Public discourse on AI is often dominated by metrics like parameter scale, benchmark rankings, and which new model has outperformed another. While not entirely meaningless, this noise often obscures a more fundamental undercurrent: a covert struggle over the allocation of AI power is unfolding within today’s technological landscape. From a civilizational infrastructure perspective, artificial intelligence is manifesting in two distinct yet intertwined forms. One resembles a towering ‘Lighthouse,’ controlled by a handful of tech giants. It pursues the farthest reach, representing the current cognitive frontier humanity can achieve. The other is akin to a handheld ‘Torch,’ prioritizing portability, private ownership, and replicability. It represents the baseline level of intelligence accessible to the public. Understanding these two sources of light allows us to see beyond marketing jargon and clearly discern where AI is leading us, who will be illuminated, and who risks being left in the dark. **The Lighthouse: Defining the Cognitive Ceiling** The ‘Lighthouse’ refers to Frontier or State-of-the-Art (SOTA) models. In complex reasoning, multimodal understanding, long-chain planning, and scientific exploration, they represent the most capable, costly, and centrally organized systems. Organizations like OpenAI, Google, Anthropic, and xAI are typical ‘lighthouse builders,’ establishing a production paradigm that trades extreme scale for boundary-pushing breakthroughs. **Why Lighthouses Are Inevitably a Minority Game** Training and iterating frontier models essentially bundles three extremely scarce resources: immense computing power (costly chips, large-scale clusters, long training cycles), vast data and feedback loops (requiring massive corpus cleaning and iterative human feedback), and complex engineering systems (distributed training, fault-tolerant scheduling). This creates a high barrier to entry, resembling a capital-intensive industrial system rather than a feat of clever code. Consequently, lighthouses are inherently centralized, typically controlled by a few entities that maintain training capabilities and data loops, ultimately offered as API services, subscriptions, or closed products. **The Dual Role of Lighthouses: Breakthrough and Guidance** The lighthouse’s value lies not in making copywriting faster for everyone, but in two more fundamental roles. First, it explores the cognitive ceiling, illuminating ‘feasible next steps’ for tasks at the edge of human capability. Second, it pioneers new technical paradigms—whether in alignment, tool use, or safety—that later trickle down and shape the broader industry. It acts as a societal laboratory. **The Lighthouse’s Shadow: Dependency and Single-Point Risk** However, significant risks accompany lighthouses. Accessibility is controlled by the provider’s strategy and pricing, leading to high platform dependency. Convenience masks fragility: outages, service termination, policy changes, or price hikes can instantly disrupt workflows. Deeper concerns involve privacy and data sovereignty, especially for regulated industries like healthcare and finance. Systemic biases or supply chain disruptions within a few dominant models can amplify into significant societal risks. **The Torch: Defining the Intelligence Baseline** The other light source is the ecosystem of open-source and locally deployable models, represented by projects like DeepSeek, Qwen, and Mistral. This is the ‘Torch.’ It corresponds not to the ceiling of capability but to the baseline—the level of intelligence the public can obtain unconditionally. **The Torch’s Significance: Turning Intelligence into an Asset** The core value of the torch is transforming intelligence from a rental service into a proprietary asset, characterized by three dimensions: privacy, portability, and composability. It can run locally or on private networks (‘ownership’ vs. ‘rental’), be migrated across different hardware and vendors, and be combined with other tools like RAG or fine-tuning to create bespoke systems. This meets critical needs in enterprise knowledge management, regulated industries with strict data residency rules, and offline or weak-network environments. **Why the Torch Grows Brighter** The rising capability of open-source models stems from the convergence of two paths: the rapid diffusion of research (papers, techniques) within the community and extreme engineering optimizations (quantization, distillation, MoE). A clear trend is emerging: the strongest models define the ceiling, but ‘sufficiently strong’ models define the speed of adoption. Most societal tasks require reliability, control, and stable cost—not necessarily peak performance. **The Torch’s Cost: Security Outsourced to the User** The torch is not inherently virtuous; its cost is the transfer of responsibility. Risks and engineering burdens previously shouldered by platforms now fall on users. Open models can be misused for generating scams or deepfakes. Local deployment requires users to handle evaluation, monitoring, prompt injection protection, and model updates themselves. This freedom comes with significant responsibility. **Converging Light: The Co-evolution of Ceiling and Baseline** Viewing lighthouses and torches as a simple ‘giants vs. open-source’对立 misses the real dynamic: they are two segments of the same technological river. Lighthouses push boundaries and establish new paradigms; torches compress, engineer, and disseminate these advancements into普及的生产力. This diffusion chain—from paper to replication, distillation to quantization, to local deployment—continuously elevates the baseline. This elevated baseline, in turn, pressures lighthouses. When a ‘sufficiently strong baseline’ is widely available, maintaining monopoly through basic capability becomes difficult, forcing continued investment in突破. Open-source ecosystems also generate rich feedback that pushes前沿 systems toward greater stability and control. Thus, this is less about two opposing camps and more about two institutional arrangements: one concentrates extreme cost for上限突破; the other disperses capability for普及, resilience, and sovereignty. Both are essential. **The Harder, More Critical Battle: What Are We Actually Fighting For?** The competition between lighthouses and torches is, on the surface, about model capabilities and open-source strategy. Beneath lies a covert war over AI allocation, fought across three decisive dimensions: 1. Defining ‘Default Intelligence’: As AI becomes infrastructure, the ‘default option’ confers power. Who provides it? Whose values and boundaries does it follow? 2. Allocating Externalities: Both forms generate externalities (energy use, copyright issues, societal impact), but distribute them differently—lighthouses centrally, torches diffusely. 3. Determining the Individual’s Position: If crucial tools require ‘online access, login, payment, and platform rules,’ digital life becomes akin to renting—convenient but never truly owned. The torch offers an alternative, allowing individuals to retain control over privacy, knowledge, and workflows. **A Dual-Track Future Will Be the Norm** The most plausible future is not a choice between fully closed or fully open source, but a hybrid system akin to power grids. We will need lighthouses for extreme tasks requiring peak performance and前沿 exploration. We will need torches for scenarios involving privacy, compliance, core knowledge, and offline usability. Between them, a rich ‘middle layer’ will emerge: enterprise-specific models, industry models, distilled versions, and hybrid routing strategies. This is not compromise but engineering reality: one track pursues突破, the other pursues普及; one seeks the极致, the other seeks reliability. **Conclusion: Lighthouses Guide the Distance, Torches Secure the Ground** Lighthouses determine how high we can push intelligence—it is civilization’s advance into the unknown. Torches determine how widely we can distribute intelligence—it is society’s act of self-preservation in the face of concentrated power. Applauding SOTA breakthroughs is justified, as they expand the boundaries of human thought. Applauding the迭代 of open-source and privately deployable models is equally justified, as they transform intelligence from a platform-owned service into a tool and asset for the many. The true watershed of the AI era may not be ‘whose model is stronger,’ but whether, when darkness falls, you hold in your hand a light you don’t have to borrow from anyone. *Disclaimer: This article represents the author’s personal views and does not constitute investment advice. Readers should comply with the laws and regulations of their country or region.*
