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AI Product manager
A seasoned AI professional driving business growth through strategic AI adoption, with a focus on developing and managing AI products, infrastructure, and governance frameworks that balance innovation with regulatory compliance. They prioritize staying updated on the latest AI models, capabilities, and MLOps advancements.
Ai strategy (20%)Models & Capabilities (20%)Ai Infrastructure & MLOps (20%)Generative AI (20%)User Experience Design (20%)
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AI governance, generative models, infrastructure...
Jeudi 18 décembre 2025 à 10:51
AI Strategy & Governance
Vendor‑driven account shutdowns expose governance gaps
Computerworld details a Swiss cybersecurity firm’s ordeal when Anthropic’s automated system abruptly cancelled its account, erasing 80 % of projects. The episode underscores the risk of opaque AI‑as‑a‑service contracts and the need for contractual safeguards, a point echoed by Greyhound Research analyst Sanchit Vir Gogia, who warns that 47 % of CIOs lack a response plan for such provider‑initiated suspensions.
Computer World
Amazon reshuffles its AGI leadership to stay competitive
The Verge reports that Amazon is appointing Peter DeSantis, an AWS veteran, to head a new division focused on advanced AI models, custom chips, and quantum computing, as Rohit Prasad prepares to depart. The move signals Amazon’s intent to tighten strategic control over its AI roadmap and to counter rival cloud providers in the race for foundational models.
The Verge
Generative AI & Creative Impact
LLMs as catalysts for corporate creativity
Harvard Business Review argues that when deployed correctly, large language models can unlock novel ideas that drive growth, shifting the conversation from pure productivity gains to creative augmentation. The analysis aligns with industry observations that generative AI is becoming a strategic differentiator rather than a mere cost‑saving tool.
Harvard Business Review
AI‑generated deepfakes stir geopolitical alarm
France 24 highlights a viral AI‑fabricated video falsely depicting a coup in France, which provoked diplomatic tension and exposed the difficulty of moderating synthetic media. The incident illustrates the escalating security and trust challenges posed by generative AI in the public sphere.
France24
Models & Capabilities
OpenAI’s hunt for codebases fuels model training pipelines
The Information reveals that OpenAI is acquiring the source code of failed AI startups to enrich its training data, a strategy aimed at accelerating the development of more capable conversational agents and code‑generation tools. This data‑centric approach reflects a broader industry trend of leveraging proprietary code assets to sharpen model performance.
The Information
Apple’s SHARP model generates 3D scenes from a single image in under a second
Wccftech reports that Apple’s new SHARP AI model can synthesize photorealistic 3D views from a lone 2D picture at lightning speed, showcasing a leap in multimodal generation that could reshape content creation pipelines for designers and developers alike. The breakthrough emphasizes the convergence of generative vision and efficient inference.
Wccftech
User Experience Design
Wearables pivot to AI‑driven interaction paradigms
The Verge notes that 2025 marks a turning point for wearables, with smart glasses and AI‑enhanced pendants shifting from health‑tracking to on‑body AI assistants, delivering contextual insights and conversational interfaces directly on the user’s body. This evolution highlights the growing importance of seamless AI integration in everyday hardware design.
The Verge
AI Infrastructure & MLOps
Data trust emerges as a decisive factor for AI ROI
Tech Radar warns that “AI blindness” – the lack of transparent, real‑time data pipelines – is eroding business confidence, urging firms to invest in trustworthy data foundations, rigorous monitoring, and auditable lineage to unlock sustainable AI value. The piece reinforces the MLOps mantra that data quality is as critical as model sophistication.
Tech Radar
Engineering rigor overtakes hype in enterprise AI deployments
SD Times argues that the next phase of AI adoption hinges on solid engineering practices, from continuous integration of models to robust governance frameworks, echoing findings from MIT and McKinsey that many pilots stall without production‑grade pipelines. The article calls for a shift from demo‑centric mindsets to durable, scalable architectures.
SD Times
AI’s soaring water and electricity consumption drives sustainability concerns
The Verge quantifies AI’s environmental footprint, estimating that AI systems this year consumed as much water as the global population’s bottled‑water usage and emitted carbon comparable to a major city. The analysis pushes enterprises to factor energy efficiency and carbon accounting into AI infrastructure planning.
The Verge
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