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AI in ASIA
Monday, 9 March 2026

3Before9

3 must-know AI stories before your 9am coffee

Who should pay attention

AI Ethics Researchers | Government Contractors | AI Developers | Regulators

What changes next

Debate is likely to intensify regarding AI governance and military applications.

1

OpenAI's Head of Robotics Quits Over the Pentagon Deal

Caitlin Kalinowski, who led hardware and robotics at OpenAI, announced her resignation on Saturday, directly citing the company's agreement to deploy its models on the Pentagon's classified cloud networks. In a post on X she said the decision to proceed without clearly defined guardrails around domestic surveillance and lethal autonomy was rushed. "These are too important for deals or announcements to be rushed," she wrote. Kalinowski joined OpenAI in late 2024 from Meta, where she had led development of the company's AR glasses. OpenAI confirmed the departure and reiterated its red lines: no domestic surveillance, no autonomous weapons. CEO Sam Altman had already acknowledged the deal announcement looked sloppy. The resignation is a visible crack in internal consensus at a company that moved fast to fill the gap left by Anthropic's Pentagon standoff.

Why it matters for Asia

Talent departures over governance concerns carry a different weight than external criticism. They signal internal disagreement that cannot be easily managed by a press statement. For enterprise AI buyers in Asia, particularly in regulated industries or government-adjacent sectors, this episode underscores that governance is not just a procurement checkbox. It is becoming a live operational risk that can destabilise the vendor you depend on.

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2

Anthropic Published the Most Rigorous Map Yet of AI's Real Impact on Jobs

A new Anthropic research paper released last week introduces a metric called "observed exposure" - the gap between what AI can theoretically do to a job and what it is actually doing today. Analysing around one million real Claude conversations against 800 occupations, researchers found that while large language models could theoretically handle 94% of tasks in computer and maths roles, actual observed coverage sits at around 33%. Unemployment rates for the most AI-exposed workers have not risen significantly since ChatGPT launched in 2022. The one clear early signal: job-finding rates for workers aged 22 to 25 entering high-exposure fields have dropped by approximately 14% compared to pre-AI baselines, suggesting companies are using AI to absorb entry-level workloads rather than hire.

Why it matters for Asia

This is the most grounded AI employment research published to date, built from real usage data rather than theoretical capability estimates. For employers across Southeast Asia navigating workforce planning, skills investment, and graduate hiring, the "hiring freeze over mass layoffs" pattern is a more actionable frame than the headline panic. The junior talent pipeline is where the pressure is landing first.

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3

The World's Most Anticipated AI Model Is Now Weeks Overdue

DeepSeek V4, the Hangzhou startup's first major release since R1 launched in January 2025, has missed three consecutive release windows: mid-February, Lunar New Year, and the opening of China's National People's Congress in early March. Multiple credible sources including the Financial Times and Reuters confirmed the model exists and is in final testing. It is expected to be a trillion-parameter multimodal model with a 1 million token context window, natively processing text, images, and video. It was reportedly optimised for Huawei Ascend chips rather than Nvidia hardware, in a deliberate break from industry norms. Community consensus on developer forums now puts the release in the second week of March. DeepSeek has confirmed nothing.

Why it matters for Asia

The longer the delay, the more significant the release is likely to be. When R1 dropped, it rattled markets and triggered a global reassessment of Chinese AI capability. V4, if it matches the leaked benchmarks, would arrive as an open-weight multimodal model at roughly one-twentieth the cost per token of GPT-5, running on domestic Chinese hardware. For Asian enterprises evaluating AI infrastructure costs and supply chain exposure to US export policy, V4's architecture choices matter as much as its capabilities.

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