The torch ignites intelligent democratization: Who will hold the true power in the AI era

When we talk about AI, public scenarios are often overshadowed by surface-level topics like “whose model is stronger” and “parameter scale rankings,” even turning into marketing battlegrounds for major tech companies. But if we shift our focus to deeper structures, you’ll find that a true power struggle is unfolding — this is not just a technological competition, but a long-term game over AI distribution rights, intelligent sovereignty, and societal resilience.

Currently, the AI ecosystem is presenting two radically different forms: one is the cutting-edge models controlled by a few giants, representing the cognitive frontier; the other is the increasingly mature open-source and local deployment ecosystem, representing accessible intelligent baselines. The former is like a lighthouse high on the coast, the latter like a torch in your hand. Understanding the fundamental differences between these two lights is essential to see how AI will reshape power structures.

The Lighthouse Guiding the Way: Power Concentration in Cutting-Edge Models

State of the Art (SOTA) level models often represent the upper limit of intelligence capabilities. Organizations like OpenAI, Google, Anthropic, and xAI invest extreme amounts of resources to achieve leadership in complex reasoning, multimodal understanding, long-term planning, and scientific exploration. This process may seem like a technical competition, but at its core, it is a resource monopoly.

Training these frontier models requires the bundling of three extremely scarce resources. First is massive computational power — not only the latest chips but also large clusters with thousands of GPUs, long training cycles, and high network costs; second is high-quality data and feedback mechanisms — vast amounts of cleaned corpora, iterative preference data, complex evaluation systems, and intensive human feedback; third is engineering systems — from distributed training and fault-tolerant scheduling to inference acceleration and pipelines that turn research results into usable products.

These elements form a high barrier to participation. It cannot be overcome by a few genius code ideas but requires a vast industrial system: capital-intensive, with complex supply chains, and increasing marginal costs for improvements. Therefore, the lighthouse naturally exhibits centralized features — often controlled by a few institutions that master training capabilities and complete data loops, ultimately providing access via APIs, subscriptions, or closed products.

The value of the lighthouse is real: it explores the cognitive frontier. When tasks approach human capability limits (such as generating complex scientific hypotheses, interdisciplinary reasoning, long-term planning), frontier models can see “further ahead” in the “feasible next step.” At the same time, it acts as a pioneer in technological pathways, with new alignment methods, tool invocation frameworks, and robust reasoning strategies often first tested here, then simplified, distilled, and open-sourced. The lighthouse is a social laboratory, pushing the entire industry chain to improve efficiency.

But it also brings obvious risks. Controlled accessibility means what level of use and affordability is entirely determined by the provider. Disconnection, service shutdowns, policy changes, and price adjustments can instantly disrupt workflows. A deeper hidden risk is privacy and data sovereignty — data flow itself is a structural risk, especially in sensitive scenarios like healthcare, finance, government, and core corporate knowledge. “Uploading internal knowledge to the cloud” is not just a technical issue but a serious governance challenge. As more critical decisions are handed over to a few model providers, systemic biases, blind spots in evaluation, and supply chain disruptions can magnify into significant societal risks.

The Torch Illuminating the Path: Democratization of Open-Source Models

Pulling back from the distant view, you see another rising light — the open-source and local deployment model ecosystem. Represented by DeepSeek, Qwen, Mistral, and others, this paradigm transforms powerful intelligence from “scarce cloud services” into “downloadable, deployable, and modifiable tools.”

The core value of the torch is transforming intelligence from a rental service into a proprietary asset, reflected in three dimensions:

Privatization means model weights and inference capabilities can run locally, on internal networks, or on private clouds. Owning a functional AI is fundamentally different from renting one — the former signifies sovereignty, the latter dependence.

Transferability allows free switching between different hardware, environments, and vendors, without binding critical capabilities to a single API. For enterprises and organizations, this means strategic autonomy.

Composability enables users to combine models with retrieval-augmented generation (RAG), fine-tuning, knowledge bases, rule engines, and permission systems, forming systems aligned with business constraints rather than being confined within generic product boundaries.

These features meet real-world needs. Internal enterprise knowledge Q&A and process automation require strict permissions, auditing, and physical isolation; regulated industries like healthcare, government, and finance have “data must not leave the domain” red lines; manufacturing, energy, and on-site operations in weak or offline networks are essential; long-term personal notes, emails, and private information also need local intelligent agents rather than “free cloud services.” The torch makes intelligence a productive asset, around which tools, workflows, and safeguards are built.

The continuous improvement of torch capabilities stems from two converging paths. One is research dissemination — cutting-edge papers, training techniques, and inference paradigms are quickly absorbed and reproduced by the community. The other is engineering efficiency optimization — techniques like quantization (8-bit/4-bit), distillation, inference acceleration, layered routing, and Mixture of Experts (MoE) enable “usable intelligence” to sink into cheaper hardware and lower deployment barriers.

The trend is clear: the strongest models set the capability ceiling, but models that are “sufficiently strong” determine the speed of adoption. Most societal tasks do not require “the strongest” but rather “reliable, controllable, and cost-stable” solutions. The torch precisely addresses these needs. It does not imply lower capability but signifies an intelligence baseline accessible to the public without conditions.

However, the torch also has costs — responsibility transfer. Risks and engineering burdens originally borne by platforms now shift to users. The more open the model, the easier it is to be used for scams, malicious code, or deepfakes. Open source decentralizes control but also shifts safety responsibilities downward. Local deployment means solving issues like evaluation, monitoring, prompt injection defenses, permission isolation, data anonymization, and update strategies oneself. The torch grants freedom, but freedom is not costless — it’s more like a tool that can be used to build or to harm.

Dual-Track Coexistence: The Complementary Laws of the Lighthouse and the Torch

Viewing them solely as “giants vs open source” misses the real structure: they are two segments of the same technological river, mutually driving each other.

The lighthouse is responsible for pushing boundaries and providing new methodologies and paradigms; the torch compresses, engineers, and democratizes these results into accessible productivity. The diffusion chain is already quite clear: from research papers to reproduction, from distillation to quantization, then to local deployment and industry-specific customization, ultimately raising the overall baseline.

And this baseline elevation, in turn, influences the lighthouse. When a “sufficiently strong baseline” becomes accessible to everyone, giants can no longer maintain long-term monopoly through “fundamental capabilities” alone and must continue investing resources to seek breakthroughs. Meanwhile, the open-source ecosystem generates richer evaluation, adversarial testing, and user feedback, pushing frontier systems to become more stable and controllable. Many application innovations occur within the torch ecosystem, with the lighthouse providing capabilities and the torch providing the soil.

In the foreseeable future, the most reasonable architecture is a combination — similar to an electrical grid. The lighthouse is used for extreme tasks (requiring the strongest reasoning, cutting-edge multimodal, cross-domain exploration, complex scientific assistance); the torch is used for key assets (involving privacy, compliance, core knowledge, long-term cost stability, offline availability). Between them, many “intermediate layers” will emerge: proprietary enterprise models, industry-specific models, distilled versions, hybrid routing strategies (local for simple tasks, cloud for complex ones).

This is not compromise but engineering reality: the ceiling seeks breakthroughs, the baseline seeks ubiquity; one pursues extremity, the other reliability. Both are indispensable — without the lighthouse, technology risks stagnation in “cost-performance optimization”; without the torch, society risks dependence on a few platforms monopolizing capabilities.

The True Watershed: Who Controls the Torch, Who Holds Sovereignty

The contest between lighthouse and torch appears on the surface as a difference in model capability and open-source strategy, but in reality, it is a covert war over AI distribution rights. This war unfolds across three dimensions:

First, the definition of “default intelligence.” When intelligence becomes infrastructure, the “default option” signifies 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.

Second, the approach to externalities. Training and inference consume energy and computational resources; data collection involves copyright, privacy, and labor; model outputs influence public opinion, education, and employment. Both lighthouse and torch generate externalities, but their distribution differs: lighthouse models are more centralized and easier to regulate but pose single points of failure; torch models are more dispersed and resilient but harder to govern.

Third, the individual’s position within the system. If all critical tools require “online connection, login, payment, and adherence to platform rules,” then personal digital life becomes “permanent leasing” — convenient but never truly owned. The torch offers an alternative: enabling offline capabilities, keeping control over privacy, knowledge, and workflows in one’s own hands.

Epilogue: The Lighthouse in the Distance, the Torch at Your Feet

The lighthouse determines how high we can push intelligence — it is civilization’s offensive into the unknown.

The torch determines how broadly we can distribute intelligence — it is society’s self-restraint in the face of power.

Cheering for SOTA breakthroughs is justified because they expand the boundaries of human thought; equally justified is celebrating open-source and torch iterations because they make intelligence not just the domain of a few platforms but a tool and asset 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 one’s borrowing — that light is the torch.