近期，黑灯公司这一概念在产业界迅速升温，引发大量创业者跟风入局。然而，在追捧热潮之下，有必要从产业演进和技术经济学的角度进行冷静辨析。首先需明确：黑灯工厂（Dark Factory）代表的是工业4.0框架下的智能制造范式，其依托物联网、柔性自动化产线和实时控制系统，实现生产环节的无人化或少人化，这是制造业降本增效的确定性趋势。而黑灯公司则指代另一类商业实体——其核心业务完全依赖于利用生成式AI（AIGC）批量产出文案、图像、视频、演示文稿乃至AI数字人直播等数字内容，并以打包服务形式向客户交付。
黑灯公司中的绝大多数（预估超过99%）将在三年内消亡，其可行商业模式窗口期最多不超过18个月。这一判断并非基于技术悲观，而是源于对其价值护城河、技术演进规律及客户行为变迁的严谨推演。
一、核心资产的虚无化
分析任何商业实体的可持续性，需首先解构其核心资产。黑灯公司声称的技术壁垒是否成立？
其真正掌握的资源，本质是一套相对熟稔的提示工程（Prompt Engineering）流程与参数调优的操作指南。这属于过程性知识（Procedural Knowledge），而非因果性知识（Causal Knowledge），其可复制性极强，壁垒极低。类比来看，这与二十年前中关村组装电脑的攒机商并无二致——彼时依靠硬件信息不对称与系统安装技能赚取差价，如今则依靠模型调用技巧与模板化内容生成获取收益。这种模式本质是套利信息差，而非创造价值增量。
二、技术迭代的归零效应
过渡期为何至多18个月？核心驱动力来自大模型本身的指数级迭代速度。当前基础模型的更新周期已压缩至数月，且每次迭代都伴随多模态能力增强、上下文窗口扩展、指令遵循精度提升及成本骤降。例如，Midjourney、Stable Diffusion、GPT系列等每隔季度即推出原生新功能，这些功能往往直接内置了过去需要第三方精细调优才能实现的特性。
这一规律意味着：任何耗时数月精调的Agent工作流，在官方一次版本更新后，其工艺价值可能瞬时归零。客户侧的算力成本与调用成本也以每月约20%的速度递减，使得专业代调服务的相对价值加速贬值。更关键的是，客户在完成两至三单外包后，其内部员工（如实习生）仅需投入极低会员费用，即可通过现成工具还原出外包质量80%的产出。此时，客户对黑灯公司的价格敏感度将降至零，内部化（Internalization）成为必然选择。
三、黑灯工厂与黑灯公司的本质
为何黑灯工厂是长期趋势，而黑灯公司不是？根源在于价值创造的结构性差异。
四、案例：跨境电商中的AI赋能
以跨境电商为例：某头部卖家同样使用AI批量生成商品图、撰写多语种文案、优化广告投放，但其客户支付的是订单量和ROI，而非这些素材本身。其核心能力集群包括：
在这些环节中，AI仅作为降本增效的辅助工具，帮助节省约30%的人力成本，但剩余70%的价值——即商业决策、供应链协同、品质兜底和用户体验设计——完全由人类专业团队创造。利润之源始终是那个完整的商业闭环，而非AI输出物。
五、拒绝跟风，拥抱重资产转型
我始终鼓励企业积极应用AI提升效率，但若仅因风口而组建黑灯公司试图赚取快钱，则属战略短视。信息差红利正以肉眼可见的速度消散，产业终局将是工具平民化。
若仍选择在此领域深耕，建议两条可行路径：
综上所述，黑灯公司的短暂繁荣是技术扩散曲线早期的正常现象，但其商业模式缺乏持续性的经济租金。唯有将AI融入更宽厚的价值网络之中，才能摆脱过渡期玩家的命运。
Recently, the concept of “dark companies” has gained rapid traction across industries, drawing a flood of entrepreneurs into the space. Amid the hype, however, calm analysis from the perspectives of industrial evolution and techno-economics is warranted. First, a distinction must be made: the Dark Factory represents a smart manufacturing paradigm under Industry 4.0. Powered by the Internet of Things, flexible automated production lines and real-time control systems, it enables unmanned or minimally manned operations in production, an irreversible trend for manufacturing to cut costs and boost efficiency. A dark company, by contrast, refers to a different type of commercial entity. Its core business relies entirely on generative AI (AIGC) to mass-produce digital assets including copywriting, images, videos, presentations and even AI avatar livestreams, which are delivered to clients as packaged services.
The vast majority of dark companies (estimated at over 99%) will cease to exist within three years, with a viable business window of no more than 18 months. This judgment stems not from tech pessimism, but rigorous deduction of their value moats, patterns of technological evolution and shifts in client behaviour.
I. The Erosion of Core Assets
To assess the sustainability of any business entity, one must first deconstruct its core assets. Do the supposed technological barriers claimed by dark companies hold up?
What these firms truly possess is essentially a mature set of prompt engineering workflows and parameter tuning guidelines. This is procedural knowledge rather than causal knowledge; it is highly replicable and carries minimal barriers to entry. By analogy, they resemble PC assemblers in Zhongguancun two decades ago. Back then, profits came from exploiting information asymmetry around hardware and system installation skills. Today, revenue is generated through model invocation techniques and templated content generation. This model is fundamentally arbitrage on information gaps, instead of the creation of incremental value.
II. The Reset Effect of Technological Iteration
Why is this transition capped at 18 months? The core driver lies in the exponential iteration speed of large models. The update cycle for base models has shrunk to mere months. Each upgrade brings enhanced multimodal capabilities, expanded context windows, improved instruction-following accuracy and sharp cost reductions. For instance, Midjourney, Stable Diffusion and the GPT series roll out native new features every quarter. These built-in functions often replicate capabilities that previously required sophisticated third-party tuning.
This pattern means that any Agent workflow refined over months may lose its commercial value instantly after a single official model update. Client-side computing and API call costs decline by roughly 20% per month, accelerating the depreciation of professional custom tuning services. More critically, after two or three outsourced projects, in-house staff such as interns can produce outputs reaching 80% of outsourced quality using off-the-shelf tools for a modest subscription fee. At this stage, clients’ price sensitivity for dark company services fades, and internalization becomes inevitable.
III. The Essential Difference Between Dark Factories and Dark Companies
Why are dark factories a long-term trend while dark companies are not? The root lies in structural differences in value creation.
A dark factory is a complex system integration within the physical world. Its differentiation arises from multi-dimensional capabilities: product design, process engineering, supply chain management, quality control, energy efficiency optimisation, branding and distribution. Automated production lines serve only as the execution layer; human experts remain in charge of decision-making and strategy. Its moat rests on systems engineering expertise and industry experience, which cannot be readily replicated by general-purpose AI.
Dark companies deliver native AIGC outputs. Such content is fragmented and context-free, lacking deep integration across the full spectrum of client operations. They provide tool outputs rather than business solutions. Clients purchase content for its conversion performance, not the content itself. Yet dark companies cannot be accountable for conversion outcomes, as they hold no closed-loop data covering product selection, pricing, channels or after-sales support.
IV. Case Study: AI Empowerment in Cross-Border E-commerce
Take cross-border e-commerce as an example. Leading sellers also deploy AI to mass-generate product images, write multilingual copy and optimise ad placements. What clients pay for, however, is order volume and ROI, not the raw creative assets. Their core capability set includes:
Within this ecosystem, AI acts merely as an auxiliary efficiency tool, cutting labour costs by approximately 30%. The remaining 70% of value — business decision-making, supply chain coordination, quality assurance and user experience design — is entirely created by human specialist teams. Profit originates from this complete commercial closed loop, not AI-generated outputs.
V. Resist Herd Mentality and Embrace Capital-Heavy Transformation
I consistently encourage enterprises to adopt AI to lift operational efficiency. However, building a dark company purely to chase quick gains amid the hype represents strategic short-sightedness. Information-gap dividends are dissipating rapidly, and the industry end state will be democratised tooling.
For those determined to deepen engagement in this sector, two viable paths are recommended:
In summary, the fleeting prosperity of dark companies is a normal phenomenon in the early phase of technology diffusion. Their business models fail to sustain economic rents. Only by embedding AI into a broader value network can firms escape the fate of transitional players.