As we delve into the ongoing evolution of industry, it becomes apparent that the release of ChatGPT marked a significant turning point, a key milestone in the progression of artificial intelligence and its applicationsIt's important to ascertain that the arrival of O3, the subsequent development that followed, represents a new chapter in this technological narrativeThe crucial aspect of this transition lies not merely in the capability of the models themselves but in understanding how to unleash their power effectively and productively.

The intrigue of this progression stems from a fundamental shift in focusBefore O3, discussions primarily revolved around the question of whether the models were functionalPost-O3, however, the critical inquiry evolves into understanding the methodologies that can maximize the potential of such advanced modelsIt would be a superficial analysis to restrict our viewpoints to mere technical aspects like crafting effective prompts; such a narrow focusing undermines the true essence of AI, which promises to deliver widespread and generalized intelligence.

This shift in focus compels us to explore more expansive dimensions of application pathways

For instance, recent occurrences, such as the 'carrot chase' phenomenon illustrates that such hurdles do not arise purely from technical or product-related issues; rather, they can significantly impede the process of product realizationEach deployment scenario might find itself confronting micro-level dilemmas akin to the overarching carrot chase issue, underscoring how the application landscape can be riddled with challenges.

In navigating these application pathways, what factors play a pivotal role? The interplay between AI and human involvement emerges as a fundamental concernThis relationship serves as the cornerstone for shaping diverse applications amidst the impending advancements in AI technology.

During the AI Collision conference on December 29, 2024, Professor Hou Hong from Peking University's National Development Institute presented a thought-provoking framework that prompts contemplation

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Although initially elusive to grasp, at its core, this framework addresses how to delineate the roles of humans versus AI in the process of knowledge creation.

It is this focus on the creation and circulation of knowledge that underscores the significance of this query, for it ultimately becomes one of the decisive factors shaping application boundariesThe blurred lines between the roles of humans and machines will significantly influence how we define the capabilities and limitations of our applicationsThe horizontal segmentation of the human-machine dynamic, when viewed in conjunction with a vertical exploration of data accessibility, establishes the potential framework for AI applications.

Thus, the delineation between human and machine roles not only determines the form and depth of applications but also dictates the manner in which intelligence is provided to individuals and organizations—whether it manifests as a Copilot or an Autopilot experience.

Equally important is the issue of data accessibility

As previously mentioned, the accessibility of data is fundamentally linked to the functional capabilities of applicationsTwo critical aspects warrant consideration: first, the compatibility of interests among stakeholders is vital for ensuring a continuous flow of dataWithout this seamless access to data, improved products cannot materialize, leaving only the proverbial low-hanging fruit in terms of developmentTake, for instance, a proposal for a legal framework intended to serve a courthouse; clearly, the challenge arises not from technological capabilities but from the need to reconstruct the production relationships surrounding data sharing and availability.

The second factor is the inherent costs associated with dataSustaining high-precision data in the long run requires investment and resourcesA failure to secure accurate data could result in the AI system being surrounded by disinformation, rendering it ineffective—a scenario that ultimately hampers product viability

In the context of product development, these aspects are strategic concerns, fundamentally pre-determining the marginal costs at which products can expand.

When we categorize AI applications into two primary classes—those that create new efficiencies and those that enhance user experiences—the earlier mentioned issues become crucial for the former.

Another substantial concern arises regarding the universality of data pathwaysAI's large language models are characterized by their core capability for generalizationIf we liken AI models to a brain capable of discerning various tasks, the expectation is that it can skilfully gather a range of inputsHowever, if, in practice, the model is limited only to execute a singular task, akin to an octopus equipped with only one type of tentacle, its potential will remain narrowly confined.

Consequently, the core of the issue is not about the model's capacity but rather the breadth of the feedback mechanisms in place—a discrepancy that encapsulates a core deviation from previous applications.

Provided that data channels are functioning optimally, AI can feasibly offer any given service

Here, the focus shifts from functionality to the systemic implications of AI application.

AI application must adopt a systems-oriented approach, for the data boundaries inherently define the application landscapeAttempts to narrow the focus to niche areas are unlikely to yield sustainable success, as such smaller-scale applications lack the necessary barrier to entryThey will either be assimilated by larger models or overshadowed by similar applications, resulting in a fleeting opportunity for successMuch like the competition witnessed in search engines or communication platforms like WeChat, the dominant solutions invariably secure an overwhelming majority of the market share.

Should the universality of data pathways become a focal point, the resultant applications will likely transition toward systemic developmentThis occurs because varying sources of demand necessitate diverse and adaptable capabilities

Such conditions inevitably lead to components analogous to the hardware abstraction layer (HAL) seen in past iterations of technology stacks, along with the emergence of skill stores where dynamic exchanges of value can occur—things like schedulers acting as kernels to streamline systems.

A significant factor that will distinguish future AI applications is their inherent adaptive abilities, especially compared to the manual processes characterized by prior modelsEvery application in the coming landscape will require a built-in capacity for self-adaptation.

When a sufficient volume of data is amassed from varied contexts, it can catalyze two evolutionary paths: one that leans towards an end-to-end model where the application functions seamlessly integrates with the model, and feedback assists in refining the system continually, while the other mode employs more extensive data and longer analysis periods to formulate superior solutions, leveraging the power supplied by O3.

In this context, the inner workings of the system will delineate responsibilities into two segments: one dedicated to rapid processing of responses and the other to iterative learning and enhancement of long-term effectiveness