‘Time has come’: Ashwini Vaishnaw says AI must be regulated, Centre to release consultation paper

businesstoday.in Oct 9, 2026

‘Time has come’: Ashwini Vaishnaw says AI must be regulated, Centre to release consultation paper

Curated by Shubham · TechCapsules · Oct 9, 2026

AI-summarised brief · reviewed before publication

Union IT Minister Ashwini Vaishnaw announced that the Indian government will publish a consultation paper on artificial‑intelligence regulation within a month. The draft will centre on AI safety, deep‑fake mitigation, psychological harms, a human‑first approach and skill development to ensure inclusive adoption. Speaking at the launch of the World Development Report, Vaishnaw said the paper will lay the groundwork for future legislation, emphasizing that safety is the paramount concern. He warned that increasingly realistic deepfakes can mislead the public and trigger harmful actions, and urged the tech industry to shoulder most responsibility for cybersecurity, user‑harm and ethical use. The proposal follows earlier calls for labelling AI‑generated content and explores consent‑based frameworks, limits on autonomous “agentic” AI and regulatory sandboxes for high‑risk uses.

💡 Why It Matters

· India is positioning itself as a proactive regulator in a rapidly evolving AI landscape, aiming to curb misinformation and protect users before fragmented rules take hold.

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Ben Affleck is an AI nerd, and the internet is impressed

Ben Affleck, who sold his AI filmmaking startup to Netflix for $587 million, recently showcased his technical expertise on GQ’s “One More Question” and in viral clips. He explained AI concepts—neural networks, transformers, tensors, GPUs—and how they apply to visual effects, detailing his own dataset creation and fine‑tuning of open models for cinematic use. Affleck highlighted his role in bridging AI and filmmaking, emphasizing ethical collaboration with artists and the development of proprietary tools.

💡 Affleck’s public AI literacy signals a shift where Hollywood leaders actively shape technology, potentially redefining creative workflows and industry standards.

therobotreport.com · Oct 9

Helm.ai reaches $70M in signed commercial contracts for its foundation models

Helm.ai announced it has secured $70 million in commercial contracts for its foundation models over the past year, partnering with global automotive OEMs, Tier 1 suppliers, and industrial automation firms. The Redwood City‑based startup, founded in 2016, focuses on autonomous driving, perception, and robotics, using an unsupervised “deep teaching” approach that separates perception from action. The deals signal real‑world deployment and move the company toward break‑even, underscoring its data‑efficient, environment‑agnostic technology.

💡 The contracts validate Helm.ai’s claim that its perception stack can generalize across diverse sectors, from vehicles to mining, reducing the data burden that has stalled many AI‑driven automation projects. This milestone demonstrates a scalable path to commercial viability in physical AI.

techcrunch.com · Oct 9

Fired OpenAI safety researchers dispute misconduct claims, warn of chilling effect

OpenAI dismissed three safety researchers—Jasmine Wang, Tomek Korbak, and Mikita Balesni—after an investigation found they allegedly shared confidential data with an external AI safety group. The researchers released an open letter denying the misconduct claims, arguing their firings signal a chilling effect on the company’s culture of open safety dialogue. They contend that their collaboration with outside experts was essential for addressing AI risks and that the abrupt terminations undermine transparency and accountability within OpenAI.

💡 The firings raise concerns about how AI firms balance proprietary secrecy with external safety oversight, potentially stifling critical independent scrutiny of emerging technologies.

linkedin.com · Oct 9

We’re supercharging Copilot on Windows with Hybrid Intelligence. With your permission, Copilot…

Microsoft announced a new hybrid intelligence feature for Windows Copilot that allows the assistant to access local PC context, perform actions, and use on‑device models when appropriate. The update aims to reduce token usage and improve privacy by requiring explicit user permission before accessing local data. It integrates local NPUs, GPUs, and cloud models to create a distributed AI system, enhancing capability while keeping sensitive files off the cloud. The feature is positioned as a cost‑effective, CFO‑friendly AI solution.

💡 By combining local and cloud intelligence, Microsoft lowers operational costs and protects user data, addressing growing concerns over data sovereignty and AI expense. This shift could set a new standard for enterprise AI deployments.

diginomica.com · Oct 9

Demographics do not matter to AI adoption – one size does not…

Cognizant surveyed over 10,000 employees and identified seven distinct AI user archetypes, revealing that demographic factors such as age, seniority, or industry do not predict AI adoption. The report, New Frontiers, New Frictions, shows that 80% of workers feel a gap between their desired AI use and workplace support. Disposition—attitudes toward technology, risk, and work—is about six times more predictive of AI behavior than job level. The study urges companies to conduct UX research and embed AI into everyday tools rather than relying on mandatory training.

💡 By pinpointing personality traits as the true drivers of AI uptake, the findings challenge conventional rollout strategies and highlight the need for tailored, experience‑focused adoption plans.

quantumcomputingreport.com · Oct 9

U.S. Department of Energy Allocates $159 Million in Phase II Genesis Mission…

The U.S. Department of Energy’s Office of Science has awarded 12 Phase II projects with $159 million under the Genesis Mission, bringing the total to 297 funded initiatives. Two projects focus on quantum hardware: Harvard’s ASQC uses AI to automate quantum error‑correction code selection and co‑design with physical processors, while Oak Ridge’s AI‑Empowered Design of Functional Quantum Magnets creates a physics‑informed AI pipeline to engineer magnetic materials for quantum sensors and QPU platforms. Fermilab’s AXESS project, in partnership with AMD, IBM, SLAC and academia, applies AI to shrink custom chip design cycles from months to minutes for radiation‑hard, sub‑Kelvin environments, and its QCVV effort uses closed‑loop AI agents to streamline quantum state measurements. Supporting software infrastructure is provided by LBNL’s MOAT‑Core, which expands the Osprey AI assistant across 16 institutions to automate accelerator and lithography operations. The awards align with the White House’s Genesis Mission goals for AI‑driven quantum algorithm discovery and system‑level quantum control.

💡 Accelerating AI‑guided hardware design shortens the path to fault‑tolerant quantum computers, turning theoretical error‑correction schemes into deployable systems.