From plush figures to digital pets, a new class of AI toys is emerging — built not around screens or sensors, but around memory, language and emotional awareness
Updated
March 17, 2026 1:02 AM
Spielwarenmesse toy fair. PHOTO: SPIELWARENMESSE
Spielwarenmesse in Nuremberg is the global meeting point for the toy industry, where brands and designers preview what will shape how children play and learn next. At this year’s fair, one message stood out clearly: toys are no longer built just to entertain, but to listen, respond and grow with children. Tuya Smart, a global AI cloud platform company, used the event to show how AI-powered toys are turning familiar formats into interactive companions that can talk, react emotionally and adapt over time.
The company’s central argument was simple but far-reaching. The next generation of artificial intelligence toys will not be defined by motors, sensors or screens alone, but by how well they understand human behavior. Instead of being single-function objects, smart toys for children are becoming systems that combine language models, emotion recognition and memory to support ongoing interaction.
One of the most talked-about examples was Tuya Smart’s Nebula Plush AI Toy. At first glance, it looks like a soft, expressive plush figure. Inside, it uses emotional recognition to change its LED facial expressions in real time. If a child sounds sad or excited, the toy’s eyes respond visually. It supports natural conversation, reacts to hugs and touch and combines storytelling, news-style updates and interactive games. Over time, it builds memory, allowing it to behave less like a gadget and more like an interactive AI toy that recalls past interactions.
Another example was Walulu, also developed using Tuya’s AI toy platform. Walulu is an AI pet built around personalization. It can detect up to 19 emotional states and speak more than 60 languages. It connects to major large language models such as ChatGPT, Gemini, DeepSeek, Qwen and Doubao. Through simple app-based controls, users choose traits like cheerful, quiet, curious or thoughtful. Those choices shape how Walulu talks and reacts. Instead of repeating scripts, it adjusts its tone and behavior over time. The result is not a novelty item, but an emotionally responsive AI toy that feels consistent in daily use.
Tuya also showed how educational AI toys can extend into learning and exploration. Its AI Learning Camera blends computer vision with interactive content. When it recognizes an object, it links it to cultural and learning material. If a child points it at a foreign word, it offers real-time pronunciation and translation. It can also turn drawings into digital artwork, encouraging active creativity rather than passive screen time. In this sense, AI toys for kids are becoming tools for learning as much as play.
These products point to a larger strategy. Tuya is not just making toys — it is building the AI toy development platform behind them. Through its AI Toy Solution, developers can design a toy’s personality, memory logic and behavior without training models from scratch. The system integrates with leading AI models and supports multi-turn conversation and emotional feedback, turning standard hardware into responsive AI companions.
The platform supports multiple development paths. Brands can use ready-to-market OEM solutions, add AI to existing products or build custom toys around their own characters. Plush toys, robots, educational tools and wearables can all become AI-powered toys without changing their physical design.
Because these products are made for children and families, safety is built in. Tuya’s system includes parental controls, conversation history review and content management. It supports standards such as GDPR and CCPA with encryption and data localization.
From a business standpoint, Tuya’s pitch is speed and scale. The company says its AI toy infrastructure can cut development time by more than half and reduce R&D costs by up to 50 percent. Its AIoT network spans over 200 countries and supports more than 60 languages, making global deployment of AI toys easier.
What emerged at Spielwarenmesse 2026 was not just a lineup of smart gadgets, but a clear shift in the category. AI toys are evolving into emotionally aware systems that talk, listen, remember and adapt. Their value lies not in sounding clever, but in fitting naturally into everyday life.
The fair did not present AI toys as a distant future. It showed them as products already entering the mainstream. The real question now is not whether toys will use AI, but how carefully that intelligence is designed for children.
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AI growth is increasingly becoming a manufacturing, packaging and deployment challenge — not just a computing one.
Updated
May 26, 2026 5:28 PM
Taipei 101 and Taipei Nan Shan Plaza, viewed from Elephant Mountain. PHOTO: UNSPLASH
As AI companies continue scaling larger models and data centers, the pressure is no longer falling only on chip design. Manufacturing capacity, advanced packaging and infrastructure deployment are becoming equally important parts of the AI race. AMD’s latest investment announcement reflects how quickly that shift is accelerating.
The US chipmaker announced plans to invest more than US$10 billion across Taiwan’s semiconductor and manufacturing ecosystem to support next-generation AI infrastructure. The investment focuses on expanding partnerships and increasing advanced packaging capacity needed for future AI systems.
The announcement highlights a growing reality across the AI industry. Building powerful AI chips is no longer enough on its own. Companies now also need the manufacturing networks, packaging technologies and supply chain coordination required to deploy AI infrastructure at global scale.
AMD’s investments center heavily around advanced chip packaging, an area becoming increasingly critical as AI systems demand higher performance and greater power efficiency. Traditional chip architectures are struggling to keep pace with the size and complexity of modern AI workloads. Advanced packaging helps connect processors, memory and computing systems more efficiently while managing power and cooling limitations inside large-scale AI environments.
The company said it is working with Taiwan-based partners including ASE, SPIL and PTI to develop next-generation packaging technologies for its upcoming 6th Gen AMD EPYC processors, codenamed “Venice.” AMD also said it had qualified what it described as the industry’s first 2.5D panel-based EFB interconnect technology alongside PTI.
At the center of the broader strategy is AMD Helios, the company’s rack-scale AI infrastructure platform scheduled for deployment beginning in the second half of 2026. The platform combines AMD Instinct MI450X GPUs, 6th Gen EPYC CPUs, networking systems and AMD’s ROCm software stack into integrated AI infrastructure systems designed for hyperscale deployment.
Rather than selling individual processors alone, companies are increasingly building complete AI infrastructure platforms that combine hardware, software, cooling systems and power management into unified deployments. That transition is reshaping how AI infrastructure is designed, manufactured and delivered.
Taiwan is also becoming more deeply embedded in that process. AMD’s investment spans not only semiconductor packaging companies but also manufacturing and system integration partners including Sanmina, Wiwynn, Wistron and Inventec. The partnerships reflect Taiwan’s growing role as one of the operational centers of the global AI infrastructure economy.
Dr. Lisa Su, Chair and CEO of AMD, said: “As AI adoption accelerates, our global customers are rapidly scaling AI infrastructure to meet growing compute demand. By combining AMD leadership in high-performance computing with the Taiwan ecosystem and our strategic global partners, we are enabling integrated, rack-scale AI infrastructure that helps customers accelerate deployment of next-generation AI systems”.
Power efficiency is becoming another major challenge shaping AI infrastructure decisions. As AI workloads consume more electricity and generate more heat, infrastructure providers are increasingly being forced to rethink cooling systems, interconnect technologies and deployment economics.
AMD’s announcement signals how the AI competition is evolving beyond model development and raw computing power. The next stage may depend just as heavily on who can manufacture, package and deploy AI infrastructure fast enough to support global demand.