Nvidia has introduced Kimodo, a new artificial intelligence model designed to generate high-quality 3D motion for humans and robots using text prompts and kinematic constraints. The system represents a step forward in motion synthesis, an area increasingly important for robotics, simulation, and digital content creation.

The model, trained on approximately 700 hours of optical motion capture data, reflects a broader push to scale training datasets in order to improve realism and control. Publicly available motion capture datasets have historically been limited in size, constraining the performance of earlier generative models.

Kimodo builds on this by enabling motion generation directly from natural language descriptions. Users can input prompts to create animations of human movement, reducing the need for manual animation or motion capture sessions. The system can also interpret how robotic structures move, including platforms such as the Unitree G1 humanoid robot, allowing developers to generate motion instructions for machines without relying on human operators.

Flexible Control Through Text and Constraints

In addition to text prompts, Kimodo supports a wide range of kinematic constraints. These include full-body keyframes, joint-level positioning and rotation, as well as two-dimensional waypoints and motion paths.

This flexibility allows developers to guide motion generation at different levels of detail, from general behavioral descriptions to precise physical positioning. The model’s architecture incorporates a two-stage denoising process, separating root motion from body movement, which helps reduce artifacts and improve consistency.

The system’s motion representation is designed to handle diverse input types, enabling it to adapt across use cases in both digital and physical environments. Nvidia said its experiments show that scaling both dataset size and model complexity leads to measurable improvements in motion quality and control accuracy.

Applications Across Robotics and Media

High-quality motion generation has applications across robotics, gaming, film production, and simulation. In robotics, it can accelerate training and deployment by providing synthetic motion data and control instructions. In media, it can streamline animation workflows and reduce production costs.

Kimodo’s ability to generate both human-like motion and robot-specific movement highlights the convergence between AI-driven simulation and real-world automation. By bridging these domains, the model could support more advanced human-robot interaction and autonomous systems.

Nvidia has made a demo of Kimodo available through a public interface, though access may be limited due to demand. The release underscores the company’s continued investment in applying generative AI to physical systems, extending beyond text and images into movement and control.

Meta is developing an internal artificial intelligence agent designed to assist CEO Mark Zuckerberg in managing information and decision-making, according to a report citing sources familiar with the project.

The system, described as a “CEO agent,” is intended to streamline how Zuckerberg accesses information across the company. Instead of relying on layers of management and internal communication, the agent can retrieve relevant data and provide direct answers, reducing friction in executive workflows.

The tool remains under development, but it reflects Meta’s broader push to embed AI across its operations. The company has increasingly focused on building internal systems that enhance productivity and automate knowledge access at scale.

Internal AI Tools Gain Momentum

Alongside the CEO agent, Meta is reportedly advancing other AI-driven tools, including a system known as “Second Brain.” This platform is designed to index internal documents and allow employees to query project data more efficiently, functioning as a centralized knowledge interface.

Employees are also experimenting with personal AI agents that can access chat histories, work files, and internal systems. These agents are capable of communicating with colleagues or even other AI systems, enabling more automated collaboration across teams.

The growing use of such tools highlights a shift toward agent-based workflows, where AI systems act on behalf of users to retrieve information, coordinate tasks, and execute actions.

Strategic Shift Toward AI

The development of internal AI agents comes as Meta continues to realign its priorities away from its earlier metaverse-focused strategy. The company recently announced it will shut down the VR version of Horizon Worlds and transition the platform to mobile-only, marking a significant pullback from its virtual reality ambitions.

At the same time, the company has accelerated the integration of AI across its operations. In December 2025, Meta acquired Chinese AI startup Manus, whose agent technology is claimed to outperform OpenAI’s DeepResearch in performance. The acquisition has helped strengthen Meta’s capabilities in autonomous systems and agent-based workflows.

This shift underscores Meta’s increasing emphasis on artificial intelligence as a core driver of future growth. The company has been investing heavily in AI infrastructure, models, and applications, positioning itself alongside other major technology firms competing in the space.

By deploying AI tools internally, Meta aims to improve efficiency and decision-making while also testing systems that could later be adapted into commercial products. Executive-level applications, such as the CEO agent, may serve as early prototypes for broader enterprise use cases.

Meta has not publicly confirmed details of the CEO agent project. However, the reported development highlights the company’s continued transition toward AI-centric operations, where software agents play an increasingly central role in both internal workflows and future product offerings.

Nvidia CEO Jensen Huang has proposed a new compensation model for engineers that includes AI “tokens” as part of their pay, reflecting a broader shift toward AI-driven productivity in the workplace.

Speaking at Nvidia’s annual GPU Technology Conference, Huang suggested that engineers could receive token budgets alongside their base salaries. These tokens, which represent units of compute used to run AI models and agents, would allow employees to deploy AI systems to automate tasks and enhance output.

Huang said engineers could earn several hundred thousand dollars in base pay, with an additional allocation of tokens valued at a significant portion of that salary. The tokens would effectively function as a productivity resource, enabling workers to scale their output by leveraging AI tools.

AI Agents Reshape Workflows

The proposal is tied to Huang’s vision of a future workplace where engineers oversee large networks of AI agents capable of executing complex, multi-step tasks. In this model, human workers act as supervisors, directing digital systems that handle coding, analysis, and other functions.

Huang has previously described a future in which Nvidia’s workforce includes far more AI agents than human employees. These systems would rely on software infrastructure, increasing demand for computing resources and development tools.

The concept aligns with a growing trend in the technology sector, where companies are integrating AI agents into everyday workflows. These systems can perform tasks such as writing code, analyzing data, and generating reports with minimal human input.

Industry observers note that this shift is changing how software is developed. Instead of writing code line by line, engineers increasingly describe desired outcomes in natural language, with AI systems generating and executing the underlying logic.

Labor Market Impact and Talent Shift

The rise of AI agents has intensified debate about the future of work. Some analysts warn that automation could displace a significant share of white-collar roles, particularly those involving repetitive or entry-level tasks.

Estimates suggest AI could automate up to a quarter of work hours in the United States, with potential productivity gains of around 15%. At the same time, companies face a “talent paradox,” where demand for AI-skilled workers is rising even as automation reduces the need for certain roles.

Entry-level positions are seen as particularly vulnerable, as AI systems increasingly handle foundational tasks that once served as training grounds for new employees. This could widen skill gaps and complicate workforce development.

Despite these concerns, economists point out that technological shifts historically create new categories of jobs, even as they eliminate others. Emerging roles related to AI management, oversight, and integration are expected to grow.