Meta, Scale AI, and the Dataset Arms Race: What It Means for Video AI and Asia

Team Clairva
Jul 1
3 min read

Updated: Jul 5

Meta's recent announcement of a potential $10 billion investment in Scale AI marks more than just another corporate partnership. It represents a paradigm shift in how the AI ecosystem views training data, especially for large video models (LVMs). As companies race toward building multimodal AI systems capable of understanding, generating, and interacting with video, the conversation is moving from "how big is your model?" to "how good is your data?"

From Compute to Context: Why Meta Chose Scale

Traditionally, Meta has been known for building AI infrastructure in-house. Partnering with Scale AI, a company specializing in large-scale data annotation, suggests that data infrastructure has now moved into the strategic core. Scale AI, already valued at $13.8 billion in 2024, is projected to generate $2 billion in revenue in 2025. This makes data, not just models or chips, the next battlefield for AI competitiveness.

The decision is also timed with the industry's pivot toward large video models. These systems demand not just vast volumes of video data, but nuanced annotations capturing spatial, temporal, emotional, and contextual layers. Scale AI's capabilities in military-grade data annotation reflect this complexity.

Why This Matters for Large Video Models

LVMs are distinct in their needs. Unlike static image or text models, they must understand movement, causality, and human interaction over time. The margin for error is slim, particularly when applied in regulated industries like defense, healthcare, or autonomous systems.

Meta's bet on Scale signals a belief that:

Specialize in temporal video tools, such as fine-grained object tracking or gesture recognition.
Semi-automate annotation pipelines, using AI to assist the annotation process to achieve efficiency.
Focus on ethical practices to gain trust, especially around privacy and consent.
Pursue public-private partnerships to align with national AI agendas and data localization mandates.

The Quiet Rise of Asia in the Data Infrastructure Race

While Scale AI has strong roots in U.S. national security and enterprise ecosystems, Asia is emerging as a critical player in the global data infrastructure map. Consider:

Aspect	Details
Massive data center investments	AWS ($6.2B), Google ($2B), and Oracle ($6.5B) in Malaysia alone.
Policy tailwinds	Sovereign AI initiatives like India's Bhashini and Singapore's SeaLion align with a regional push for local datasets and regulatory compliance.
Regulatory frameworks	Personal Data Protection Act (PDPA) in Singapore and Digital Personal Data Protection (DPDP) Act in India enforce data localization and consent, creating fertile ground for homegrown data providers.

This infrastructure not only fuels economic growth through job creation but also positions Asia as a potential global exporter of structured, culturally contextual, and ethically sourced video data.

Copyright, Consent, and Provenance: The New Compliance Stack

One of the least-discussed, but most critical, components of Meta's investment is what it implies for content legitimacy. The shift toward video data magnifies long-standing issues around:

Issue	Description
Copyright	Video data often incorporates third-party content, sound, and likeness rights.
Consent	Personal identity and facial data make video among the most privacy-sensitive formats.
Provenance	Without traceable origins, datasets risk legal exposure and ethical scrutiny. Enter tools like Google's SynthID, which watermark AI-generated content, images, text, video, with imperceptible signals that allow for downstream verification. These technologies are rapidly becoming indispensable in building datasets that are not only performant but auditable.

A Call to Action for Startups and Policymakers

Meta's Scale AI partnership validates data infrastructure as core IP. For startups in video AI, it suggests several strategies:

Specialize in temporal video tools, such as fine-grained object tracking or gesture recognition.
Semi-automate annotation pipelines, using AI to assist the annotation process to achieve efficiency.
Focus on ethical practices to gain trust, especially around privacy and consent.
Pursue public-private partnerships to align with national AI agendas and data localization mandates.

Conclusion: Control the Data, Shape the Future

Meta's move isn't just about training better models. It's about owning the next layer of differentiation, compliant, high-context, culturally fluent data. As the AI stack becomes increasingly modular, whoever controls the training data layer will shape how AI systems see, hear, and understand the world.

Asia has a unique opportunity here. With its infrastructure investments, diverse cultures, and tightening regulations, the region can become not just a consumer but a creator of world-class AI datasets, especially in video.

The next arms race in AI is not just about algorithms. It's about data, and who gets it right, from the ground up. The Meeker Report shows that AI adoption is accelerating. What it doesn't show but what Meta's decision makes clear - is that who controls the data layer will shape the future AI stack.

This is the dataset race no one is charting yet.