A secret war over AI distribution rights begins: Lighthouse guides the way, torches compete for sovereignty

AI development is not just a competition of models, but a battle for the distribution of power between lighthouses and torches— the former pushes the limits of capability, while the latter safeguards sovereignty of intelligence, jointly shaping the future order.

When we talk about AI, public discourse is easily distracted by topics like “parameter scale,” “leaderboard rankings,” or “which new model has crushed whom.” We can’t say these noises are meaningless, but they often resemble foam on the water’s surface, obscuring the more essential undercurrents beneath: in today’s technological landscape, a secret war over AI distribution rights is quietly unfolding.

If we elevate our perspective to the scale of civilization infrastructure, we will see that artificial intelligence is simultaneously manifesting in two very different yet intertwined forms.

One resembles a lighthouse perched on a high coast, controlled by a few giants, seeking the furthest illumination—representing the current cognitive upper limit accessible to humans.

The other resembles a handheld torch, portable, private, and replicable—representing the baseline of intelligence accessible to the public.

Understanding these two lights allows us to break free from marketing fog, clearly judge where AI will take us, who will be illuminated, and who will be left in the dark.

Lighthouse: The Cognitive Height Defined by SOTA

A “lighthouse” refers to models at the Frontier / SOTA (State of the Art) level. In dimensions such as complex reasoning, multi-modal understanding, long-chain planning, and scientific exploration, they represent the most capable, most costly, and most organizationally centralized systems.

Institutions like OpenAI, Google, Anthropic, and xAI are typical “builders of towers.” They are constructing not just individual models, but a production approach that “exchanges extreme scale for boundary breakthroughs.”

Why Lighthouses Are Destined to Be a Few People’s Game

Training and iterating frontier models fundamentally involve forcibly bundling three extremely scarce resources.

First is computing power, which entails not only expensive chips but also large-scale clusters, long training cycles, and high network costs; second is data and feedback, requiring massive data cleaning, iterative preference data, complex evaluation systems, and intensive human feedback; third is engineering systems, covering distributed training, fault-tolerant scheduling, inference acceleration, and pipelines transforming research into usable products.

These elements form a very high barrier. It cannot be replaced by a few geniuses writing “smarter code”; it resembles a vast industrial system—capital-intensive, complex chains, with increasingly expensive marginal improvements.

Therefore, lighthouses naturally tend toward centralization: often controlled by a few organizations with training capabilities and data loops, ultimately used by society via APIs, subscriptions, or closed products.

The Dual Meaning of Lighthouses: Breakthroughs and Guidance

The existence of lighthouses is not just to “help everyone write copy faster.” Their value lies in two more hardcore roles.

First is exploring the upper limits of cognition. When tasks approach the edge of human ability—such as generating complex scientific hypotheses, cross-disciplinary reasoning, multi-modal perception and control, or long-term planning—you need the strongest beam. It doesn’t guarantee absolute correctness, but it can illuminate the “feasible next step” further.

Second is guiding technological directions. Frontier systems often pioneer new paradigms: better alignment methods, more flexible tool invocation, more robust reasoning architectures, and safety strategies. Even if these are later simplified, distilled, or open-sourced, the initial paths are often carved by lighthouses. In other words, a lighthouse is a societal laboratory that shows us “how far intelligence can go” and pushes the entire industry to improve efficiency.

The Shadows of Lighthouses: Dependency and Single Point Risks

But lighthouses also have obvious shadows, risks that are often not mentioned in product launches.

The most direct is accessibility control. How much you can use and whether you can afford it depends entirely on the provider’s strategy and pricing. This leads to high dependency on platforms: when intelligence mainly exists as cloud services, individuals and organizations essentially outsource key capabilities to these platforms.

Convenience comes with fragility: network outages, service interruptions, policy changes, price hikes, interface modifications—any of these can instantly disable your workflow.

Deeper risks involve privacy and data sovereignty. Even with compliance and commitments, data flow itself remains a structural risk. Especially in scenarios involving healthcare, finance, government, or core corporate knowledge, “sending internal knowledge to the cloud” is not just a technical issue but a serious governance challenge.

Furthermore, as more industries delegate critical decisions to a few model providers, systemic biases, blind spots in evaluation, adversarial attacks, and supply chain disruptions can amplify into significant societal risks. Lighthouses can illuminate the sea surface, but they belong to the shoreline: they provide direction but also implicitly define the navigable channels.

Torch: The Open-Source Baseline of Intelligence

Pulling back from the distant view, another light source appears: the open-source and locally deployable model ecosystem. DeepSeek, Qwen, Mistral, among others, are prominent examples. They represent a new paradigm—transforming powerful intelligence capabilities from “cloud scarcity services” into “downloadable, deployable, and modifiable tools.”

This is the “torch.” It does not correspond to the upper limit of capability but to the baseline. This does not mean “lower capability,” but rather the intelligence standard that the public can access unconditionally.

The Significance of Torches: Turning Intelligence into Asset

The core value of torches is transforming intelligence from a rental service into a personal asset, reflected in three dimensions: private ownership, portability, and composability.

“Private ownership” means model weights and inference capabilities can run locally, on intranets, or proprietary clouds. “I own a working piece of intelligence,” fundamentally different from “renting intelligence from a company.”

“Portability” means you can freely switch between different hardware, environments, and providers without binding critical capabilities to a single API.

“Composability” allows combining models with retrieval (RAG), fine-tuning, knowledge bases, rule engines, and permission systems, forming systems aligned with your business constraints rather than being confined within a generic product boundary.

In practice, this applies to very concrete scenarios. Internal knowledge Q&A and process automation in enterprises often require strict permissions, audits, and physical isolation; regulated industries like healthcare, government, and finance have strict “data not leaving the domain” red lines; and in manufacturing, energy, and on-site operations in weak or offline networks, edge inference is a necessity.

For individuals, long-term accumulated notes, emails, and private data also need a local intelligent agent to manage, rather than entrusting a lifetime of data to a “free service.”

Torches make intelligence no longer just access rights but a form of production data: you can build tools, workflows, and safeguards around it.

Why Torches Will Shine Brighter Over Time

The improvement of open-source model capabilities is not accidental but results from the convergence of two paths. One is research diffusion—cutting-edge papers, training techniques, and inference paradigms are quickly absorbed and reproduced by the community; the other is engineering efficiency optimization—quantization (like 8-bit / 4-bit), distillation, inference acceleration, layered routing, and MoE (Mixture of Experts) techniques enable “usable intelligence” to be deployed on cheaper hardware and lower barriers.

A very realistic trend has emerged: the strongest models set the ceiling, but “sufficiently strong” models determine the pace of adoption. Most tasks in society do not require “the strongest,” but “reliable, controllable, and cost-stable” solutions. Torches precisely meet these needs.

The Cost of Torches: Security Outsourced to Users

Of course, torches are not inherently just. Their cost is the transfer of responsibility. Many risks and engineering burdens previously borne by platforms are now shifted to users.

The more open the model, the easier it is to generate scams, malicious code, or deepfakes. Open source does not mean harmless; it simply decentralizes control, but also responsibility. Moreover, local deployment means you must handle evaluation, monitoring, prompt injection defenses, permission isolation, data anonymization, model updates, and rollback strategies yourself.

Many so-called “open source” models are more accurately “open weights,” with restrictions on commercial use and redistribution—this is not only a moral issue but also a compliance concern. Torches give you freedom, but freedom is never “zero cost.” It’s more like a tool: capable of building, but also capable of harming; capable of self-rescue, but requiring training.

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

If we only see lighthouses and torches as “giants vs open source,” we miss a deeper structure: they are two segments of the same technological river.

Lighthouses push boundaries outward, offering new methodologies and paradigms; torches compress, engineer, and sink these results into accessible productivity. This diffusion chain is now very clear: from papers to reproduction, from distillation to quantization, to local deployment and industry customization, ultimately raising the overall baseline.

And the rising baseline, in turn, influences the lighthouse. When “sufficiently strong baseline” models are accessible to all, giants find it hard to maintain monopolies solely through “fundamental capabilities” and must continue investing in breakthroughs. Simultaneously, the open ecosystem fosters richer evaluation, adversarial testing, and user feedback, which in turn drives frontier systems to become more stable and controllable. Many application innovations occur within the torch ecosystem, with lighthouses providing capabilities and torches providing the soil.

Thus, rather than two opposing camps, these are two institutional arrangements: one concentrates extreme costs to achieve upper limits; the other disperses capabilities to promote ubiquity, resilience, and sovereignty. Both are indispensable.

Without lighthouses, technology risks stagnating in “cost-performance optimization”; without torches, society risks dependence on a few platforms monopolizing capabilities.

The More Critical Question: What Are We Really Fighting For?

The battle between lighthouses and torches, on the surface about model capabilities and open-source strategies, is actually a secret war over AI distribution rights. This war is not fought on smoky battlegrounds but unfolds across three seemingly calm yet decisive dimensions:

1. The right to define “default intelligence.” When intelligence becomes infrastructure, “default options” imply power. Who provides the default? Whose values and boundaries does it follow? What are the default censorship, preferences, and commercial incentives? These questions do not automatically disappear with stronger technology.
2. The way externalities are borne. Training and inference consume energy and compute; data collection involves copyright, privacy, and labor; model outputs influence public opinion, education, and employment. Both lighthouses and torches generate externalities, but their distribution differs: lighthouses are more centralized and regulated, akin to single points; torches are more dispersed, resilient, but harder to govern.
3. The position of individuals within the system. If all critical tools require “online, login, paid, platform-compliant” access, personal digital life becomes like renting a house: convenient but never truly owned. Torches offer an alternative: enabling individuals to possess some “offline capabilities,” keeping control over privacy, knowledge, and workflows.

Dual-Track Strategies Will Become the Norm

In the foreseeable future, the most reasonable state is not “full closed-source” or “full open-source,” but a hybrid similar to power systems.

Lighthouses are needed for extreme tasks—handling scenarios requiring the strongest reasoning, cutting-edge multi-modality, cross-industry exploration, and complex scientific assistance; torches are needed for key assets—building defenses in scenarios involving privacy, compliance, core knowledge, long-term stable costs, and offline availability. Between the two, many “middle layers” will emerge: enterprise proprietary models, industry-specific models, distilled versions, and hybrid routing strategies (simple tasks on local, complex tasks on cloud).

This is not compromise but engineering reality: pursuit of upper limits for breakthroughs, pursuit of baselines for ubiquity; one seeks extremity, the other reliability.

Conclusion: Lighthouses Guide the Distance, Torches Guard the Footing

Lighthouses determine how high we can push intelligence—an offensive move by civilization into the unknown.

Torches determine how broadly we can distribute intelligence— a self-sustaining act in the face of power.

Applauding breakthroughs in SOTA is justified because they expand the boundaries of human thought; equally justified is applauding iterations of open-source and privatization, because they make intelligence not just belong to a few platforms but tools and assets for more people.

The true watershed of the AI era may not be “whose model is stronger,” but whether, when night falls, you hold a light that needs no borrowing from anyone.

• This article is reprinted with permission from：《Deep Tide TechFlow》
• Original title: 《Lighthouse Guides the Way, Torch Competes for Sovereignty: A Secret War over AI Distribution Rights》
• Original author: Pan Zhixiong