What Nvidia, Google and Meta Are Building Beyond Chips and Compute

While much of the Big Tech narrative continues to revolve around chips and compute infrastructure, this week highlighted a quieter but equally significant set of moves unfolding in parallel. From industrial artificial intelligence (AI) platforms and AI-native social media experiments to autonomous agents in scientific research, technology companies are pushing AI deeper into real-world workflows.

Industrial AI Moves From Simulation to System of Record

At the industrial end of the spectrum, Nvidia and Dassault Systèmes announced a long-term partnership aimed at building what the companies describe as a shared industrial AI platform.

Revealed at Dassault’s 3DExperience World event, the collaboration combines Nvidia’s accelerated computing stack and AI software with Dassault’s virtual twin technology, which is used to model products, factories and biological systems with physics-based accuracy.

The companies are framing the effort as a step toward “industry world models,” AI systems designed to understand and simulate real-world behavior rather than simply generate outputs from statistical patterns. Nvidia CEO Jensen Huang said the goal is to create AI grounded in scientific truth, where simulations are validated against physical constraints and can be trusted as decision-making systems for complex engineering and manufacturing tasks.

Strategically, the partnership deepens Nvidia’s push to embed its hardware and software into industrial workflows that have traditionally relied on siloed simulation tools. Dassault’s platforms already serve as core systems for aerospace, automotive, energy and life sciences customers, and the integration of Nvidia’s AI models and acceleration libraries positions those platforms as AI-native environments rather than legacy engineering software retrofitted with machine learning.

Dassault’s cloud subsidiary, Outscale, also plans to deploy Nvidia AI infrastructure across multiple regions, reflecting growing demand for sovereign and regionally compliant industrial AI deployments.

Meta Tests AI-Native Video as a Standalone Social Product

While Nvidia is anchoring AI in factories and labs, Meta is testing how far generative AI can go as a consumer media format. According to TechCrunch, Meta is experimenting with a standalone mobile app for Vibes, its AI-generated short-video feed that previously lived inside the Meta AI app.

Vibes allow users to generate short videos using text prompts and browse a feed of AI-created clips that visually resemble TikTok or Instagram Reels. Spinning the product out into its own app suggests Meta is exploring whether AI-generated content can sustain a dedicated social experience, rather than acting as a novelty feature inside a broader assistant or messaging product.

The move aligns with Meta’s broader strategy of positioning AI as a core driver of engagement and creation across its platforms. Executives have increasingly pointed to generative AI as a way to expand the supply of content while lowering the barrier to creation, particularly as competition intensifies for user attention. A standalone Vibes app also allows Meta to test monetization models, including subscriptions or premium creation tools, without disrupting its existing social products.

Google Cloud Pushes AI Agents Deeper Into Scientific Discovery

In enterprise and research, Google Cloud is sharpening its focus on AI agents as a core differentiator, particularly in scientific and life sciences workflows. This week, Google Cloud announced an expanded collaboration with Evogene, integrating advanced AI agents into Evogene’s ChemPass AI platform for small-molecule discovery.

While Evogene provides the domain expertise and proprietary chemistry models, Google Cloud is positioning Vertex AI as the engine that enables autonomous, multistep scientific reasoning at scale. The agents are designed to plan experiments, evaluate molecular candidates and iterate through design-test-analyze loops with minimal human intervention, moving beyond predictive models toward more self-directed discovery systems.

In parallel, Google Research highlighted how AI trained in one domain can unlock insights in another. Researchers showed that a bioacoustics model originally trained on bird sounds can be repurposed to analyze underwater audio, helping surface new patterns in whale and marine ecosystems. The work points to how foundation models are being reused across scientific fields in ways their creators did not initially anticipate.