CES 2026: Everything revealed, from Nvidia’s debuts to AMD’s new chips to Razer’s AI oddities

2026-01-06 · Source: Robotics News | TechCrunch · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cloud Computing & IT Infrastructure · Depth: Advanced, extended

Summary

CES 2026 in Las Vegas highlights AI as a central theme, with major announcements from Nvidia, AMD, Amazon, and others. Nvidia unveiled its Rubin computing architecture, designed to replace Blackwell later this year, offering speed and storage upgrades for AI adoption. Nvidia also showcased its Alpamayo family of open-source AI models for autonomous vehicles, aiming to establish its infrastructure as the "Android for generalist robots." AMD introduced new Ryzen AI 400 Series processors to expand AI's reach in personal computers. Boston Dynamics and Google partnered to train Atlas robots, while Amazon expanded Alexa's AI capabilities with Alexa.com and revamped Ring features. Razer presented Project Motoko (smart glasses without glasses) and Project AVA (an AI companion avatar). Lego made its first CES appearance with Smart Play System bricks that interact and play sounds, starting with Star Wars themes. The event also featured discussions on AI infrastructure, its seamless adoption, high utilization, and significant funding, with a focus on enterprise and physical AI applications.

Key takeaway

For CTOs and R&D leaders evaluating AI investments, recognize that the industry is undergoing a dual platform shift where AI is both an application and a new way to develop and run software. Prioritize investments in full-stack AI solutions that offer energy efficiency, confidential computing, and power smoothing, such as Nvidia's Rubin architecture, to ensure long-term scalability and cost-effectiveness for your AI initiatives.

Key insights

AI is driving a fundamental platform shift, reinventing computing across hardware, software, and applications with significant investment.

Principles

• AI infrastructure requires seamless adoption and high utilization.
• Open models accelerate AI proliferation and developer mindsets.
• Physical AI demands simulation and synthetic data for training.

Method

AI applications are built on proprietary and customized language models within an agentic, reasoning framework that accesses tools, files, and other agents, often using multi-model and multi-cloud architectures.

In practice

• Utilize agentic systems for enterprise interfaces beyond traditional UIs.
• Employ synthetic data generation to train physical AI models for diverse scenarios.
• Adopt open-source models for cost-effective, scalable AI workloads.

Topics

• NVIDIA AI Hardware
• Autonomous Vehicle AI
• Robotics AI
• AI Infrastructure
• Enterprise AI Applications

Best for: Investor, CTO, Computer Vision Engineer, AI Engineer, AI Architect, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by Robotics News | TechCrunch.