guess’s co-founder, Paul Marciano, approached them on Instagram to create an AI model for the brand’s summer campaign.

Valentina Gonzalez explained, ‘We created 10 draft models for him and he selected one brunette woman and one blonde that we developed further.’ Petrescu described AI image generation as a complex process, with their five employees taking up to a month to create a finished product, charging clients like Guess up to the low six figures.

However, plus-size model Felicity Hayward, with over a decade in the industry, criticised the use of AI models, stating it ‘feels lazy and cheap’ and worried it could ‘undermine years of work towards more diversity in the industry.’

Hayward believes the fashion industry, which saw strides in inclusivity in the 2010s, has regressed, leading to fewer bookings for diverse models. She warned, ‘The use of AI models is another kick in the teeth that will disproportionately affect plus-size models.’

Gonzalez and Petrescu insist they do not reinforce narrow beauty standards, with Petrescu claiming, ‘We don’t create unattainable looks – the AI model for Guess looks quite realistic.’ They contended, ‘Ultimately, all adverts are created to look perfect and usually have supermodels in, so what we do is no different.’

While admitting their company’s Instagram shows a lack of diversity, Gonzalez explained to the BBC that attempts to post AI images of women with different skin tones did not gain traction, stating, ‘people do not respond to them – we don’t get any traction or likes.’

They also noted that the technology is not yet advanced enough to create plus-size AI women. However, this mirrors a 2024 Dove campaign that highlighted AI bias by showing image generators consistently producing thin, white, blonde women when asked for ‘the most beautiful woman in the world.’

Vanessa Longley, CEO of eating disorder charity Beat, found the advert ‘worrying,’ telling the BBC, ‘If people are exposed to images of unrealistic bodies, it can affect their thoughts about their own body, and poor body image increases the risk of developing an eating disorder.’

The lack of transparent labelling for AI-generated content in the UK is also a concern, despite Guess having a small disclaimer. Sinead Bovell, a former model and now tech entrepreneur, told the BBC that not clearly labelling AI content is ‘exceptionally problematic’ due to ‘AI is already influencing beauty standards.’

Sara Ziff, a former model and founder of Model Alliance, views Guess’s campaign as “less about innovation and more about desperation and need to cut costs,’ advocating for ‘meaningful protections for workers’ in the industry.

Seraphinne Vallora, however, denies replacing models, with Petrescu explaining, ‘We’re offering companies another choice in how they market a product.’

Despite their website claiming cost-efficiency by ‘eliminating the need for expensive set-ups… hiring models,’ they involve real models and photographers in their AI creation process. Vogue’s decision to run the advert has drawn criticism on social media, with Bovell noting the magazine’s influential position, which means they are ‘in some way ruling it as acceptable.’

Looking ahead, Bovell predicts more AI-generated models but not their total dominance, foreseeing a future where individuals might create personal AI avatars to try on clothes and a potential ‘society opting out’ if AI models become too unattainable.

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