Alex Chi – Chief Information Digital Officer (S P Setia) In 2025, AI will transform into ‘everyday AI’ — affordable, utility-based, and accessible to all. From simplifying daily tasks to boosting personal productivity, it will empower individuals to level up their skills and unlock new opportunities. For businesses, this means reimagining their offerings to keep pace, as ‘good enough’ won’t cut it anymore. The challenge will be staying competitive in a world where AI amplifies expectations, requiring a growth mindset and clarity on the ultimate goal. As AI becomes a natural extension of our lives, those who embrace it with purpose will thrive. Alex Tan – Group Chief Information Officer (Yinson) As 2025 unfolds, we foresee a shift in the technology landscape: The generative AI (genAI) frenzy will give way to pragmatic applications, commencing with bespoke in-house chatbots that streamline operations. AI-infused software-as-a-service (SaaS) solutions will become the norm, elevating business efficiency. Meanwhile, the narrowing air gap in industrial control systems (ICS) will propel operational technology (OT) security to the forefront — necessitating robust and proactive measures. Furthermore, the year will mark a significant leap towards the democratisation of IT — with citizen IT and fusion teams playing pivotal roles in bridging the gap between technology and business strategies, fostering a culture of inclusive innovation. Christian Piccardi – Chief Information Officer (Mox Bank) In 2025, Mox will extend the use of AI and automation to enhance customer support, decision-making, and operational efficiency while exploring AI co-piloting to augment tech capabilities. We will also incorporate emerging application ecosystems such as HarmonyOS into our environment to broaden customer coverage and serviceability. Continuing the migration to multi-/hybrid-cloud environments for flexibility and scalability will be another focus area and equally important is further strengthening of cloud security and compliance practices. From a workforce perspective, Mox will increase investment in training and up-skilling measures to support our people in the use of new technologies and digital tools. Chua Yong Howe – Chief Digital Officer (UEM Edgenta) 2025 will be the year of AI agents redefining digital transformation. These autonomous agents — capable of partially or fully taking over human roles — will dominate trends like service-as-software. In green- and smart-building management, AI agents paired with the internet of things (IoT) will handle routine metrics, issue alerts, and autonomously schedule maintenance crews for optimal efficiency. My goal this year is to revolutionise facility management by deploying AI agents to augment maintenance teams, shifting from manual to highly autonomous operations. Additionally, we aim for AI agents to handle over 90% of contact centre tasks, streamlining customer interactions and driving transformative efficiency. Cornelius Budianto – Director, IT (Kompas Gramedia) GenAI will transform customer interactions with more dynamic and personalised experiences. Today, many chatbots leave customers feeling disconnected — especially as companies make human customer service harder to access. By 2025, genAI will bridge this gap by deeply understanding customer needs and delivering tailored solutions like personalised recommendations and targeted marketing. Smarter AI chatbots will offer empathetic and efficient support, while predictive analytics proactively resolves issues. This shift will streamline operations and lower costs but still enhance customer satisfaction and business growth. Daniel Suraboyini – Global Chief Information Officer (SIPEF) Digital transformation goals for SIPEF Group in 2025 will focus on driving innovation and operational efficiency through key projects like Project Horizon and Program Fruit (automation, drones, IoT, and AI initiatives). This year, we will automate all our tanks across our mills for real-time product information with accurate storage and forecasting information. The three-phase project plan of Program Fruit is advancing as we bring in AI for management reporting as well as descriptive and predictive analytics with a goal towards genAI. 2025 will see drones as part of our operational support to enhance the capabilities for large-scale plantation activities from pesticide-spraying to fruit-harvesting. Thanks to our SIPEF management for their continuous support and encouragement in overcoming challenges, ensuring stabilisation, and aligning digital strategies with regional organisational goals. Dr Darron Sun – Head of Information Technology (Hong Kong Housing Society) Digital transformation will be all about smarter, faster, and more connected experiences in 2025. AI and machine learning will dominate, powering hyper-automation and real-time decision-making. Edge computing — boosted by 5G — will make data processing quicker and more efficient, especially for IoT devices. Quantum computing might start showing practical uses, while blockchain will keep reshaping finance and digital ownership. Extended reality will become more mainstream, transforming industries like retail and healthcare. Sustainability will also be huge, with green tech and carbon tracking tools gaining traction. Also, expect digital twins and autonomous systems to revolutionise industries like manufacturing and logistics. Exciting times ahead! Dave Chen – Head of Information Technology (Hong Kong Trade Development Council) AI Integration — the focus will be on the business value AI brings, emphasising its practical applications. AI running at the edge will gain traction, enabling faster responses and supporting a wider range of use cases. Cybersecurity — this remains a top priority, with increased resources to combat phishing through user awareness programs. Data and AI governance will also be a key focus, ensuring the secure and ethical use of information. Together, these advancements in AI and cybersecurity will drive significant digital transformation — creating more efficient, secure, and responsive systems across various industries. Exciting developments ahead! Franky Lam – Associate Director of Information Technology (Hong Kong-Shenzhen Innovation and Technology Park) An increasing number of enterprises are leveraging AI across various domains to enhance operational efficiency, create new business values, and improve decision-making. However, it is crucial for organisations to prioritise AI governance — including regular risk assessments of data involved in AI automation — to mitigate potential side effects. Edge computing in hybrid- and multi-cloud environments will become more prevalent, facilitating data processing closer to the source for quicker insights within a more flexible and scalable cloud infrastructure. Additionally, senior management is increasingly focused on the sustainability of digital transformation initiatives in response to stakeholders and societal demands for corporate responsibility. Furthermore, 5G and IoT technologies play a pivotal role in driving many digital transformation initiatives by enabling

深圳「一簽多行」恢復後的首個春節假期，加上新春盛事節目多，吸引約100萬旅遊訪港，令市面過年氣氛更熱鬧。新鴻基地產(簡稱新地) 十五大商場包括apm、大埔超級城、銅鑼灣wwwtc mall、新太陽廣場、元朗廣場、柴灣新翠商場、上水新都廣場等今年提早部署，於1月初已在大眾點評派發逾萬張優惠券，率先鎖定內地客的消費目標，又推出各式特色賀年主題裝置、賀年市集、年廿九晚會活動，部份商場於年初一起加推消費換賀年福袋，濃厚年味兼高性價比消費成功帶動人流，1月29日至2月2日(5天)過年黃金檔期人流及生意額較去年同期升約10%。 新鴻基地產執行董事馮秀炎小姐表示: 「今年農曆新年零售消費檔期由1月中開始，一簽多行政策除帶來觀光旅客外，亦增加公務訪港客及探親客量，在客流上帶來助力，令團年飯及辦年貨需求在1月中開始反映。零售氣氛愈近過年愈濃厚，本港多處舉行過年盛事，旗下15大商場亦提早推出多重消費優惠及加強賀年藝術裝置的宣傳，積極為商場引流。有旅遊區商場的換禮活動未到過年已近換罄，要在年廿九加碼名額，不少旅客為賺禮遇而愈賺愈買，消費達10萬元。新地商場舉辦多場新春慶祝活動、旅客訪港禮遇、新春激筍推廣活動、大型年宵市集等，成功帶動市民及旅客的節日消費意慾。總結新春黃金檔期(1月29日至2月2日) 新地十五大商場人流及生意額較去年升約10% 。當中衣履服裝受惠節前氣溫急降，剛性需求加上節日優惠大，銷情理想。今年雙春兼潤月、新春後又緊接情人節，令精品金飾珠寶受捧，料生意額按年增近10%。過節必要食得好，除了在傳統中式酒樓食開年飯外，今年特色餐飲尤其是過江龍品牌特別受歡迎，如apm分別有兩個餐飲品牌均趕及過年前開業，新春期間經常長龍，節日餐飲生意按年升至少15%。」 新鴻基地產(簡稱新地) 15大商場包括apm、大埔超級城、銅鑼灣wwwtc mall、新太陽廣場、元朗廣場、柴灣新翠商場、上水新都廣場、置富南區廣場、利港商場、開心廣場、翠怡商場、美景商場、新葵興廣場、形薈及本舍。 LinkedIn Email Facebook Twitter WhatsApp The post 多重優惠迎旅客 新地15大商場新春生意額及人流升10% appeared first on VeriMedia. source

Much like the seismic arrival of the internet, artificial intelligence quickly became the technology most every organization has sought to leverage, and we are still in the early days. The path to realizing those AI expectations, however, comes with a variety of costs that are not all monetary — and could have surprising impacts on the world. This is the opening chapter of an InformationWeek special series of stories and video essays to explore some of the costs that can be incurred in our collective pursuit of AI. As we try to answer the core question, what is the cost of AI, let’s start small. Let’s look at some of the costs organizations may face when they seek to develop AI-enabled apps in-house. This is often a way for enterprises to make their first inroads into leveraging AI for their operations. This video features footage from The AI Summit New York, December 2024 and includes excerpts from the event with speeches and panel discussions that include New York Governor Kathy Hochul; Haley Massa, ML solutions engineer, Snorkel AI; and Romi Mahajan, CEO, Exofusion. The video also includes one-on-one interviews with Rakesh Malhotra, principal for digital and emerging technologies, EY; Jehangir Amjad, head of AI platform, Ikigai Labs; Ritika Gunnar, general manager for data & AI, IBM, and Gianpaolo Barozzi, VP, 3P chief innovation and technology officer, Cisco. Related:The Real Cost of AI: An InformationWeek Special Report source

Kathy Kay is a senior IT executive who enables business success by driving simplicity in a complex world. As executive vice president and CIO, Kay oversees the global technology and digital strategies for Principal Financial Group, where she is helping build the financial investment management and insurance company’s future. On a recent episode of the Tech Whisperers podcast, we explored Kay’s various roles as a builder, from building businesses, teams, and innovative ecosystems to building clarity, trust, and community. Afterwards, we spent more time discussing how a mission-driven commitment to listening, learning, and pivoting to meet customer needs has been key to Principal’s longevity and its ability to remain future-ready. A true example of “drinking your own champagne,” Kay embodies and role models that commitment with her teams, inspiring a customer-centric approach to innovation and transformation. What follows is our conversation, edited for length and clarity. source

By Linda Chiem ( January 28, 2025, 8:30 AM EST) — The Federal Aviation Administration’s former chief counsel Marc Nichols has joined DLA Piper in Washington, D.C., as partner and co-chair of its transportation practice, the firm announced Tuesday…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

By Dorothy Atkins ( January 30, 2025, 10:15 PM EST) — The California attorney general urged the Ninth Circuit on Wednesday to block orders requiring third-party state agencies to respond to Meta Platforms’ discovery demands in multidistrict litigation over social media’s alleged harms, arguing in a mandamus petition the “clearly erroneous” ruling “runs roughshod” over the state’s constitutional divisions of power…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

（圖中）署理財經事務及庫務局局長陳浩濂、（左4）香港黃金交易主席張德熙博士等主要嘉賓舉行新春開市儀式。 香港金銀業貿易場早前公布「香港黃金交易所」已於2024年6月註冊成立，並宣佈香港黃金交易所將於2025年1月1日正式營運，接替金銀業貿易場成為香港的現貨黃金、白銀交易所。香港黃金交易所今日舉行新春開市儀式，主席張德熙博士表示，成立香港黃金交易所是承傳金銀業貿易場的歷史任務，該交易所日後將加強優化企業管治，估計可更容易吸納國際機構參與。 蛇年金市開市，九九金每両開市報價25928元，較休市前高328元；離岸人民幣每公斤金條第一口價報660.08元人民幣。 陳浩濂出席開市儀式 香港黃金交易所舉行新春開市儀式，署理財經事務及庫務局局長陳浩濂表示，香港經濟正處結構調整的過程，環球形勢複雜多變下，預計今年是挑戰和機遇並存的一年，但相信香港的機遇會大於挑戰。他又指，行政長官李家超提出要發掘新增長點和令香港成為國際黃金交易中心，是鞏固和提升國際金融中心地位的新切入點。 陳浩濂補充，香港只要發揮超級聯繫人及超級增值人的角色，其機遇定必大於挑戰。港府一方面會鞏固提升傳統優勢，勇於改革、敢於創新，為金融服務業注入新動能。同時，亦會以創科為新經濟動能，為本港人工智能、數據科技、金融科技等範疇構建蓬勃生態圈。 他又表示，新一屆美國政府政策方向，以至聯儲局可能放慢減息步伐，都為前景帶來不確定性，但亞洲經濟表現相對強勁，市場對中央提出的政策亦反應正面，預期會進一步促進內需，推動內地經濟穩步上升。 而張德熙表示，推動黃金市場發展工作小組在去年底已開首次會議，討論涉及新的黃金倉儲選址。他指成員希望在北部都會區落成，但需要一年半載準備，希望在2027年開始構建，目前會先擴建機場的倉儲。 張德熙提到，地緣政治等因素令金價穩步上揚，料金價在蛇年會挑戰3000美元，形容「高處未算高」，並認為金價回落的機會不大。張德熙預計，在政府推動發展黃金交易中心下，黃金在香港的成交量有望升10%至15%。而香港黃金交易所已全面電子化，會配合港府在惡劣天氣下繼續買賣成交的要求，實行「打風不停市」。 LinkedIn Email Facebook Twitter WhatsApp source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The AI narrative has reached a critical inflection point. The DeepSeek breakthrough — achieving state-of-the-art performance without relying on the most advanced chips — proves what many at NeurIPS in December had already declared: AI’s future isn’t about throwing more compute at problems — it’s about reimagining how these systems work with humans and our environment. As a Stanford-educated computer scientist who’s witnessed both the promise and perils of AI development, I see this moment as even more transformative than the debut of ChatGPT. We’re entering what some call a “reasoning renaissance.” OpenAI’s o1, DeepSeek’s R1, and others are moving past brute-force scaling toward something more intelligent — and doing so with unprecedented efficiency. This shift couldn’t be more timely. During his NeurIPS keynote, former OpenAI chief scientist Ilya Sutskever declared that “pretraining will end” because while compute power grows, we’re constrained by finite internet data. DeepSeek’s breakthrough validates this perspective — the China company’s researchers achieved comparable performance to OpenAI’s o1 at a fraction of the cost, demonstrating that innovation, not just raw computing power, is the path forward. Advanced AI without massive pre-training World models are stepping up to fill this gap. World Labs’ recent $230 million raise to build AI systems that understand reality like humans do parallels DeepSeek’s approach, where their R1 model exhibits “Aha!” moments — stopping to re-evaluate problems just as humans do. These systems, inspired by human cognitive processes, promise to transform everything from environmental modeling to human-AI interaction. We’re seeing early wins: Meta’s recent update to their Ray-Ban smart glasses enables continuous, contextual conversations with AI assistants without wake words, alongside real-time translation. This isn’t just a feature update — it’s a preview of how AI can enhance human capabilities without requiring massive pre-trained models. However, this evolution comes with nuanced challenges. While DeepSeek has dramatically reduced costs through innovative training techniques, this efficiency breakthrough could paradoxically lead to increased overall resource consumption — a phenomenon known as Jevons Paradox, where technological efficiency improvements often result in increased rather than decreased resource use. In AI’s case, cheaper training could mean more models being trained by more organizations, potentially increasing net energy consumption. But DeepSeek’s innovation is different: By demonstrating that state-of-the-art performance is possible without cutting-edge hardware, they’re not just making AI more efficient — they’re fundamentally changing how we approach model development. This shift toward clever architecture over raw computing power could help us escape the Jevons Paradox trap, as the focus moves from “how much compute can we afford?” to “how intelligently can we design our systems?” As UCLA professor Guy Van Den Broeck notes, “The overall cost of language model reasoning is certainly not going down.” The environmental impact of these systems remains substantial, pushing the industry toward more efficient solutions — exactly the kind of innovation DeepSeek represents. Prioritizing efficient architectures This shift demands new approaches. DeepSeek’s success validates the fact that the future isn’t about building bigger models — it’s about building smarter, more efficient ones that work in harmony with human intelligence and environmental constraints. Meta’s chief AI scientist Yann LeCun envisions future systems spending days or weeks thinking through complex problems, much like humans do. DeepSeek’s-R1 model, with its ability to pause and reconsider approaches, represents a step toward this vision. While resource-intensive, this approach could yield breakthroughs in climate change solutions, healthcare innovations and beyond. But as Carnegie Mellon’s Ameet Talwalkar wisely cautions, we must question anyone claiming certainty about where these technologies will lead us. For enterprise leaders, this shift presents a clear path forward. We need to prioritize efficient architecture. One that can: Deploy chains of specialized AI agents rather than single massive models. Invest in systems that optimize for both performance and environmental impact. Build infrastructure that supports iterative, human-in-the-loop development. Here’s what excites me: DeepSeek’s breakthrough proves that we’re moving past the era of “bigger is better” and into something far more interesting. With pretraining hitting its limits and innovative companies finding new ways to achieve more with less, there’s this incredible space opening up for creative solutions. Smart chains of smaller, specialized agents aren’t just more efficient — they’re going to help us solve problems in ways we never imagined. For startups and enterprises willing to think differently, this is our moment to have fun with AI again, to build something that actually makes sense for both people and the planet. Kiara Nirghin is an award-winning Stanford technologist, bestselling author and co-founder of Chima. DataDecisionMakers Welcome to the VentureBeat community! DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation. If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers. You might even consider contributing an article of your own! Read More From DataDecisionMakers source

By Nadia Dreid ( January 29, 2025, 8:08 PM EST) — Now that the Federal Communications Commission is under Republican leadership, one Republican senator wants the new chair to review the agency’s decision to approve Soros Fund Management’s acquisition of an ownership interest in radio station owner Audacy…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The AI world was rocked last week when DeepSeek, a Chinese AI startup, announced its latest language model DeepSeek-R1 that appeared to match the capabilities of leading American AI systems at a fraction of the cost. The announcement triggered a widespread market selloff that wiped nearly $200 billion from Nvidia’s market value and sparked heated debates about the future of AI development. The narrative that quickly emerged suggested that DeepSeek had fundamentally disrupted the economics of building advanced AI systems, supposedly achieving with just $6 million what American companies had spent billions to accomplish. This interpretation sent shockwaves through Silicon Valley, where companies like OpenAI, Anthropic and Google have justified massive investments in computing infrastructure to maintain their technological edge. But amid the market turbulence and breathless headlines, Dario Amodei, co-founder of Anthropic and one of the pioneering researchers behind today’s large language models (LLMs), published a detailed analysis that offers a more nuanced perspective on DeepSeek’s achievements. His blog post cuts through the hysteria to deliver several crucial insights about what DeepSeek actually accomplished and what it means for the future of AI development. Here are the four key insights from Amodei’s analysis that reshape our understanding of DeepSeek’s announcement. 1. The ‘$6 million model’ narrative misses crucial context DeepSeek’s reported development costs need to be viewed through a wider lens, according to Amodei. He directly challenges the popular interpretation: “DeepSeek does not ‘do for $6 million what cost U.S. AI companies billions.’ I can only speak for Anthropic, but Claude 3.5 Sonnet is a mid-sized model that cost a few $10s of millions to train (I won’t give an exact number). Also, 3.5 Sonnet was not trained in any way that involved a larger or more expensive model (contrary to some rumors).” This shocking revelation fundamentally shifts the narrative around DeepSeek’s cost efficiency. When considering that Sonnet was trained 9-12 months ago and still outperforms DeepSeek’s model on many tasks, the achievement appears more in line with the natural progression of AI development costs rather than a revolutionary breakthrough. The timing and context also matter significantly. Following historical trends of cost reduction in AI development — which Amodei estimates at roughly 4X per year — DeepSeek’s cost structure appears to be largely on trend rather than dramatically ahead of the curve. 2. DeepSeek-V3, not R1, was the real technical achievement While markets and media focused intensely on DeepSeek’s R1 model, Amodei points out that the company’s more significant innovation came earlier. “DeepSeek-V3 was actually the real innovation and what should have made people take notice a month ago (we certainly did). As a pretrained model, it appears to come close to the performance of state of the art U.S. models on some important tasks, while costing substantially less to train.” The distinction between V3 and R1 is crucial for understanding DeepSeek’s true technological advancement. V3 represented genuine engineering innovations, particularly in managing the model’s “Key-Value cache” and pushing the boundaries of the mixture of experts (MoE) method. This insight helps explain why the market’s dramatic reaction to R1 may have been misplaced. R1 essentially added reinforcement learning capabilities to V3’s foundation — a step that multiple companies are currently taking with their models. 3. Total corporate investment reveals a different picture Perhaps the most revealing aspect of Amodei’s analysis concerns DeepSeek’s overall investment in AI development. “It’s been reported — we can’t be certain it is true — that DeepSeek actually had 50,000 Hopper generation chips, which I’d guess is within a factor ~2-3X of what the major U.S. AI companies have. Those 50,000 Hopper chips cost on the order of ~$1B. Thus, DeepSeek’s total spend as a company (as distinct from spend to train an individual model) is not vastly different from U.S. AI labs.” This revelation dramatically reframes the narrative around DeepSeek’s resource efficiency. While the company may have achieved impressive results with individual model training, its overall investment in AI development appears to be roughly comparable to its American counterparts. The distinction between model training costs and total corporate investment highlights the ongoing importance of substantial resources in AI development. It suggests that while engineering efficiency can be improved, remaining competitive in AI still requires significant capital investment. 4. The current ‘crossover point’ is temporary Amodei describes the present moment in AI development as unique but fleeting. “We’re therefore at an interesting ‘crossover point’, where it is temporarily the case that several companies can produce good reasoning models,” he wrote. “This will rapidly cease to be true as everyone moves further up the scaling curve on these models.” This observation provides crucial context for understanding the current state of AI competition. The ability of multiple companies to achieve similar results in reasoning capabilities represents a temporary phenomenon rather than a new status quo. The implications are significant for the future of AI development. As companies continue to scale up their models, particularly in the resource-intensive area of reinforcement learning, the field is likely to once again differentiate based on who can invest the most in training and infrastructure. This suggests that while DeepSeek has achieved an impressive milestone, it hasn’t fundamentally altered the long-term economics of advanced AI development. The true cost of building AI: What Amodei’s analysis reveals Amodei’s detailed analysis of DeepSeek’s achievements cuts through weeks of market speculation to expose the actual economics of building advanced AI systems. His blog post systematically dismantles both the panic and enthusiasm that followed DeepSeek’s announcement, showing how the company’s $6 million model training cost fits within the steady march of AI development. Markets and media gravitate toward simple narratives, and the story of a Chinese company dramatically undercutting U.S. AI development costs proved irresistible. Yet Amodei’s breakdown reveals a more complex reality: DeepSeek’s total investment, particularly its reported $1 billion in computing hardware, mirrors the spending of its American counterparts. This moment of cost parity