After the humanoid robot's Spring Festival Gala performance: Top 10 prospects for embodied intelligence in the Year of the Horse

There is a subtle detail in the Year of the Horse Spring Festival Gala that is thought-provoking: within the first half-hour, three performances featured embodied intelligent robots appearing frequently. In 2025, robots still resemble trembling elderly people, needing assistance to walk. By 2026, they can tumble and perform martial arts, with clean and precise movements. Of course, the Spring Festival Gala is not a technology launch event, but it has always been a thermometer of social sentiment. When robots shift from being novel props on stage to performing roles, it signifies they are moving from laboratories toward industry, from curiosity to scale.

The questions also become more practical: where will robots first enter? Who will they replace first? Who will profit first? Who will be eliminated? By 2026, the true inflection point for embodied intelligent robots may already be approaching. I attempt to condense the possible key changes into ten predictions. These are not wish lists but judgments about which things will happen first in the next phase and which links will earliest realize value.

1. First work night shifts, then talk about entering homes

In 2026, the first large-scale deployment of robots will not be in homes but in enterprises. The first to be replaced will not be daytime workers but night shift workers. Night shifts have long faced three structural issues: significantly higher labor costs than day shifts, difficulty recruiting and high turnover, and the lowest requirements for social skills and understanding complex scenarios. Tasks like warehouse bin handling and sorting, manufacturing inspections and material replenishment, hotel linen delivery and security patrols will constitute the most intensive on-site scenarios in 2026. In these roles, as long as robots can avoid accidents, not drop the chain, and sustain an entire night shift, they will have a reason to be purchased.

The shortage of night shift workers is structural: aging populations, tight labor markets in service industries, and young people unwilling to work night shifts are not issues that can be reversed in a year or two. For industry and investment, in 2026, focus should not be on “household penetration rate,” but on two indicators: night shift substitution rate and the number of stable operating hours per robot in real night environments. These two figures are the true signals of whether robots are moving toward scale. In short: this year, robots will first help people stay up late; only later will they do household chores.

2. Blockbusters are tools, not humans

The biggest misconception in embodied intelligence is viewing competition as a “humanoid race,” where the more human-like the movements, the more advanced the robot. In 2026, the real gap will not be who moves more like a human, but who can reliably perform the most boring, repetitive physical labor over the long term in real environments.

Pushing carts,搬箱, shelving, picking and placing, simple sorting, opening doors, fixed-point inspections—these actions are not complex but constitute a large part of frontline work daily. In these scenarios, robots do not need dazzling degrees of freedom or complex emotional expression; they only need to avoid failures during high-frequency use. For enterprise clients, stable coverage of 80% of scenarios is far more valuable than demonstrating 100% capabilities.

Disagreements are emerging here: many teams still invest resources in high-degree-of-freedom actions and complex behaviors, but customers care more about stable output than technical showmanship. By 2026, this difference in orientation will be directly reflected in product metrics: core indicators will shift from “what actions can be performed” to “continuous operation time,” “fault intervals,” “fall rates,” and “manual intervention frequency.” Therefore, judging whether embodied intelligence is approaching scale should no longer rely on video demonstrations but on whether the robot can work continuously on production lines or sites without becoming a new source of instability. Spectators care about “what it can do,” but paying customers only care if “it can work long-term without causing trouble.” In short: the more tool-like, the closer to business.

3. Strength is judged by demos; worth is judged by mistakes

Large models will become widespread, and the gap in “intelligence” will narrow. The real factor determining whether a robot company can move beyond pilot projects into large-scale use is not how smart it is in ideal conditions, but whether it can handle frequent problems in the real world. Daily challenges include missed pickups, dropped objects, blocked passages, tool jams, and even sudden human proximity. If these are not handled well, it’s not just a poor experience but a safety risk. Those who can incorporate these common errors into their design, turning “what to do after a problem occurs” into a stable process, will be more likely to be truly deployed on production sites. Conversely, if every failure requires full manual oversight, it remains in the pilot stage.

This watershed has already begun to appear. More and more solutions emphasize anomaly handling, remote takeover, and post-incident playback, with safety boundaries written into cooperation agreements. For clients, reliability depends on whether failures are controllable—can the robot immediately contain risks, avoid injuring people or damaging objects, and record causes and responses clearly and traceably? Companies capable of this tend to reach stable cash flow earlier. In short: being able to work is not enough; avoiding accidents when mistakes happen is what counts.

4. To deliver, learn to simplify first

Many believe that by 2026, embodied intelligent robots will become increasingly “all-round.” I believe the opposite: products that can truly be delivered and replicated will actively become “simpler.” The more functions, the bulkier the system, and the more failure points. The more complex, the harder to verify, maintain, and mass produce. By 2026, a few high-reuse models will likely emerge: focused on handling, inspection, delivery, or fixed-position collaboration—doing one or two things thoroughly rather than trying to do ten.

This de-functionalization is not a technological regression but a stage of engineering maturity. Scale benefits are never about flashy features; they reward reliability: achieving extremely low failure rates on key actions, maintaining consistency under high-frequency use, and reassuring on-site personnel that it’s a tool, not a showpiece. Once products reach this stage, imagination gives way to stability and reproducibility.

In 2026, manufacturers’ marketing may shift from “what I can do” to “how long I can run continuously without issues.” For industry and investment, this signals that after model convergence, the real beneficiaries will be highly standardized components and control systems, not the most feature-rich machines. In short: the path to scale is paved with simplification.

5. The first to profit are often not the whole machine but key parts

If you only focus on “who will become the Apple of robotics,” you might miss the most certain profits in 2026. The reason is simple: full-machine manufacturers will face elimination battles with volatile orders, but key components will be used repeatedly across all machines, regardless of who makes the full robot. Think of robots as a combination of “skeleton, muscles, nerves, and heart”—the joints, drives, sensors, and power management—these “internal organs” will see sustained demand before the outer shell.

Actuators (servo motors + gearboxes + drives), high-consistency bearings and materials, tactile and force sensors, battery management systems (BMS) will encounter structural capacity shortages first in 2026. For ordinary investors, this means a straightforward insight: rather than betting on a star robot, focus on the essential parts all robots need. In short: sellers of “shovels” often make the earliest profits.

6. Selling is not just hardware, but a long-term “hardware + subscription + service” contract

By 2026, selling a robot will increasingly resemble selling a smartphone: delivery is just the beginning, and the real costs come afterward. Buyers want not just ownership but long-term, stable operation in real environments. Therefore, more collaborative models will emerge: an upfront hardware payment, annual software fees, and maintenance services. Essentially, buying a robot becomes akin to purchasing a long-term production tool rather than a one-time gadget.

This trend has precedents: automation equipment is easy to buy but often ends up unused, broken, or unmaintained. If a robot frequently alarms or requires engineers on-site, maintenance costs will quickly outweigh savings. The key to future orders is not initial demo performance but whether the robot can keep working months later, and whether issues can be quickly resolved. The stage on the Spring Festival Gala may show robots flipping, but enterprises need long-term guarantees of daily operation.

Competition will shift: more companies will be able to produce robots, but those that can keep them running, maintain them, and recover quickly will be rarer. In short: delivery is just the first day; running for a year is the real deal.

7. Advantage is not cheapness but speed of engineering and stability

Many companies still think that in this wave of embodied intelligence, advantage mainly comes from “lower cost.” But by 2026, the real key is engineering speed: rapid small-batch modifications, continuous problem discovery and quick fixes on real sites, turning complex structures into scalable manufacturing processes. This capability determines who can move from pilot to replication. Robots are not designed in conference rooms; they are “refined” on-site: every stall, fall, or misgrab is a starting point for the next improvement.

In 2026, the competition will be more brutal and more realistic: it’s not about who has the best concept, but who can fix problems within three months, streamline assembly, and simplify maintenance. Teams capable of these will deliver faster and enter reproducible zones sooner. In short: victory or defeat is not in the launch, but on the construction site and workshop.

8. One robot is a prototype; a group is productivity

The likely inflection point in 2026 is not just a smarter single machine, but whether multiple robots can work together. A single robot, no matter how capable, is just a demonstration. Only when a group of robots can coordinate, avoid collisions, relay tasks, and fill in for each other in the same space will they truly become efficient tools. The impressive “WuBOT” performance at the Spring Festival Gala is also due to this: it’s not one robot showing off, but a team working in concert.

This shifts industry competition from mechanical structure and flashy actions to task scheduling, path planning, handover rules, and site adaptation. What enterprises need is not a universal robot but a “robot team” that can operate stably in warehouses, factories, and hotel logistics. Who handles搬运, replenishment, inspections, how overtime is managed, how charging is scheduled, and how failures are handled—all these will matter. In 2026, more projects will shift from buying one to trying a batch, because only mass operation reveals true efficiency. In short: a single machine is a product; multiple machines form a system.

9. Ecosystems won’t start with stores, but with industry packages

“Robot app stores” will eventually appear, but in 2026, they are unlikely to resemble open mobile app stores. Instead, industry-specific packages will emerge first: one for warehouses, one for hotel logistics, one for factory stations, one for hospital logistics. Because enterprises want controllable, deliverable, and reusable solutions, not random installations.

This explains why competition in 2026 will not be solely about hardware. The real challenge is how to embed robots into workflows. Those who can turn scenarios into packaged solutions will gain repeat business earlier than those only selling general capabilities. Third-party ecosystems will more likely appear as system integrators and industry service providers. In short: creating industry templates first is the key to ecosystem growth.

10. The threshold for scale is not technology but responsibility and insurance

When embodied robots truly enter workplaces, the most difficult issues are not “can they perform actions” but “who is responsible if something goes wrong.” Injuries, damages, misoperations, downtime, data leaks—once these risks enter corporate decision-making, the industry shifts from a “tech race” to a “governance race.” The key to scaling in 2026 will not only be product capability but also three factors: are there industry standards, how responsibilities are divided, and whether insurance coverage is available.

It may sound unglamorous, but these determine whether robots can move from small pilots to large deployments. Will companies be willing to loosen control? For embodied intelligence robots to become a quasi-infrastructure, it often depends on whether they can be “clearly explained, adequately insured, and responsibly supported.” In short: technology opens the door; responsibility and insurance decide whether you can walk through.

Conclusion: From excitement to implementation, the watershed in 2026

Putting these ten predictions together, you will see that the main theme in 2026 is not romance: it’s not about everyone suddenly owning household robots or robots becoming omnipotent overnight, but about moving from pilots to replication, from videos to working hours, from showy tech to responsibility. The key leap for embodied intelligence robots is shifting from “looks powerful” to “practical and valuable.” For industry deployment and investment, the path becomes clearer: prioritize signals of large-scale night shift scenarios, focus on teams that can handle failures and turn delivery into templates, and seize key components and operational services with more certainty. The real inflection point is not whether robots can do backflips, but whether they can handle night shifts, perform heavy tasks reliably, contain mistakes, deliver reproducibly, and clarify responsibilities. Once these are achieved, embodied intelligent robots will no longer be just a spectacle at the Spring Festival Gala or a topic in tech circles, but a new factor of production in the real economy.

(Author Hu Yi is a data worker and author of “The Future is Promising: Walking with Artificial Intelligence.”)

(Source: The Paper)