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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 Strategy, Generative Outlook, Model Innovation…
Mardi 9 décembre 2025 à 11:58
AI Strategy and Governance
Counterintuitive AI tackles the “Twin Traps” of modern LLMs
Counterintuitive AI argues that today’s large language models suffer from floating‑point nondeterminism and memory‑less Markovian reasoning, which it labels the “Twin Traps.” Founder Gerard Rego says the firm is building a deterministic “artificial reasoning unit” (ARU) and a full reasoning stack that measures energy per decision, audits logic steps, and keeps humans in the loop. The approach promises reproducible outputs and lower compute waste, a direct challenge to the scaling‑only paradigm.
SD Times
IBM’s $11 billion purchase of Confluent reshapes enterprise AI data pipelines
IBM announced an $11 bn cash acquisition of Confluent, the Kafka‑based real‑time streaming platform, to cement its AI ambitions. Chief Executive Arvind Krishna says the deal will let IBM “glue together” the data sprawl that underpins next‑gen AI workloads, accelerating deployment of generative and agentic models across its cloud and software portfolio. The move underscores a strategic shift toward owning the data‑infrastructure layer essential for trustworthy AI.
Financial Times
The Register
The Information
New academic guidelines aim to curb AI misuse in scholarly publishing
More than 100 researchers convened in Beijing to unveil the Guideline on the Boundaries of AI‑Generated Content Usage in Academic Publishing 3.0. The framework permits AI‑assisted literature review and summarisation but mandates manual verification of citations and transparent disclosure of AI involvement. Senior Academy of Sciences member Tan Tieniu stressed that responsible AI use must be codified to preserve research integrity, signaling a governance push that could become a global standard.
China Daily
CoreWeave raises $2 billion in convertible debt to fuel AI infrastructure growth
AI‑focused cloud provider CoreWeave announced a $2 bn convertible‑debt offering, with an optional $300 m greenshoe, to expand its GPU‑intensive compute capacity. The financing comes as the firm seeks to meet soaring demand for generative‑AI training while navigating tighter capital markets. Analysts note the raise highlights the capital intensity of modern AI infrastructure and the need for innovative financing structures.
CoinDesk
Generative AI Outlook
MIT Technology Review’s “State of AI” predicts a 2030 landscape shaped by generative models
In the final edition of the joint Financial Times–MIT Technology Review series, senior editor Will Douglas Heaven and FT correspondent Tim Bradshaw debate whether generative AI will eclipse the Industrial Revolution in impact. While acknowledging a slowdown in breakthrough model performance, they argue that new applications—world models, reinforcement learning, and “agentic” pipelines—will keep the sector vibrant, with market dynamics favoring firms that can monetize reliable, low‑cost deployments.
MIT Technology Review
Model Innovation and Capability Trends
Nvidia’s next‑gen GPU stack may dissolve the CUDA moat, boosting memory‑efficient AI models
Nvidia unveiled a major CUDA update that simplifies kernel porting, a move championed by chip architect Jim Keller as a potential end to the “CUDA moat.” The enhancement is timed with industry demand for models that retain more context while using less VRAM, a trend highlighted by Citi analysts who expect Nvidia’s new offering to accelerate adoption of memory‑rich generative architectures.
Market Watch
Wccftech
NeurIPS researchers call for a paradigm shift toward continual learning in AI
At the recent Neural Information Processing Systems conference, a cohort of researchers from OpenAI, Google, and academia warned that current static‑training pipelines will not sustain breakthroughs in domains like biology or medicine. They advocate for AI systems that can acquire new capabilities incrementally—mirroring human lifelong learning—to overcome the plateau of diminishing returns from larger models.
The Information
User Experience and Product Adoption
Enterprise AI pilots stumble because product design, not model quality, is the bottleneck
A Mind the Product analysis finds that despite $30‑40 bn poured into AI projects, only 5 % have become core workflow components. The report attributes failure to poor user‑experience design: pilots lack integration into existing processes, trust mechanisms, and clear ROI signals. Successful scaling, it argues, hinges on embedding AI tools into daily routines and delivering measurable productivity gains.
Mind the Product
AI Infrastructure and MLOps Advances
Unconventional AI proposes brain‑inspired chips to slash AI power consumption
Startup Unconventional AI, founded by former Intel and Databricks exec Naveen Rao, is developing neuromorphic processors that emulate the brain’s energy efficiency. The company claims its design could dramatically reduce the electricity footprint of large‑scale model training, addressing a key sustainability hurdle as datacenter demand outpaces supply.
The Register
AI‑enhanced DevOps transforms CI/CD pipelines into autonomous workflows
DevOps.com reports that AI and machine‑learning are being woven into continuous‑integration/continuous‑delivery (CI/CD) pipelines, enabling self‑healing builds, predictive security testing, and automated code reviews. Organizations that adopt these “intelligent pipelines” report faster release cycles and reduced human error, marking a shift toward fully autonomous software delivery ecosystems.
DevOps.com
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