The Automation Revolution Driven by AI and Encryption Technology: Bots' "ChatGPT Moment"

The emergence of ChatGPT has completely changed people's understanding and expectations of artificial intelligence. When large language models began to interact with the external software world, many believed that AI agents were the ultimate form. However, looking back at classic science fiction works reveals that humanity's true dream is to have artificial intelligence interact in the physical world in the form of Bots.

Industry experts believe that the "ChatGPT moment" in the field of Bots is about to arrive. In recent years, breakthroughs in artificial intelligence have been changing the industry landscape, while improvements in battery technology, latency optimization, and data collection will further shape future development. Encryption technology will also play an important role in this process. Bot safety, financing, evaluation, and education are vertical areas that need to be focused on.

Change Factors

artificial intelligence breakthrough

The progress of multimodal large language models is providing the necessary "brain" for Bots to perform complex tasks. Bots primarily perceive their environment through vision and hearing. Traditional computer vision models excel at object detection or classification, but struggle to translate visual information into purposeful action commands. Large language models perform well in text understanding and generation, but have limited perception capabilities of the physical world.

Vision-Language-Action Model ( VLA ) enables Bots to integrate visual perception, language understanding, and physical action within a unified computational framework. In February 2025, a company released a universal humanoid robot control model that set new industry standards with its zero-shot generalization capability and dual-system architecture. The zero-shot generalization feature allows Bots to adapt to new scenarios, objects, and instructions without the need for repeated training for each task. The dual-system architecture separates high-level reasoning from lightweight reasoning, achieving a commercial humanoid robot that combines human-like thinking with real-time accuracy.

Economic Bots Become a Reality

The technologies that change the world are all characterized by their accessibility. When the price of certain Bots falls below that of a mid-range car or the annual minimum wage in the United States, the world where physical labor and daily tasks are primarily handled by Bots is no longer far off.

From warehousing to consumer market

Robot technology is expanding from warehouse solutions to the consumer sector. This world is designed for humans—humans can perform all the tasks of specialized robots, while specialized robots cannot fulfill all human jobs. Robot companies are no longer limited to manufacturing factory-specific robots, but are instead developing more versatile humanoid robots. As a result, the forefront of robot technology is not only found in warehouses but will also permeate daily life.

Cost is one of the main bottlenecks for scalability. The most critical metric is the comprehensive cost per hour, calculated as the sum of the opportunity costs of training and charging time, task execution costs, and the cost of purchasing Bots, divided by the total operating hours of the Bots. This cost must be below the average wage level of the relevant industry to be competitive.

To fully penetrate the warehousing sector, the comprehensive cost of Bots must be below $31.39 per hour. In the largest consumer market—the private education and health services sector—this cost must be kept below $35.18. Currently, Bots are evolving towards being cheaper, more efficient, and more versatile.

The Next Breakthrough in Bots Technology

Battery Optimization

Battery technology has always been a bottleneck for user-friendly Bots. Early electric vehicles struggled to gain popularity due to limitations in battery technology, leading to short range, high costs, and low practicality, and Bots are facing the same dilemma. Some commercial Bots have a single charge duration of only 90 minutes to 2 hours. Users are clearly unwilling to manually charge every two hours, thus autonomous charging and docking infrastructure have become key development directions. Currently, there are two main modes of charging for Bots: battery replacement or direct charging.

The battery replacement mode achieves continuous operation by quickly replacing the depleted battery pack, minimizing downtime, and is suitable for field or factory scenarios. This process can be operated manually or automated.

Inductive charging uses wireless power supply methods. Although complete charging takes a long time, it can easily achieve a fully automated process.

Delay Optimization

Low-latency operations can be divided into two categories: environmental perception and remote control. Perception refers to the Bots' spatial awareness of the environment, while remote control specifically refers to real-time control by human operators.

Research shows that the Bots' perception systems begin with inexpensive sensors, but the technological moat lies in the integration of software, low-power computing, and millisecond-level precise control loops. Once the Bots complete spatial positioning, lightweight neural networks will mark elements such as obstacles, pallets, or humans. After the scene labels are input into the planning system, motor commands are generated and sent to the feet, wheel groups, or robotic arms immediately. A perception delay of less than 50 milliseconds is equivalent to human reflex speed—any delay beyond this threshold leads to clumsy Bot actions. Therefore, 90% of decisions need to be made locally through a single vision-language-action network.

Fully autonomous Bots must ensure that the performance of the VLA model has a latency of less than 50 milliseconds; remote-controlled Bots require that the signal latency between the operation end and the Bots does not exceed 50 milliseconds. The importance of the VLA model is particularly highlighted here - if visual and text inputs are processed by different models before being input into the large language model, the overall latency will far exceed the 50 milliseconds threshold.

Data Collection Optimization

There are mainly three ways to collect data: real-world video data, synthetic data, and remote-controlled data. The core bottleneck between real data and synthetic data lies in bridging the gap between the physical behaviors of Bots and the video/simulated models. Real video data lacks physical details such as force feedback, joint movement errors, and material deformation; synthetic data, on the other hand, lacks unpredictable variables such as sensor failures and friction coefficients.

The most promising data collection method is remote control - where human operators remotely control Bots to perform tasks. However, labor costs are the main constraint on remote-controlled data collection.

Custom hardware development is also providing new solutions for high-quality data collection. A certain company combines mainstream methods with custom hardware to collect multidimensional human motion data, which is processed into datasets suitable for training Bots' neural networks, providing massive high-quality data for AI robot training with a rapid iteration cycle. These technological pipelines collectively shorten the conversion path from raw data to deployable robots.

Key Exploration Areas

encryption technology and Bots integration

Encryption technology can incentivize trustless parties to enhance the efficiency of Bots networks. Based on the key areas mentioned earlier, encryption technology can improve efficiency in infrastructure integration, latency optimization, and data collection.

The decentralized physical infrastructure network ( DePIN ) is expected to revolutionize charging infrastructure. When humanoid Bots operate globally like cars, charging stations need to be as accessible as gas stations. Centralized networks require massive upfront investments, while DePIN distributes costs among node operators, allowing charging facilities to rapidly expand into more areas.

DePIN can also optimize remote control latency using distributed infrastructure. By aggregating computing resources from geographically dispersed edge nodes, remote control instructions can be processed by local or nearest available nodes, minimizing data transmission distance and significantly reducing communication latency. However, current DePIN projects mainly focus on decentralized storage, content distribution, and bandwidth sharing. Although some projects demonstrate the advantages of edge computing in streaming media or the Internet of Things, it has not yet extended to the field of Bots or remote control.

Remote control is the most promising data collection method, but the cost for centralized entities to hire professionals for data collection is extremely high. DePIN addresses this issue by incentivizing third parties to provide remote control data through encryption tokens. A certain project is building a global network of remote operators, converting their contributions into tokenized digital assets, forming a permissionless decentralized system—participants can both earn rewards and participate in governance while assisting in AGI Bots training.

Security is always a core concern.

The ultimate goal of robotics technology is to achieve complete autonomy, but as some science fiction movies warn, what humanity least wants to see is autonomy turning robots into offensive weapons. The safety issues of large language models have raised concerns, and when these models possess physical action capabilities, the safety of robots becomes a key prerequisite for social acceptance.

Economic security is one of the pillars of the prosperity of the Bots ecosystem. A certain company in this field is building a decentralized machine coordination layer that achieves device identity authentication, physical presence verification, and resource acquisition through encryption proof. Unlike simple task market management, this system allows Bots to autonomously prove identity information, geographic location, and behavior records without relying on centralized intermediaries.

Behavior constraints and identity authentication are executed through on-chain mechanisms, ensuring that anyone can audit compliance. Bots that meet safety standards, quality requirements, and regional regulations will be rewarded, while violators will face penalties or disqualification, thus establishing accountability and trust mechanisms in the autonomous machine network.

The third-party re-staking network can also provide equivalent security guarantees. Although the penalty parameter system still needs to be improved, the relevant technology has entered a practical stage. It is expected that industry security standards will soon be established, and at that time, penalty parameters will be modeled according to these standards.

Implementation Plan Example:

• Bots company joins the re-staking network.
• Set verifiable forfeiture parameters (e.g., "apply more than 2500 Newtons of human contact force");
• Stakers provide margin to ensure that Bots comply with parameters;
• In case of violations, the pledge will be used as compensation for the victims.

This model incentivizes companies to prioritize security while promoting consumer acceptance through the insurance mechanism of the staked fund pool.

Filling the Gaps in the Bots Technology Stack

Unlike AI, the field of Bots is hard to enter with limited funding. To achieve the popularization of Bots, the development threshold needs to be reduced to the same level of convenience as AI application development. There is room for improvement in three areas: financing mechanisms, evaluation systems, and educational ecosystems.

Financing is a pain point in the field of Bots. Developing computer programs only requires a computer and cloud computing resources, while building a fully functional robot necessitates purchasing hardware such as motors, sensors, batteries, etc., with costs easily exceeding $100,000. This hardware nature makes robot development less flexible and much more expensive compared to AI.

The evaluation infrastructure for robots in real-world scenarios is still in its infancy. The AI field has established a clear system of loss functions, and testing can be fully virtualized. However, excellent virtual strategies cannot be directly translated into effective solutions in the real world. Robots need evaluation facilities to test autonomous strategies in diverse real-world environments in order to achieve iterative optimization.

Once the infrastructure matures, there will be a massive influx of talent, and humanoid robots will replicate the explosive growth curve of Web2. A certain encryption bot company is moving in this direction—its open-source project ( "robot version of the Android system" ) transforms raw hardware into economically aware, upgradable intelligent agents. Visual, linguistic, and motion planning modules can be plug-and-play like mobile applications, with all reasoning steps presented in clear English, allowing operators to audit or adjust behaviors without touching firmware. This natural language reasoning capability allows a new generation of talent to seamlessly enter the robotics field, taking a crucial step toward igniting the robot revolution on an open platform, just as the open-source movement has accelerated AI.

Talent density determines the trajectory of the industry. A structured and inclusive education system is crucial for the talent supply in the Bots field. The landing of a certain company on NASDAQ marks the beginning of a new era where intelligent machines participate in both financial innovation and physical education. The company, in collaboration with its partners, has jointly announced the launch of the first universal education curriculum based on humanoid robots in public K-12 schools in the United States. The curriculum is designed to be platform-agnostic, adaptable to various robot forms, and provides students with practical operation opportunities. This positive signal reinforces the industry's judgment: in the coming years, the education resources for Bots.