由 jingzhang 
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.
Measure the full contribution margin
Gross revenue can hide payment fees, AI serving cost, creator share and support burden. Contribution margin is more useful for deciding whether growth is creating economic value.
Segment the calculation by product or customer type because one blended percentage can conceal weak economics in a fast-growing area.
Use scenario analysis for uncertain inputs
AI businesses face volatile usage and infrastructure cost. Base, upside and downside cases help management see which assumptions matter most and where protective actions are available.
Reconcile forecast with actual drivers monthly
Instead of only comparing total spend or revenue, explain how much variance came from volume, price, mix and unit cost. This turns the forecast into a learning system for the business.
Start with the business driver behind the metric
A finance ratio is useful only when teams understand what operational behavior moves it. Break revenue, margin or payback into user volume, conversion, pricing, usage and variable cost so a variance can be traced to a real product or sales change.
This makes forecasts easier to update because assumptions can change independently instead of rebuilding one large top-down model.
Use cohort evidence before extrapolating growth
AI products often change quickly, so recent cohorts may behave differently from early adopters. Compare retention, spending and support cost by signup period or customer segment before assuming historical averages will continue.
Connect financial metrics to operational decisions
The purpose of the model is not only reporting. It should help decide pricing, hiring, infrastructure commitments and channel spend while there is still time to change course.
Separate leading indicators from financial outcomes
Usage, retention and pipeline often move before recognized revenue or cash. A useful dashboard combines these leading signals with financial results so management can distinguish a temporary accounting lag from a real deterioration in demand.
Definitions should remain stable enough that teams do not change the metric when performance becomes inconvenient.
Document assumptions beside the forecast
Model outputs are only as reliable as their assumptions about pricing, conversion, cost and timing. Keeping those assumptions visible makes scenario analysis faster and reduces false precision.
Review the model when product behavior changes
New modalities, pricing plans or sales motions can invalidate old averages. Finance models should evolve with the product rather than forcing new behavior into outdated categories.
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