Norwegian robotics firm 1X has launched NEO, a household humanoid robot designed to automatically perform daily chores and provide personalized assistance via built-in artificial intelligence (AI). It is priced at $20,000 for outright purchase, or available through a subscription model at $499 per month.

NEO helps humans manage household tasks efficiently. With simple voice commands or button presses from users, it can process daily tasks such as folding clothes and organizing items in real time. It can also learn new skills by scheduling expert guidance. Equipped with a built-in large language model (LLM) and multimodal perception capabilities, NEO supports natural conversations and offers contextual assistance—for example, identifying ingredients to recommend recipes. It also has a memory function, enabling consistent and personalized interactions. Through AI learning in real-world scenarios and software updates, NEO continuously evolves in home environments, gradually expanding its service capabilities. Its hardware platform adopts a patented tendon-driven system, ensuring gentle and safe movements. It features dexterous hands with 22 degrees of freedom and a soft body structure throughout. Weighing approximately 30 kilograms, it can lift objects of nearly 70 kilograms, while operating at a noise level of only 22 decibels.

Pre-orders for NEO are now open, with the first batch of orders to be delivered in 2026. Berent Bornich, CEO and founder of 1X, stated: “Humanoid robots were once the stuff of science fiction… Later, they became research tools, but now, with the launch of NEO, they have truly become a reality.”

However, as such robots with remote assistance functions enter households, widespread concerns have been raised regarding privacy protection. When NEO’s expert mode is activated, 1X employees can view the user’s home environment from NEO’s perspective. In response, 1X has emphasized that all household assistant services can only be activated upon active request by the user, and users can terminate the service at any time.

Meanwhile, the NEO robot is currently unable to complete all household chores independently. Some functions still require remote control by 1X employees, who direct the robot’s movements like manipulating a puppet. This human assistance helps 1X accumulate training data for AI development.

“True autonomy remains the biggest challenge facing the humanoid robot industry,” Brett Adcock, CEO of Figure AI, a U.S.-based humanoid robot startup, has also noted. Currently, no product in the entire humanoid robot industry can achieve fully autonomous operation.

How to interpret the development beyond the headline

Fast-moving technology and crypto stories should separate confirmed facts from forecasts, market reaction and interpretation. Product launch, adoption, liquidity, regulation and execution can all determine whether an early headline becomes a durable shift.

The useful follow-up is concrete evidence: shipped capability, user growth, audited transactions, financial guidance or regulatory decisions that confirm or weaken the initial narrative.

Watch second-order effects

Major developments can change incentives for developers, creators, exchanges, infrastructure providers and users. The practical impact may appear through costs, standards, distribution or policy rather than through the headline event itself.

Keeping long-term scenarios conditional makes later updates easier to interpret without rewriting early speculation as fact.

Separate market sentiment from product execution

Price movement and social attention can react instantly, while technology deployment takes longer. A useful analysis asks what operational milestone would prove that the story has changed real behavior.

That milestone might be production use, a measurable cost change, a regulatory approval or sustained customer demand rather than one day of market volatility.

Identify the assumptions behind the forecast

Every long-term scenario depends on assumptions about adoption, competition, capacity and policy. Making those assumptions explicit helps readers understand what evidence would cause the outlook to improve or weaken.

Practical implementation check

Before adopting the mechanism described above, define the exact business rule, who controls it and what evidence will show that it worked. Test the failure path as carefully as the successful path, especially when rights, payments or wallet actions are involved.

The final system should also document versioning, rollback and the assumptions that may become stale over time. That turns a one-off experiment into an operating model that creators, users and developers can understand.