Author: Zhixiong Pan

When we talk about AI, the public discourse is easily diverted by topics like "parameter scale," "ranking positions," or "which new model has outperformed whom." We cannot say that these noises are meaningless, but they often act like a layer of froth, obscuring the more essential undercurrents beneath the surface: a covert war over the distribution of AI power is quietly unfolding in today's technological landscape.

If we elevate our perspective to the scale of civilizational infrastructure, we find that artificial intelligence is simultaneously presenting two radically different yet intertwined forms.

One resembles a "lighthouse" high above the coast, controlled by a few giants, pursuing the farthest illumination distance, representing the cognitive limits that humanity can currently touch.

The other resembles a "torch" held in hand, pursuing portability, privatization, and replicability, representing the baseline intelligence accessible to the public.

By understanding these two types of light, we can escape the maze of marketing jargon and clearly judge where AI will lead us, who will be illuminated, and who will be left in the dark.

Lighthouse: The Cognitive Heights Defined by SOTA

The so-called "lighthouse" refers to Frontier / SOTA (State of the Art) level models. In dimensions such as complex reasoning, multimodal understanding, long-chain planning, and scientific exploration, they represent the most powerful, highest-cost, and most centralized systems.

Institutions like OpenAI, Google, Anthropic, and xAI are typical "tower builders." What they construct is not just a series of model names, but a production method that "exchanges extreme scale for boundary breakthroughs."

Why the Lighthouse is Destined to be a Game for the Few

The training and iteration of frontier models essentially forcefully bundle together three extremely scarce resources.

First is computing power, which not only means expensive chips but also large-scale clusters, long training windows, and extremely high interconnect costs; second is data and feedback, which require massive corpus cleaning, continuously iterated preference data, complex evaluation systems, and high-intensity human feedback; finally, there is the engineering system, encompassing distributed training, fault-tolerant scheduling, inference acceleration, and the entire pipeline for transforming research results into usable products.

These elements create a very high barrier to entry; it cannot be replaced by a few geniuses writing "smarter code." It resembles a vast industrial system, capital-intensive, with a complex chain, and increasingly expensive marginal improvements.

Thus, the lighthouse inherently carries a centralized characteristic: it is often controlled by a few institutions that possess training capabilities and data loops, ultimately being used by society in the form of APIs, subscriptions, or closed products.

The Dual Significance of the Lighthouse: Breakthrough and Traction

The existence of the lighthouse is not to "make everyone write copy faster"; its value lies in two more hardcore functions.

First is the exploration of cognitive limits. When tasks approach the edge of human capability, such as generating complex scientific hypotheses, conducting interdisciplinary reasoning, multimodal perception and control, or long-term planning, what you need is the strongest beam of light. It does not guarantee absolute correctness, but it can illuminate the "feasible next step" further.

Second is the traction of technological routes. Frontier systems often pioneer new paradigms: whether it’s better alignment methods, more flexible tool invocation, or more robust reasoning frameworks and safety strategies. Even if they are later simplified, distilled, or open-sourced, the initial paths are often paved by the lighthouse. In other words, the lighthouse serves as a societal laboratory, allowing us to see "what level of intelligence can still be achieved," and forcing the entire industry chain to improve efficiency.

The Shadows of the Lighthouse: Dependence and Single Point Risks

However, the lighthouse also has obvious shadows, and these risks are often not mentioned in product launches.

The most direct issue is controlled accessibility. How much you can use and whether you can afford it entirely depends on the provider's strategy and pricing. This leads to a high dependence on the platform: when intelligence primarily exists as a cloud service, individuals and organizations effectively outsource critical capabilities to the platform.

Behind convenience lies fragility: network outages, service suspensions, policy changes, price increases, and interface alterations can instantly render your workflow ineffective.

A deeper hidden danger lies in privacy and data sovereignty. Even with compliance and commitments, the flow of data itself remains a structural risk. Especially in scenarios involving healthcare, finance, government affairs, and core corporate knowledge, "putting internal knowledge in the cloud" is often not merely a technical issue but a severe governance problem.

Moreover, as more industries delegate critical decision-making to a few model providers, systemic biases, evaluation blind spots, adversarial attacks, and even supply chain disruptions can be magnified into significant social risks. The lighthouse can illuminate the sea surface, but it is part of the coastline: it provides direction but also, invisibly, defines the shipping lanes.

Torch: The Intelligence Baseline Defined by Open Source

Pulling our gaze back from the distance, we see another source of light: an ecosystem of open-source and locally deployable models. DeepSeek, Qwen, Mistral, and others are just some of the more prominent representatives, behind which lies a new paradigm that transforms considerable intelligent capabilities from "cloud-scarce services" into "downloadable, deployable, and modifiable tools."

This is the "torch." It corresponds not to the upper limit of capability but to the baseline. This does not mean "low capability," but rather represents the intelligent benchmark that the public can unconditionally access.

The Significance of the Torch: Turning Intelligence into Assets

The core value of the torch lies in its transformation of intelligence from a rental service into owned assets, reflected in three dimensions: privatization, portability, and combinability.

Privatization means that model weights and inference capabilities can run locally, on intranets, or proprietary clouds. "I own a working intelligence" is fundamentally different from "I am renting intelligence from a company."

Portability means you can freely switch between different hardware, environments, and vendors without binding critical capabilities to a specific API.

Combinability allows you to integrate models with retrieval (RAG), fine-tuning, knowledge bases, rule engines, and permission systems, forming systems that meet your business constraints rather than being confined by the boundaries of a generic product.

This translates into very concrete scenarios in reality. Internal knowledge Q&A and process automation often require strict permissions, audits, and physical isolation; regulated industries like healthcare, government, and finance have strict "data must not leave the domain" red lines; and in manufacturing, energy, and on-site operations in weak network or offline environments, edge-side inference is a necessity.

For individuals, long-accumulated notes, emails, and private information also require a local intelligent agent to manage, rather than handing over a lifetime of data to some "free service."

The torch makes intelligence no longer just a matter of access but more like a means of production: you can build tools, processes, and safeguards around it.

Why the Torch Will Shine Brighter

The improvement of open-source model capabilities is not coincidental but comes from the confluence of two paths. One is research diffusion, where frontier papers, training techniques, and reasoning paradigms are quickly absorbed and replicated by the community; the other is the extreme optimization of engineering efficiency, with techniques like quantization (e.g., 8-bit/4-bit), distillation, inference acceleration, layered routing, and MoE (Mixture of Experts) allowing "usable intelligence" to continuously sink to cheaper hardware and lower deployment thresholds.

Thus, a very realistic trend emerges: the strongest models determine the ceiling, but "sufficiently strong" models determine the speed of popularization. The vast majority of tasks in social life do not require the "strongest"; what is needed is "reliable, controllable, and cost-stable." The torch precisely corresponds to this type of demand.

The Cost of the Torch: Security Outsourced to Users

Of course, the torch is not inherently just; its cost is the transfer of responsibility. Many risks and engineering burdens originally borne by platforms are now shifted to users.

The more open the model, the easier it is to be used for generating scam scripts, malicious code, or deep fakes. Open source does not equate to harmless; it merely decentralizes control while also decentralizing responsibility. Additionally, local deployment means you must solve a series of issues yourself, such as evaluation, monitoring, prompt injection protection, permission isolation, data de-identification, model updates, and rollback strategies.

Moreover, many so-called "open source" models are more accurately described as "open weights," still constrained in commercial scope and redistribution, which is not only a moral issue but also a compliance issue. The torch grants you freedom, but freedom is never "cost-free." It is more like a tool: it can build, but it can also harm; it can save itself, but it requires training.

The Convergence of Light: The Co-evolution of Upper Limits and Baselines

If we only view the lighthouse and the torch as a "giants vs. open source" opposition, we will miss a more authentic structure: they are two segments of the same technological river.

The lighthouse is responsible for pushing boundaries further, providing new methodologies and paradigms; the torch is responsible for compressing, engineering, and sinking these results, turning them into widely applicable productivity. This diffusion chain is already quite clear today: from papers to replication, from distillation to quantization, and then to local deployment and industry customization, ultimately achieving an overall elevation of the baseline.

The elevation of the baseline will, in turn, affect the lighthouse. When "sufficiently strong baselines" are accessible to everyone, it becomes difficult for giants to maintain monopolies based solely on "basic capabilities"; they must continue to invest resources to seek breakthroughs. Meanwhile, the open-source ecosystem will form richer evaluations, adversarial feedback, and usage feedback, which in turn promotes frontier systems to be more stable and controllable. A large number of application innovations occur within the torch ecosystem, where the lighthouse provides capability, and the torch provides soil.

Therefore, rather than viewing this as two camps, it is more accurate to see it as two institutional arrangements: one system concentrates extreme costs to achieve upper limit breakthroughs; the other disperses capabilities to achieve popularization, resilience, and sovereignty. Both are indispensable.

Without the lighthouse, technology is prone to stagnation, "only capable of cost-performance optimization"; without the torch, society is likely to fall into "dependency on a few platforms monopolizing capabilities."

The More Difficult but Crucial Part: What Are We Really Competing For

The struggle between the lighthouse and the torch superficially concerns the differences in model capabilities and open-source strategies, but in essence, it is a covert war over the distribution of AI power. This war unfolds not on a battlefield filled with smoke but across three seemingly calm yet future-defining dimensions:

First, the struggle for the definition of "default intelligence." When intelligence becomes infrastructure, the "default option" signifies power. Who provides the default? Whose values and boundaries does the default adhere to? What are the default reviews, preferences, and commercial incentives? These questions will not automatically disappear because the technology is stronger.

Second, the struggle over the burden of externalities. Training and inference consume energy and computing power, data collection involves copyright, privacy, and labor, and model outputs influence public opinion, education, and employment. Both the lighthouse and the torch create externalities, but the distribution methods differ: the lighthouse is more centralized, regulatory, but also more like a single point; the torch is more decentralized, more resilient, but harder to govern.

Third, the struggle for the position of individuals within the system. If all important tools must be "networked, logged in, paid for, and comply with platform rules," individuals' digital lives will become like renting: convenient, but never truly their own. The torch offers another possibility: allowing people to possess a portion of "offline capabilities," keeping control over privacy, knowledge, and workflows in their own hands.

A Dual-Track Strategy Will Be the Norm

In the foreseeable future, the most reasonable state will not be "fully closed source" or "fully open source," but rather a combination resembling an electrical system.

We need the lighthouse for extreme tasks, to handle scenarios requiring the strongest reasoning, cutting-edge multimodal capabilities, interdisciplinary exploration, and complex scientific assistance; we also need the torch for critical assets, to build defenses in scenarios involving privacy, compliance, core knowledge, long-term stable costs, and offline availability. Between the two, a large number of "intermediate layers" will emerge: proprietary models built by enterprises, industry models, distilled versions, and hybrid routing strategies (simple tasks handled locally, complex tasks handled in the cloud).

This is not a compromise but an engineering reality: the pursuit of breakthroughs at the upper limit, and the pursuit of popularization at the baseline; one seeks extremity, while the other seeks reliability.

Conclusion: The Lighthouse Guides the Distance, the Torch Guards the Ground

The lighthouse determines how high we can push intelligence, representing civilization's offensive in the face of the unknown.

The torch determines how broadly we can distribute intelligence, representing society's restraint in the face of power.

Applauding breakthroughs in SOTA is reasonable because it expands the boundaries of what humanity can think about; applauding the iteration of open-source and privatization is equally reasonable because it allows intelligence to belong not just to a few platforms but to become tools and assets for more people.

The true watershed of the AI era may not be "whose model is stronger," but rather whether you have a beam of light in your hand that does not need to be borrowed from anyone when the night falls.

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