Edge AI: The Revolution Remaking AIoT and the Future of Smart Devices

Since the end of last year, we’ve witnessed a surge in on-device AI applications across a growing number of scenarios. Edge AI compresses the traditional AIoT loop of “sense-communicate-decide-act” into a single terminal device, granting AIoT “on-site decision-making power” for the first time. This represents an unprecedented decentralization of decision-making in the industry.

As we move from the “connectivity” of IoT to the “intelligence” of AIoT, the rise of on-device AI allows terminal nodes to finally reap the benefits of AI technology, elevating the entire industry to a new level of intelligence. While it’s easy to attribute the rise of Edge AI to solving the three major pain points of “latency, privacy, and bandwidth,” this overlooks a more fundamental issue: only when the benefits of AI technology fully permeate to the edge and terminal nodes can AIoT move beyond the simple additive logic of “IoT + Cloud AI” and give rise to a new industrial paradigm.

Edge AI is not just a simple technological upgrade for the AIoT industry; it’s a complete restructuring of its industrial logic and a redistribution of its value chain. The implementation of on-device AI will upgrade AIoT from a linear process of “data transmission -> cloud-based decision -> command issuance” to a closed-loop system of “on-site perception -> real-time decision -> intelligent service.” This shift will trigger a restructuring of device forms, business models, industrial division of labor, and value distribution.

The Four Transformations Driven by Edge AI

• Device Form Restructuring: From “sensing + communication” devices to “autonomous decision-making” intelligent device roles.
• Business Model Restructuring: From “selling hardware + cloud platform” to a “smart service subscription” model defined by scenarios.
• Industrial Division of Labor Restructuring: From a “chip-module-terminal-cloud” chain-like division of labor to a “cloud-edge collaboration, hardware-software integration” networked ecosystem.
• Value Distribution Restructuring: The profit center is shifting from the cloud to the edge and terminal sides.

The AIoT industry is at a new juncture, driven by AI technology, and on-device AI is the key to unlocking AIoT’s potential and completing its transformation. This article will delve into these four restructurings, exploring the real depths of the AIoT industry’s transformation in the era of Edge AI.

Hardware Role Restructuring: The Logic Behind Decentralized Intelligent Decision-Making

The core of IoT can be understood as connectivity—solving the underlying communication and transmission problems of connecting everything. Through technologies like sensors, RFID, and communication modules, it connects physical devices, environments, and data to the network, achieving a basic loop from perception to communication. However, at this stage, the Internet of Things is merely a “data pipeline,” with its value limited to “connection + command transmission.” Numerous terminal devices are defined here as “single data collection nodes.”

AIoT represents an “intelligent upgrade” to traditional IoT. In the previous upgrade phase, the introduction of cloud-based AI technology gave the Internet of Things the ability to interpret data, analyzing the massive amounts of data uploaded by IoT devices to generate decisions. However, this stage of AIoT relies on cloud computing power, resulting in poor real-time performance, high bandwidth costs, and significant privacy risks. The terminal device form remains fixed in the role of a “data collection node,” following a simple additive logic of “collection node + IoT transmission base + cloud AI.”

The advent of on-device AI frees terminal devices like sensors and actuators from being confined to the role of data entry points. These IoT hardware devices are continuously upgraded to support the operation of on-device AI models. The processing power brought by computing upgrades and the decentralized intelligent decision-making granted by on-device models transform these once single-purpose IoT devices from “passive observers” to “active decision-makers.” The form of the device finally changes at this stage, undergoing a restructuring. This restructuring of device form and meaning allows the value of data to be continuously mined at the edge. In conjunction with models, AIoT achieves intelligence from the perception stage onwards, a positioning and significance completely different from before.

Whether it’s the algorithmic enhancement after model deployment or the leap in local processing capabilities, the breakthrough in computing power has indeed created a new landscape. This raises our first question: In the era of on-device AI, why is the breakthrough point not “computing power,” or rather, not just computing power?

I believe the core demand of on-device AI is to “accurately complete specific tasks,” using the right amount of computing power and the right amount of power consumption to perform specific functions in the most cost-effective way, rather than pursuing general-purpose computing power. The same is true for AIoT devices. On the surface, the TOPS of chips doubles year after year, but in reality, the sensitivity of AIoT devices to power consumption is increasing exponentially—a difference of 1 mA can make a world of difference in battery life. The breakthrough in the power-to-compute ratio is the real driving force behind the restructuring of device forms.

The Power-to-Compute Ratio: The True Driver of Device Evolution

Most AIoT scenarios, represented by on-device AI, are constrained by both physical laws and the rigid demands of the scenario. Maximizing effective intelligence under the constraints of energy is the goal of device form restructuring. When battery capacity, heat dissipation space, and regulatory safety limits are all maxed out, the power-to-compute ratio is the only degree of freedom that can be optimized. Therefore, the energy efficiency budget is the real hard budget, and computing power is just a disposable variable.

The restructuring of device form and positioning means that whoever can squeeze out a little more advantage on this energy efficiency curve gains the priority to “define the scenario’s devices” and can also lock in the future device form in advance. It can be said that the “power-to-compute ratio” is both a technical parameter and a prerequisite for the power of terminal intelligence.

Business Model Restructuring: A New Logic for Profitability

With the changes in AIoT carriers and the diversification of service functions empowered by models, on-device devices deeply integrated with application scenarios can provide long-term customized and continuous intelligent services with the help of vertical models. In the past, companies relied on one-time sales of sensing and communication equipment, plus cloud platform services, for profit. This is now changing to a “smart service subscription” model defined by scenarios.

This brings us to our second question: What kind of profitability logic has the business model restructuring actually created?

On-device AI allows AI to sink to the terminal, giving algorithms a “priceable” nature within the terminal. The “hardware premium” in the traditional profit model of the terminal is no longer the main means of profitability. As stated in “Redefining the ‘Terminal’: Why is On-Device AI Hardware the Second Battlefield After Large Models?”, if large models are the “brains” of the new generation of intelligence, then hardware is their “body” and “interface.” Whoever controls the user’s entry point controls the initiative in data, feedback, interaction, and ecosystem construction. On-device AI hardware stands at the intersection of technological evolution and the restructuring of human-machine relationships, becoming the “new entry point” of the AI industry chain, the “new starting point” of the data cycle, and the “physical anchor” of the platform ecosystem.

With the development of on-device AI, AIoT is gradually shifting towards an era where scenarios define hardware. Hardware may be sold at close to cost, or even given away with a subscription service, with the focus of profitability shifting to “priceable” intelligent services. The vast majority of users will also be more willing to pay for the “results” of enjoying intelligent services rather than the “materials” of the AIoT scenario.

The “smart service subscription” model defined by scenarios is continuous. This restructuring transforms a traditional one-time transaction into a long-tail revenue stream, continuously compounding returns through technology. At the same time, personalized data assets, under the premise of privacy and security compliance, can also make the user ecosystem barriers more solid.

Industrial Division of Labor and Value Reorganization: The Turning Point in the AIoT Revolution

In the past, the AIoT industry chain was a relatively one-way value transfer chain. Each link in the industry chain only needed to be responsible for its downstream, with clear boundaries and each performing its own duties. After the rise of on-device AI, scenario data is closed-looped in real-time on the device side, and algorithms must evolve in synergy with hardware. As a result, the originally vertical industry chain is pulled into a “cloud-edge collaboration, hardware-software integration” networked ecosystem.

This leads to our third question: From a chain-like to a networked division of labor, where are the resistance and breakthrough points?

In a chain-like division of labor, each link in the industry chain has a relatively fixed role, and its profit distribution is also relatively solidified according to its position in the industry chain. In a networked ecosystem, the increased value of the edge and terminal sides has impacted the original structure. The resistance comes precisely from the cost of renegotiating the re-division of roles and the redistribution of benefits during the transition from the old structure to the new one.

Traditional companies in the industry chain are mostly specialized. For example, module manufacturers are good at hardware integration, and cloud vendors are good at computing power scheduling. However, a networked ecosystem requires companies to have “hardware-software collaboration” capabilities. Module manufacturers need to deploy AI model optimization and engineering design, chip manufacturers need to provide reference designs for scenario requirements, and terminal manufacturers need to define specific model functions and hardware standards based on the implementation plan. This collaboration is an inevitable trend. In the new stage of AIoT, when intelligent decentralized decision-making becomes the core, data, computing power, and algorithms must be synergistically optimized within the same iteration cycle. Any lag at any level will lengthen the application deployment cycle, leading to a loss of competitiveness for the roles in the industry chain.

The source of resistance is also the breakthrough point that the upstream and downstream of the industry chain need to anchor. Since the industrial division of labor is shifting from “fixed distribution according to upstream and downstream” to “collaboration around scenario applications,” whoever can couple the four elements of on-device AI—“chip, module, terminal, and intelligence”—into the smallest and most efficient iteration unit will be able to take the initiative in this negotiation.

Value Distribution Restructuring: Who Benefits in the New Ecosystem?

The three restructuring directions mentioned above make the path of value distribution restructuring clear. Driven by on-device AI, the value center of the AIoT industry is shifting from the cloud to the edge and terminal sides. This brings us to our fourth question: As value distribution shifts to the edge, who will become the “beneficiaries” of the new ecosystem at this stage?

Before the rise of on-device AI, the value creation of AIoT relied on “cloud computing power + centralized data processing.” Hardware did not have the dominant power, and value distribution was skewed towards the cloud. The breakthrough of on-device AI has made hardware the physical entry point of the intelligent ecosystem and the physical carrier of intelligent agents. The first to be affected, AI hardware, is becoming the front line of the next decisive battle connecting algorithms and people, and linking models and ecosystems, and is the core of the competition among all parties. Whoever masters the right to define hardware can lock in the high-value data entry points in subdivided scenarios, and the hardware definer becomes the primary beneficiary after the value shift.

Vertical model capability vendors that are subdivided into specific scenarios and specific applications will also gain the favor of the new ecosystem. With their exclusive scenario-based intelligent packaging capabilities, industry know-how is transformed into lightweight AI models. At the same time, the high-quality data exclusive to the scenario further enhances the value of these models, which not only solves the problem of overcapacity of general-purpose large models in terminal scenarios but also makes up for the lack of precision of general-purpose models in scenarios. The window of opportunity for intelligent capability suppliers in vertical fields in the new ecosystem is also quite clear.

Conclusion

The disruption brought by on-device AI is far more than just making terminal devices smarter. It decentralizes intelligent decision-making, allowing intelligence to be private, quantifiable, and priceable. Under this disruptive change, traditional AIoT has finally broken out of the simple additive logic of “IoT + Cloud AI” and given rise to a new paradigm of intelligent applications. And in this process of change, the deep waters of the restructuring of device forms, business models, industrial division of labor, and value distribution are also the key turning points in the reshaping of the industry’s logic.