JSE restricts the use of artificial intelligence agents due to difficulties in measuring business value

For companies implementing artificial intelligence, key questions are not only about choosing a model but also about where the AI operates, its cost, and the ability to keep confidential data under the company's control. PeakVision AI offers a solution that involves moving most of the AI tasks inside the enterprise.

The company's system considers the task, rather than an individual application, as the main unit of work: users state a goal, and AI agents perform the necessary steps in a local computing environment. Co-founder and CTO Liangyu Nie told TechNode that PeakVision AI was founded in 2024, overcoming two major obstacles to AI adoption in corporations: the complexity of technology deployment and concerns over data security.

In March 2026, the company opened its headquarters in Hangzhou and received government support for research and development amounting to 15 million yuan. In May, it presented its first enterprise systems and completed an initial funding round, followed by a second round worth tens of millions of yuan in August.

PeakVision AI creates an enterprise AI stack that integrates locally deployed computing, FengKan AIOS, and AI agent services. This system is designed to focus on how agents complete tasks, rather than how people move between different applications. As Nie noted, users no longer need to load different applications; they just need to tell the AI what they want via a chat window, and it will do the work.

The startup's central thesis is that enterprise software will increasingly be structured around intent and execution, rather than menus, files, and separate applications. The company plans to make FengKan AIOS open source by the end of October. PeakVision AI monetizes through hardware, subscriptions, and custom enterprise AI implementations. The company's broader goal is to transform project-specific work into universal solutions for various industries.

FengKan AIOS supports third-party hardware, while PeakVision AI takes a selective approach to its skills ecosystem. Nie clarified that users' end skills, which may contain proprietary workflows, will remain untouched. However, more generalized skills created by partners and leading engineers can be released to the market.

Currently, the company is testing its model through the FDE academy, launched by Shanghai Sixunited Intelligent Technology with support from Advanced Micro Devices (AMD). AMD provides computing hardware, technical toolchains, and resources for projects. PeakVision AI also collaborates with chip manufacturers in early product development stages to optimize costs and performance, including a partnership with ChangXin Memory Technologies to reduce memory costs.

Unlike companies such as Wonderful, which target large, often regulated organizations and provide them with AI platforms and implementation teams, PeakVision AI targets small and medium-sized businesses. Nie emphasized that these enterprises face the same problems—costs, technical complexity, and data security—but have fewer resources to solve them. PeakVision AI uses inexpensive hardware from the Chinese supply chain so that each implementation becomes a repeatable solution for similar clients.

Nie stated:

Previously, artificial intelligence was used to create images, videos, and write software code. However, a recent news item from the USA showed that AI is capable of developing blueprints for firearms. The main concern is that AI technologies could simplify access to information on how to create weapons.

Kode Wilson, a specialist in 3D-printed weapons in the USA, presented an AI model called GatGPT. He stated that this model was able to design a weapon named WASP, which is manufactured using 3D printing. Wilson considers this example the first case of weapon creation using AI.

WASP is a compact, single-shot pistol capable of firing only once per shot. It is being discussed due to the involvement of AI in the design development. GatGPT is presented not as a regular chatbot answering general questions, but as a specialized model working with weapon information and blueprints.

According to a report by The Verge, this model was trained on an extensive database of digital firearm designs. Wilson's team tasked it with creating a new design, after which the AI proposed a concept that was then refined by a human group. Thus, it was not a completely autonomous creation by AI, as the process included human guidance, analysis of existing models, and technical verification.

Therefore, this event is viewed not just as the emergence of a new type of weapon, but as a turning point in the application of AI. Previously, generative AI was used to accelerate the creation of images, articles, videos, and software. But when this technology is used in weapon design, it directly affects human safety.

The other side of this story is also important. According to The Verge, during the demonstration, three attempts were made to fire the WASP. Two shots were fired, while the first attempt was unsuccessful. Experts also identified several flaws in the design.

This means that although AI was able to prepare a blueprint, it does not prove that it can create more reliable or advanced weapons than humans. At this moment, it is only an initial experiment, and questions remain open regarding its reliability, safety, and long-term functionality.

The main reason for concern is precisely this. Traditional weapon manufacturing requires significant technical knowledge and many resources. However, if AI begins to easily provide information related to design, some obstacles in the weapon production process may disappear.

The organization Everytown, which deals with gun safety issues, expressed its concern in an interview with The Verge, noting that such models could facilitate the creation of weapons even for people who lack the necessary technical knowledge or do not meet the legal requirements for owning a firearm.

In this context, the term 'Ghost Gun' is actively discussed. It is usually applied to pistols made at home or assembled from individual parts without a serial number, like standard weapons. This makes them difficult to identify and track.

Nevertheless, not every 3D-printed object is a weapon, and not every homemade weapon is illegal. Legislation can vary depending on the country and region. However, the possibility of manufacturing such weapons without identification data could complicate law enforcement efforts to identify the manufacturer or user.