36Kr learned that “Maimai Technology Group” (hereinafter referred to as “Maimai Technology”) has completed a Pre – A round of financing exceeding 100 million yuan, with a post – investment valuation exceeding 1 billion yuan. The round was jointly led by institutions such as Qihong Yuyuan, Xinglian Capital, Spring Light Lane, and Honglian Qiyuan, with some existing shareholders participating in the follow – on investment. The funds from this round of financing will mainly be used for the R & D and innovation of core technologies such as AI agricultural large models and intelligent sensing devices.

According to Li Nan, the founder, chairman, and CEO of Maimai Technology Group, this financing is the largest – scale early – stage financing in the smart agriculture field recently.

Maimai Technology is an artificial intelligence company that takes data, algorithms, and scenarios as its core foundation and “technology + consumption” as its core business model. It transforms traditional agriculture through agricultural production technologies and information technologies such as the Internet of Things, artificial intelligence, cloud computing, and blockchain. Its headquarters is located in the core area of Zhongguancun, Beijing.

When talking about why he entered the agricultural sector, Li Nan admitted that it stemmed from two heart – wrenching contrasts. When he worked in a large Internet company before, he saw that cutting – edge technologies such as aerospace remote sensing and virtual reality were concentrated in the consumer entertainment field, while the agriculture that supported consumption lagged behind in technology. Later, during a rural survey, he found that over 95% of farmers in rural areas were over 55 years old, and “people under 55 neither liked nor knew how to farm.” Agriculture was facing a crisis of “no successors.”

Under such an impact, Li Nan entered the agricultural field and spent half a year assembling a so – called “luxury” founding team. Different from traditional agricultural enterprises or pure technology companies, its core team consists of three types of cross – border elites, forming a stable triangular structure of “understanding agriculture, strong in technology, and good at commercialization.”

Since 2018, Maimai Technology’s business model has been iterating according to industrial needs.

In the first two years, in Li Nan’s words, the company was more like an “agricultural technology solution integrator,” piecing together various technologies to solve specific problems of farmers, targeting the technical needs of customers. Later, in order to become a technology company that understands the industry best, Maimai Technology completed the innovation of a new model, providing customers with technical capabilities and helping them complete the connection between production and sales.

What really helped Maimai Technology build its moat was the investment in relevant research on artificial intelligence starting in 2021. After several years of exploration, Maimai Technology has successfully built a core foundation of “model + data + scenario” and a complete crop growth model system covering “description, diagnosis, prediction, and decision – making,” deeply empowering key links in agricultural planting management and establishing advantages in crop models, data computing power, and in – depth scenario development.

The Super Brain Smart Agriculture Big Data Platform is a digital infrastructure for smart agriculture built by Maimai Technology based on technologies such as the Internet of Things, AI, big data, satellite remote sensing, and blockchain, combined with agricultural professional knowledge and front – line business experience. The platform takes “models, scenarios, and data” as its core elements and realizes the collection, processing, analysis, and application of data across the entire agricultural industry chain through a three – layer architecture (information perception layer, data processing layer, and intelligent analysis layer), providing data empowerment for the entire process of agricultural production, circulation, and sales.

According to Li Nan, the “crop large model” developed by Maimai Technology is not a “simplified version” of a general large model, but a “coupled system of small models” focusing on vertical scenarios, which can accurately solve specific industrial problems. For example, a vertical model can only solve the problem of peach planting in Central China, while different models are needed for plum and cherry planting in East China.

As of now, Maimai Technology has completed the R & D of nearly a thousand vertical scenario models for 15 major categories and over 200 sub – categories of staple food crops such as wheat and rice and cash crops such as strawberries and blueberries. It has also deployed multi – point data collection in places such as Beijing, Jingmen in Hubei, Chongqing, and Hainan for model verification and optimization.

The value of technology ultimately needs to be verified by industrial results. A citrus – growing customer in Hubei previously faced three major pain points: a high proportion of blemished fruits, which prevented them from entering supermarkets; a low percentage of large fruits, resulting in low selling prices; and insufficient sugar content, making the fruits less competitive in taste. After analysis, the Maimai Technology team took “water” as the core regulatory factor and quantified two key dimensions through the model: one is the water environment (drought duration, air humidity, soil moisture), and the other is the crop’s water demand pattern (upper and lower limits of water demand at different growth stages).

After four years of practical application, the results are remarkable: the blemished fruit rate has dropped to less than 5%, the large – fruit rate has stabilized above 85%, and the sugar content has increased by 2.5 – 2.7 units, ultimately helping the customer successfully enter some high – end supermarket channels.

Taking a large blueberry model in Yunnan as an example, based on in – depth analysis of the blueberry crop mechanism, Maimai Technology scientifically demonstrated the natural environment in Mengzi and established a comprehensive blueberry model system, including environment simulation and optimization models, soil water and fertilizer simulation and optimization models, etc. Eventually, the application of the crop growth model helped increase the blueberry yield by up to 30%.

In the strawberry – planting large model, Maimai Technology combines its self – developed crop growth model with agricultural Internet of Things technology. Through the intelligent collection and analysis of strawberry – planting environment data, it can make accurate decisions on environmental parameters such as light, temperature, humidity, and carbon dioxide in the strawberry – planting environment. Practical data shows that the application of the strawberry growth model can shorten the strawberry growth cycle by 10% and increase the yield by 20%.

In terms of R & D, Maimai Technology has two core R & D institutions: the National R & D Center and the Agricultural Industry Research Institute. The National R & D Center has assembled 7 national – level expert teams and has a professional R & D team of 270 people, focusing on the R & D of hard technologies such as drones, automated equipment, and visual recognition technology, covering the entire chain of technological breakthroughs in agricultural artificial intelligence. The Agricultural Industry Research Institute focuses on research at agricultural bases, specifically studying crop growth mechanisms and product optimization, and is committed to breakthroughs in cutting – edge technologies such as large crop growth models.

In terms of technology accumulation, Maimai Technology holds over 120 patents and software copyrights related to smart agriculture and has served over 170 Fortune 500 – level customers in China. On the production side, it is deeply involved in the technology and production guidance of over 70 modern digital farms, with a total production capacity exceeding 10 billion yuan; it has established ecological cooperation with over 190 modern digital farms, with a total production capacity exceeding 18 billion yuan.

It is worth mentioning that the company has had positive audited net profits for six consecutive years, and its revenue growth rate has remained above 200% for many years. Currently, the company has initiated the planning for a Series A round of financing and has clearly set the strategic goal of officially striving for an IPO in 2027. In the future, it will continue to achieve exponential growth in revenue and profit driven by technology.

Since its emergence a few years back, generative AI has been the center of controversy, from environmental concerns to deepfakes to the non-consensual use of data to train models. One of the most troubling issues has been deepfakes and voice cloning, which have affected everyone from celebrities to government officials. 

In May, a deepfake video of Qatari Emir Sheikh Tamim bin Hamad Al Thani went viral. It appeared to show him criticizing US President Donald Trump after his Middle East tour and claiming he regretted inviting him. Keyframes from the clip were later traced back to a CBS 60 Minutes interview featuring the Emir in the same setting.

Most recently, YouTube drew backlash for another form of non-consensual AI use after revealing it had deployed AI-powered tools to “unblur, denoise, and improve clarity” on some uploaded content. The decision was made without the knowledge or consent of creators, and viewers were also unaware that the platform had intervened in the material.

In February, Microsoft disclosed that two US and four foreign developers had illegally accessed its generative AI services, reconfigured them to produce harmful content such as celebrity deepfakes, and resold the tools. According to a company blog post tied to its updated civil complaint, users created non-consensual intimate images and explicit material using modified versions of Azure OpenAI services. Microsoft also stated it deliberately excluded synthetic imagery and prompts from its filings to avoid further circulation of harmful content.

THE RISE OF FAKE CONTENT

Matin Jouzdani, Partner, Data Analytics & AI at KPMG Lower Gulf, says more and more content is being produced through AI, whether it’s commentary, images, or clips. “While fake or unauthorized content is nothing new, I’d say it’s gone to a new level.  When browsing content, we increasingly ask, ‘Is that AI-generated?’ A concept that just a few years ago barely existed.”

Moussa Beidas, Partner and ideation lead at PwC Middle East, says the ease with which deepfakes can be created has become a major concern.

“A few years ago, a convincing deepfake required specialist skills and powerful hardware. Today, anyone with a phone can download an app and produce synthetic voices or images in minutes,” Beidas says. “That accessibility means the issue is far more visible, and it is touching not just public figures but ordinary people and businesses as well.”

Though regulatory frameworks are evolving, they still struggle to catch up to the speed of technical advances in the field. “The Middle East region faces the challenge of balancing technological innovation with ethical standards, mirroring a global issue where we see fraud attempts leveraging deepfakes increasing by a whopping 2137% across three years,” says Eliza Lozan, Partner, Privacy Governance & Compliance Leader at Deloitte Middle East.

Fabricated videos often lure users into clicking on malicious links that scam them out of money or install malware for broader system control, adds Lozan.

These challenges demand two key responses: organizations must adopt trustworthy AI frameworks, and individuals must be trained to detect deepfakes—an area where public awareness remains limited.

“To protect the wider public interest, Digital Ethics and the Fair Use of AI have been introduced and are now gaining serious traction among decision-makers in corporate and regulatory spaces,” Lozan says.

DEFINING CONSENT

Drawing on established regulatory frameworks, Lozan explains that “consent” is generally defined as obtaining explicit permission from individuals before collecting their data. It also clearly outlines the purpose of the collection—such as recording user commands to train cloud-based virtual assistants.

“The concept of proper ‘consent’ management can only be achieved on the back of a strong privacy culture within an organization and is contingent on privacy being baked into the system management lifecycle, as well as upskilling talent on the ethical use of AI,” she adds.

Before seeking consent, Lozan notes, individuals must be fully informed about why their data is being collected, who it will be shared with, how long it will be stored, any potential biases in the AI model, and the risks associated with its use.

Matt Cooke, cybersecurity strategist for EMEA at Proofpoint, echoes this: “We are all individuals, and own our appearance, personality, and voice. If someone will use those attributes to train AI to reproduce our likeness, we should always be asked for consent.”

There’s a gap between technology and regulation, and the pace of technological advancement has seemingly outstripped lawmakers’ ability to keep up. 

While many ethically minded companies have implemented opt-in measures, Cooke says that “cybercriminals don’t operate with those levels of ethics and so we have to assume that our likeness will be used by criminals, perhaps with the intention of exploiting the trust of those within our relationship network.”

Beidas simplifies the concept further, noting that consent boils down to three essentials: people need to know what is happening, have a genuine choice, and be able to change their mind.

“If someone’s face, voice, or data is being used, the process should be clear and straightforward. That means plain language rather than technical jargon, and an easy way for individuals to opt out if they no longer feel comfortable,” he says.

TECHNOLOGY SAFEGUARDS

Still, the idea of establishing clear consent guidelines often seems far-fetched. While some leeway is given due to the technology’s relative newness, it is difficult to imagine systems capable of effectively moderating the sheer volume of content produced daily through generative AI, and this reality is echoed by industry leaders.

In May, speaking at an event promoting his new book, former UK deputy prime minister and ex-Meta executive Nick Clegg said that a push for artist consent would “basically kill” the AI industry overnight. He acknowledged that while the creative community should have the right to opt out of having their work used to train AI models, it is not feasible to obtain consent beforehand.

Michael Mosaad, Partner, Enterprise Security at Deloitte Middle East, highlights some practices being adopted for generative AI models. 

“Although not a mandatory requirement, some Gen AI models now add watermarks to their generated text as best practice,” he explains.

“This means that, to prevent misuse, organizations are embedding recognizable signals into AI-generated content to make it traceable and protected without compromising its quality.”

Mosaad adds that organizations also voluntarily leverage AI to fight AI, using tools to prevent the misuse of generated content by limiting copying and inserting metadata into text. 

Expanding on the range of tools being developed, Beidas says, “Some systems now attach content credentials, which act like a digital receipt showing when and where something was created. Others use invisible watermarks hidden in pixels or audio waves, detectable even after edits.”  

“Platforms are also introducing their own labels for AI-generated material. None of these are perfect on their own, but layered together, they help people better judge what they see.”

GOVERNMENT AND PLATFORM REGULATIONS

Like technology safeguards, government and platform regulation are still in the air. However, their responsibility remains heavy, as individuals look to them to address online consent violations.

While platform policies are evolving, the challenge is speed. “Synthetic content can spread across different apps in seconds, while review processes often take much longer,” says Beidas. “The real opportunity lies in collaboration—governments, platforms, and the private sector working together on common standards such as watermarking and provenance, as well as faster response mechanisms. That is how we begin to close the gap between creation and enforcement.”

However, change is underway in countries such as Qatar, Saudi Arabia, and the UAE, which are adopting AI regulations or guidelines, following the example of the European Union’s AI Act.

Since they are still in their early stages, Lozan says, “a gap persists in practically supporting organizations to understand and implement effective frameworks for identifying and managing risks when developing and deploying technologies like AI.”

According to Jouzdani, since the GCC already has a strong legal foundation protecting citizens from slander and discrimination, the same principles could be applied in AI-related cases. 

“Regulators and lawmakers could take this a step further by ensuring that consent remains relevant not only to the initial use of content but also to subsequent uses, particularly on platforms beyond immediate jurisdiction,” he says, adding the need to strengthen online enforcement, especially when users remain anonymous or hidden.

Recently, the “Opinions on Deeply Implementing the ‘Artificial Intelligence +’ Initiative” issued by the State Council clearly stated that by 2027, the penetration rate of new – generation intelligent terminals and AI agents should exceed 70%, marking that the “AI agent economy” has entered an accelerated implementation phase. Against this policy background, the 3rd China AI Agent Annual Conference will be held in Shanghai on November 21st. The conference, themed “Initiating a New Intelligent Journey”, is hosted by MetaverseFamily. It focuses on two core tracks: enterprise – level and financial AI agents, bringing together the forces from upstream and downstream of the industrial chain to solve the pain points in the implementation of agent technologies and promote the effective implementation of the “Artificial Intelligence +” policy.

Two Core Segments Drive to Create a “Benchmark Platform” for Industry Exchange

As an important industry event after the implementation of the “Artificial Intelligence +” policy, this conference takes “practicality” and “precision” as its core. Through the form of “setting the tone at the main forum + solving problems at sub – forums + empowering through special sessions”, it provides participants with all – dimensional communication opportunities.

The Main Forum Focuses on AI Agent Trends and Determines the Technological Direction: In the morning, industry experts will discuss “the latest development and future opportunities of AI agents”, deeply analyze three technological paths: multimodal fusion, tool enhancement, and memory upgrade. At the same time, in line with the requirements of the “Opinions”, they will explore how agents can be deeply integrated with six key sectors, providing direction for enterprise layout.

Sub – forums Target “Enterprise – level and Financial AI Agents” and Provide Practical Solutions: Two sub – forums are set up in the afternoon to precisely meet the needs of different fields. Among them, the sub – forum on “Enterprise – level AI Agents Driving Marketing and Business Transformation” will break down the practical methods of local deployment of agents and database construction, share cases of cost reduction through business process automation, and help enterprises transform technologies into actual benefits. The sub – forum on “Financial AI Agents Reshaping the Industry’s Future” focuses on topics such as the technical principle of the 7×24 “risk – control sentinel” and the upgrade of intelligent customer service driven by large models, providing references for financial institutions to balance innovation and compliance.

Strengthen Resource Matching

The conference also features in – depth breakdowns of over 10 benchmark cases, a fireside chat among agent leaders, an annual award ceremony, and over 15 technology demonstration and experience areas. Participants can experience up close the application of agents in scenarios such as contract review and risk – control interception, and connect with over 300 industry professionals.

Supported by High – level Decision – Makers and Covering the Entire Industrial Chain

This conference has attracted the core forces of the AI agent industrial chain. The participants are characterized by a “high – end” profile. According to statistics, 30% of the participants are enterprise chairmen/general managers, and 25% are management personnel, covering key departments such as planning, marketing, information technology, and business, which can directly promote the implementation of cooperation.

As the conference organizer, MetaverseFamily is a hub – type media platform in the AI and metaverse fields. It has rich industry resources and event experience, providing strong support for the effectiveness of the conference. So far, the platform has held over 20 offline AI and metaverse conferences and over 40 online events, covering over 100,000 industry practitioners and having over 20,000 high – quality private WeChat group members. At the same time, it has successfully facilitated over 20 business cooperations, including the investment promotion project in Nanjing Niushoushan, the metaverse business cooperation with Mobile Tmall, and the upgrade of the AI shopping experience in an outlet mall, which can effectively connect resources and promote cooperation for participants.

In addition to the core topics, the “2025 AI Agent Annual Award Ceremony” at the conference is also worth looking forward to. The winners of heavy – weight awards such as the “Top 10 AI Agent Influential Brands of 2025” and the “Model Award for Financial AI Agents” are about to be announced.

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Zhi Shu: 159 0191 1431  Alex.gan@ccglobal.com.cn

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