Artificial intelligence is moving beyond text-based interactions toward understanding and simulating the physical world. World models, an emerging category of AI technology, create realistic three-dimensional environments that allow machines to predict movement, understand physical relationships, and practice tasks before operating in real-world conditions, tells Wired.com (full article available to subscribers).

Unlike large language models, which predict sequences of words, world models use visual information to anticipate changes in their surroundings. These systems incorporate physics, cause and effect, and interactions between objects, people, and environments, making them valuable for robotics and autonomous transportation.

Niantic Spatial, which emerged from the company behind Pokémon Go, is developing world models using extensive geospatial data collected through augmented reality applications. Its technology could enable robots to train within digital replicas of actual environments, potentially completing millions of simulated exercises without physical movement.

Autonomous vehicle developer Wayve is applying similar technology to driving. Using dashcam footage, its generative world models recreate hazardous road conditions and unpredictable traffic situations that would be difficult or dangerous to reproduce during physical testing. This approach helps autonomous systems improve their responses to unfamiliar scenarios.

World models also have applications in video game development, where realistic physics and interactive environments are essential. Companies including Tencent and Google are investing in the technology, while researchers such as Yann LeCun and Fei-Fei Li have attracted substantial attention to the field.

Developing these systems requires enormous quantities of visual training data. Companies increasingly employ individuals to record everyday activities, including household chores, providing examples of human movement and object manipulation.

As world models advance, they could become fundamental to domestic robotics, autonomous transportation, and interactive digital environments. Their broader significance lies in enabling AI systems to understand physical consequences rather than simply recognize patterns, bringing machines closer to operating independently in complex environments.

Kenyan startup Fastagger is making artificial intelligence accessible to small businesses by developing AI systems that work on inexpensive smartphones and unreliable internet connections. Its approach addresses technological limitations that often prevent businesses in developing economies from adopting advanced digital tools, tells IEEE Spectrum.

Founded by Mutembei Kariuki, Jude Mwenda, and Stephanie Njerenga, Fastagger focuses on practical AI applications tailored to African markets. Its flagship product, Auni, launched in 2024 and was integrated into M-Pesa, Kenya’s leading mobile money platform. Approximately 6,000 businesses have signed up to use the AI-powered business intelligence assistant.

Auni analyzes mobile payment transactions to identify customer behavior, sales patterns, and marketing opportunities. For example, Nairobi salon owner Peter Chege uses the application to identify peak business hours, recognize loyal customers, and improve staffing decisions. Restaurants can similarly identify frequent customers and offer personalized discounts without manually reviewing transaction records.

A key technical innovation involves adapting optical character recognition (OCR) technology to extract information from poorly formatted financial statements. Fastagger combines OCR with small neural networks to organize transaction data into structured formats. Several functions, including document processing, data analysis, and visualization, operate offline on low-cost devices. More advanced analysis and conversational features still require internet connectivity.

The startup is also expanding beyond retail businesses. Its workforce management solution uses SMS-based AI agents to monitor security guards working in remote locations, detect unusual activity, and generate operational alerts.

Despite receiving support from Google, Microsoft, and Nvidia accelerator programs, Fastagger operates with just three employees. The company is developing additional AI agents capable of automatically executing marketing activities.

Fastagger demonstrates the potential of designing AI around local infrastructure and business requirements rather than adapting technologies developed for wealthier markets. However, experts caution against creating a technological divide where developing economies receive only smaller, less capable AI systems while advanced models remain concentrated in richer regions.