Lighthouses guide the way, torches vie for sovereignty: a hidden war over the distribution of AI.

Author: Zhixiong Pan

When we talk about AI, the public discourse is easily swept up by topics like "parameter scale," "rankings," or "which new model has crushed whom." We can't say that this noise is meaningless, but it often acts like a layer of foam, obscuring the more fundamental undercurrents beneath the surface: in today's technological landscape, a covert war for the allocation of AI is quietly taking place.

If we broaden our perspective to the scale of civilization's infrastructure, we will find that artificial intelligence is simultaneously exhibiting two distinct yet intertwined forms.

Like a "lighthouse" hanging high on the coast, it is controlled by a few giants, pursuing the farthest illumination distance, representing the upper limit of human cognition that we can currently reach.

Another type is like a "torch" held in one's hand, which seeks to be portable, private, and replicable, representing the baseline of intelligence that the public can access.

Only by understanding these two kinds of light can we break free from the illusion of marketing rhetoric and clearly judge where AI will lead us, who will be illuminated, and who will be left in the shadows.

Lighthouse: The Cognitive Height Defined by SOTA

The term "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 capable, costly, and centrally organized types of systems.

OpenAI, Google, Anthropic, xAI, and other organizations are typical "tower builders." What they build is not just model names, but a production method that "trades extreme scale for breaking boundaries."

Why is the lighthouse destined to be a game for the few?

The training and iteration of cutting-edge models essentially involves forcibly binding together three extremely scarce resources.

First is computing power, which means not only expensive chips, but also clusters with tens of thousands of chips, long training windows, and extremely high interconnection costs. Second is data and feedback, which requires cleaning massive amounts of corpus data, as well as constantly iterating preference data, complex evaluation systems, and intensive human feedback. Finally, there is the engineering system, which covers the entire pipeline of distributed training, fault-tolerant scheduling, inference acceleration, and transforming research results into usable products.

These elements constitute an extremely high barrier to entry, which cannot be replaced by a few geniuses writing "smarter code." It is more like a massive industrial system, capital-intensive, with complex chains, and increasingly expensive marginal improvements.

Therefore, lighthouses are inherently centralized: training capabilities and data loops are often controlled by a few institutions, and they are ultimately used by society in the form of APIs, subscriptions, or closed products.

The dual significance of a lighthouse: breakthrough and guidance

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

First, there's the exploration of the limits of cognition. When tasks approach the edge of human capability—such as generating complex scientific hypotheses, engaging in interdisciplinary reasoning, multimodal perception and control, or long-term planning—you need the strongest beam of light. It doesn't guarantee absolute correctness, but it can illuminate the "feasible next step" further.

Secondly, there's the pull of technological pathways. Cutting-edge systems often pioneer new paradigms: whether it's better alignment, more flexible tool usage, or more robust inference frameworks and security strategies. Even if these are later simplified, distilled, or open-sourced, the initial path is often paved by the lighthouse. In other words, the lighthouse is a societal laboratory, showing us "how far intelligence can go" and driving efficiency improvements across the entire industry chain.

The Shadow of the Lighthouse: Dependence and Single Point of Risk

But even a beacon has its shadows, and these risks are often not mentioned at product launches.

The most direct consequence is controlled accessibility. The extent to which you can use it, and whether you can afford it, depends entirely on the provider's strategy and pricing. This leads to a high degree of dependence on platforms: when intelligence primarily exists as a cloud service, individuals and organizations are effectively outsourcing key capabilities to these platforms.

Convenience masks vulnerability: network outages, service interruptions, policy changes, price increases, and interface modifications can all instantly render your workflow unusable.

A deeper concern lies in privacy and data sovereignty. Even with compliance and commitments, data flow itself remains a structural risk. Especially in healthcare, finance, government affairs, and scenarios involving core corporate knowledge, "sending internal knowledge to the cloud" is often not just a simple technical issue, but a serious governance problem.

Furthermore, as more and more industries entrust key decision-making processes to a few model providers, systemic biases, evaluation blind spots, adversarial attacks, and even supply chain disruptions will be amplified into enormous social risks. A lighthouse illuminates the sea, but it is part of the coastline: it provides direction, but also implicitly defines the shipping lanes.

Torch: The Intelligent Bottom Line Defined by Open Source

Turning our gaze back to the distant horizon, we see another source of light: an ecosystem of open-source and locally deployable models. DeepSeek, Qwen, and Mistral are just some of the more prominent examples, representing a completely new paradigm that transforms powerful intelligent capabilities from "scarce cloud services" into "downloadable, deployable, and modifiable tools."

This is the "torch." It doesn't correspond to the upper limit of ability, but rather to the baseline. This doesn't represent "low ability," but rather a benchmark of intelligence that the public can unconditionally access.

The significance of the torch: turning intelligence into an asset

The core value of the torch lies in its transformation of intelligence from a rental service into a owned asset, which is reflected in three dimensions: privatability, portability, and composability.

The term "private" means that model weights and inference capabilities can run locally, on an intranet, or in a private cloud. "I own a working piece of intelligence" is fundamentally different from "I'm renting intelligence from a company."

The term "portable" means that you can freely switch between different hardware, different environments, and different vendors without having to tie key capabilities to a single API.

Composability, on the other hand, allows you to combine models with Retrieval and Optimization (RAG), fine-tuning, knowledge bases, rule engines, and permission systems to form a system that meets your business constraints, rather than being confined by the boundaries of a general-purpose product.

This translates into very specific scenarios in reality. Knowledge-based Q&A and process automation within enterprises often require strict permissions, auditing, and physical isolation; regulated industries such as healthcare, government, and finance have strict "data not leaving the domain" red lines; and in weak network or offline environments such as manufacturing, energy, and on-site maintenance, edge inference is an essential requirement.

For individuals, the notes, emails, and private information accumulated over a long period of time also need to be managed by a local intelligent agent, rather than handing over a lifetime's worth of data to a "free service".

The torch makes intelligence more than just access rights; it becomes a means of production: you can build tools, processes, and safeguards around it.

Why does the torch get brighter and brighter?

The improvement in the capabilities of open-source models is not accidental, but rather the result of the convergence of two paths. The first is research diffusion, where cutting-edge papers, training techniques, and inference paradigms are quickly absorbed and reproduced by the community. The second is the extreme optimization of engineering efficiency, with technologies such as quantization (e.g., 8-bit/4-bit), distillation, inference acceleration, hierarchical routing, and MoE (hybrid experts) allowing "usable intelligence" to continuously be deployed to cheaper hardware and with lower deployment barriers.

This leads to a very real trend: the strongest model determines the ceiling, but a "sufficiently strong" model determines the speed of adoption. Most tasks in social life don't require the "strongest" model; what's needed is "reliability, controllability, and stable cost." The torch perfectly addresses these needs.

The cost of torches: security outsourced to users

Of course, the torch is not inherently righteous; its cost is the transfer of responsibility. Many risks and engineering burdens that were originally borne by the platform are now transferred to the users.

The more open a model is, the easier it is to be used to generate fraudulent scripts, malicious code, or deepfakes. Open source does not equal harmless; it merely delegates control, but also responsibility. Furthermore, local deployment means you have to handle a whole host of issues yourself, including evaluation, monitoring, injection protection, access control, data anonymization, and model update and rollback strategies.

Even many so-called "open source" projects, which are more accurately described as "open weights," still face constraints in terms of commercial use and redistribution. This is not only a moral issue but also a compliance one. A torch grants you freedom, but freedom is never "cost-free." It's more like a tool: it can build, but it can also harm; it can save you, but it also requires training.

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

If we only see the lighthouse and the torch as an opposition between "giants vs. open source", we will miss the more real structure: they are two parts of the same technological river.

The lighthouse pushes boundaries, providing new methodologies and paradigms; the torch compresses, engineers, and disseminates these achievements, transforming them into widely applicable productivity. This diffusion chain is now quite clear: from academic papers to reproduction, from distillation to quantification, and then to local deployment and industry customization, ultimately achieving an overall elevation of the baseline.

The rise of the baseline, in turn, affects the Lighthouse. When a "sufficiently strong baseline" is available to everyone, it becomes difficult for giants to maintain their monopoly in the long run by relying on "basic capabilities," and they must continue to invest resources to seek breakthroughs. At the same time, the open-source ecosystem will generate richer evaluation, adversarial, and usage feedback, which in turn will drive the cutting-edge system to be more stable and controllable. A large number of application innovations occur in the Torch ecosystem, with the Lighthouse providing the capabilities and the Torch providing the fertile ground.

Therefore, rather than viewing these as two camps, it's more accurate to describe them as two institutional arrangements: one system concentrates extreme costs to achieve breakthroughs in limits; the other system disperses capabilities to achieve widespread adoption, resilience, and sovereignty. Both are indispensable.

Without a guiding light, technology is prone to stagnation, focusing solely on cost-effectiveness optimization; without a torch, society is prone to dependence on a few platforms that monopolize capabilities.

The more difficult but crucial part: What exactly are we fighting for?

The debate between the lighthouse and the torch, ostensibly about differences in model capabilities and open-source strategies, is in reality a covert war over the allocation of AI. This war doesn't unfold on the battlefield, but rather in three seemingly calm yet future-determining dimensions:

First, there's the struggle for the right to define "default intelligence." When intelligence becomes infrastructure, "default options" equate to power. Who provides the defaults? Whose values and boundaries do the defaults follow? What are the default censorship, preferences, and business incentives? These questions won't automatically disappear just because technology becomes more advanced.

Second, the struggle over how to bear externalities. Training and inference consume energy and computing power, data collection involves copyright, privacy, and labor, and model output affects public opinion, education, and employment. Both lighthouses and torches create externalities, but they are distributed in different ways: lighthouses are more centralized and can be regulated, but they are more like single points; torches are more dispersed, more resilient, but more difficult to govern.

Third, the struggle for an individual's place within the system. If all essential tools require "internet access, login, payment, and adherence to platform rules," an individual's digital life becomes like renting an apartment: convenient, but never truly their own. Torch offers another possibility: granting individuals some "offline capabilities," allowing them to retain control over their privacy, knowledge, and workflows.

A dual-track strategy will be the norm.

In the foreseeable future, the most reasonable state is not "fully closed source" or "fully open source", but a combination more like a power system.

We need lighthouses for extreme missions, handling scenarios requiring the strongest reasoning, cutting-edge multimodal and cross-domain exploration, and complex scientific research support; we also need torches for critical assets, building defenses in scenarios involving privacy, compliance, core knowledge, long-term stable costs, and offline availability. Between these two, numerous "middle layers" will emerge: proprietary models built by enterprises, industry models, distilled versions, and hybrid routing strategies (simple tasks run locally, complex tasks run in the cloud).

This is not compromise, but engineering reality: the upper limit aims for breakthroughs, while the baseline aims for widespread adoption; one pursues excellence, while the other pursues reliability.

Conclusion: Lighthouses guide us to distant places, torches guard our feet.

The lighthouse determines how high we can push intelligence; it is civilization's attack on the unknown.

The torch determines how widely we can distribute intelligence; it reflects society's self-restraint in the face of power.

It is reasonable to applaud the breakthroughs of SOTA, because it expands the boundaries of what humans can think about; it is equally reasonable to applaud open source and privatizable iterations, because it makes intelligence not just belong to a few platforms, but become a tool and asset for more people.

The real watershed moment in the AI era may not be "whose model is stronger," but rather whether you have a ray of light in your hand that you don't have to borrow from anyone when darkness falls.