Read More《当所有人都在学提示工程时，聪明人却专注于掌握这项技能》
Positive Review: The “Translation Layer” Skill Hits the Pain Point of AI Implementation and Reconstructs New Value in Human-Machine Collaboration
When the whole nation was caught up in the “Prompt Engineering Fever”, the concept of the “translation layer” proposed in the article was like a wake-up call, precisely pointing out the core of workplace competitiveness in the AI era – it’s not about competing with AI for the “right to operate”, but about becoming the “value bridge” between AI and humans. The forward-looking nature and practical significance of this view are reflected in at least the following three aspects:
First of all, it directly addresses the “last mile” dilemma of AI implementation. Currently, enterprises have made substantial investments in AI, but a large amount of insights generated by AI (such as customer sentiment analysis, market trend prediction, and operational optimization suggestions) often end up as “digital garbage”. The crux of the problem is not the lack of AI capabilities, but the inability of humans to convert technical language into decision-making language. For example, a retail enterprise once used AI to analyze user reviews and concluded that “3 out of 47 emotional patterns were strongly related to repeat purchases”. However, since no one could explain the specific scenarios of these 3 emotions (such as “anxiety caused by delayed shipping speed”) and the corresponding operational improvement plans (such as optimizing logistics timeliness), the valuable insights were shelved. The existence of the “translation layer” is precisely to solve the gap between “technical output” and “business execution”, enabling AI to truly upgrade from an “analytical tool” to a “decision-making partner”.
Secondly, it captures the essence of skill scarcity. The “shelf life” of technical operation skills such as prompt engineering is extremely short – as AI interfaces evolve (from text prompts to multimodal interactions), today’s “optimal prompt words” may become ineffective tomorrow. On the other hand, the communication skills required by the “translation layer” (such as simplifying complex information, associating business backgrounds, and designing action frameworks) are unique “soft skills” of humans. Not only will they not be replaced by AI, but they will become even scarcer due to the popularization of AI. Just as in the Internet era, the “information retrieval” ability depreciated with the emergence of search engines, while the “information interpretation and integration” ability (such as data analysts and content planners) became a new necessity. Similarly, when AI can quickly generate a large amount of analysis, the translation ability to “make the analysis valuable” will naturally become the core resource that enterprises compete for.
Finally, it conforms to the underlying laws of technological evolution. In every technological revolution in history, it has been a process of “tools replacing repetitive labor and humans shifting towards higher-value creation”. During the Industrial Revolution, machines replaced manual production, but gave rise to new occupations such as “production line management” and “quality control”. In the Internet era, search engines replaced some information retrieval work, but new roles such as “user experience design” and “data visualization” emerged. The development of AI also follows this logic: when AI takes over “left-brain tasks” such as information processing and pattern recognition, the “right-brain advantages” of humans (such as situational understanding, emotional resonance, and strategic judgment) will be magnified, and the “translation layer” is precisely the hub connecting these two abilities. It will not only not be replaced by AI, but will become even more important due to the power of AI – the stronger AI becomes, the more humans need a “translator” to explain the meaning of its output, otherwise, technology will become a “soulless tool”.
Negative Review: Between the Ideal and Reality of the “Translation Layer”, Three Potential Risks Need to Be Watched Out For
Although the concept of the “translation layer” is highly inspiring, overemphasizing its value may cover up some real challenges and even mislead the skill development direction of some practitioners. The following three points are worthy of in-depth discussion:
Firstly, the effectiveness of the “translation layer” highly depends on the dual thresholds of “technical understanding” and “business depth”. The “converting AI output into human decisions” mentioned in the article is not simply a “language conversion”, but requires a profound understanding of AI technical principles (such as model limitations and data biases) and business scenarios (such as industry rules and enterprise resources). For example, if a translator is unaware of the problem of “over – generalization of emotional labels” that may occur when an AI model analyzes user reviews (such as misjudging “neutral feedback” as “negative emotion”), they may wrongly push irrelevant suggestions to the business team. If they are not familiar with the current strategic priorities of the enterprise (such as focusing on expanding new customers rather than maintaining old customers in a certain quarter), they will not be able to determine whether the “old – customer repeat purchase optimization” suggestion put forward by AI is worth investing resources in. This means that the “translation layer” cannot be fulfilled only by “communication skills”, but requires a compound background of “technology + business”. The difficulty of cultivating such skills is much higher than that of simple prompt engineering or pure business communication, which may lead to the dilemma of “the ideal is plump, but the reality is hard to achieve”.
Secondly, overemphasizing the “translation layer” may ignore the long – term value of technical depth. The article argues that “technical skills are quickly obsolete”, but in some fields (such as AI model development and underlying algorithm optimization), technical depth remains an irreplaceable core competitiveness. For example, in the field of medical AI, expert – level algorithm engineers are needed to ensure the diagnostic accuracy of models, and in the field of financial risk control, technical talents are required to deal with adversarial attacks on AI models. If practitioners blindly pursue the “translation layer” and abandon in – depth technical exploration, it may lead to an imbalance in their “technology – business” compound abilities. More importantly, if enterprises overly rely on “translators” and neglect the construction of technical teams, they may lose their core competitiveness in the rapid iteration of AI technology – after all, the premise of “translation” is to have “high – quality AI output”, which requires strong technical capabilities as support.
Thirdly, the evolution of AI itself may weaken the necessity of the “translation layer”. Currently, the “unexplainability” of AI output (such as the “black – box” problem of large models) indeed requires human translation, but technological development may change this situation. For example, the progress of explainable AI (XAI) is making model output more transparent (such as visualizing key features through attention mechanisms); the popularization of multimodal interactions (such as natural language + charts + voice) may enable AI to directly generate more understandable “decision – making reports”; and in the future, there may even be “intelligent translation AI” specifically responsible for converting technical language into business language. Although these technologies are not yet mature, their development trend means that the value of the “translation layer” may change dynamically over time. If practitioners regard it as an “eternal core skill”, they may face new career risks.
Advice for Entrepreneurs: Find a Dynamic Balance between “Technology” and “Translation”
For entrepreneurs, the core challenge in the AI era is not “choosing to learn technology or translation”, but “how to build a collaborative system of technical capabilities and translation capabilities”. Based on the “translation layer” view in the news, the following suggestions are worth considering:
Clarify the Positioning of the “Translation Layer” in the Team and Avoid “Translating for the Sake of Translating”
Entrepreneurs need to first identify the “AI – human gap” within the enterprise: Is the AI output too abstract for the business team to execute? Or is the business requirement too vague for AI to respond effectively? For example, the marketing team may need to convert the “user profile” generated by AI into specific advertising placement strategies, while the R & D team may need to convert the “technical feasibility analysis” into the priority ranking of product development. For different scenarios, the “translators” in the team should focus on the specific “gap” rather than simply emphasizing “communication skills”. At the same time, it is necessary to avoid the “translation layer” becoming a “middleman for information transfer” – the real value lies in “adding value”, that is, by supplementing the business background, evaluating resource limitations, and designing action paths, significantly increasing the probability of implementing AI suggestions.Cultivate Compound Talents with “Technology + Business” Skills, Not “Pure Translators”
Entrepreneurs should give priority to recruiting or cultivating compound talents who understand both AI technology (such as being able to understand the limitations of model output) and business (such as being familiar with industry rules and enterprise strategies). For example, in the retail industry, a “translator” needs to master both basic machine – learning knowledge (such as knowing that the “user segmentation” model may produce distorted results due to data biases) and retail business logic (such as the short – term impact of promotional activities on user behavior). Enterprises can accelerate the cultivation of such compound abilities through “technical rotation” (letting business personnel participate in AI projects) or “business training” (letting technical personnel go to the front line), avoiding the “translation layer” becoming an intermediate obstacle where “technologists don’t understand business and business people don’t understand technology”.Balance Technical Investment and Translation Ability Building to Avoid “Limping Development”
Entrepreneurs need to clearly recognize that the value of the “translation layer” is based on “high – quality AI output”. If an enterprise’s AI technical capabilities are weak (such as low model accuracy and poor data quality), even with excellent translators, it will be impossible to convert “wrong insights” into “correct decisions”. Therefore, enterprises need to find a balance between technical R & D (such as improving model performance and optimizing data collection) and translation ability (such as cultivating business interpreters). For example, in the early stage, they can give priority to investing in technology to ensure the basic quality of AI output, and at the same time, pilot the “translator” role in key business areas (such as customer insights and operational optimization), and gradually expand after verifying its value.Keep an Eye on AI Technology Trends and Dynamically Adjust the Functional Boundaries of the “Translation Layer”
Entrepreneurs need to closely follow the development of AI technology (such as explainable AI and multimodal interactions) and adjust the functions of the “translation layer” accordingly. For example, if an enterprise introduces an AI model with strong explainability, the focus of the translator’s work can shift from “explaining model output” to “associating business scenarios”; if AI tools support more natural human – machine conversations (such as directly generating action suggestions through voice interaction), translators need to pay more attention to “verifying the rationality of suggestions” and “promoting implementation”. The key to dynamic adjustment is to always let the “translation layer” solve the most pressing “AI – human gap” at present, rather than sticking to a fixed function.
Conclusion: The core competitiveness in the AI era has never been “confronting AI” or “simply imitating AI”, but “making AI work for humans”. The value of the “translation layer” concept lies in reminding us that the ultimate goal of technology is to serve humans, and the “translator” connecting technology and humans will be the most irreplaceable role in this era. For entrepreneurs, the key is not to blindly pursue the label of the “translation layer”, but to find a dynamic balance between technical capabilities and translation capabilities, enabling AI to truly become the “intelligent engine” driving business growth.
创业时评《当所有人都在学提示工程时，聪明人却专注于掌握这项技能》
- China Uncovered”Chengdu | A Day in the “Center of the Universe””
- Startup Commentary”Just now, Altman launched GPT-5, offering “doctor-level” AI for free to everyone, but the benchmark graph errors sparked online criticism.”
- Startup Commentary”Ant Digital Technology Collaborates with Licensed Financial Institutions in Hong Kong, One-Stop Tokenization Issuance Ecosystem Takes Shape | Frontline Report”
- Chain Exploration”In – depth Research Report on Stablecoins: The Anchor Asset for the Next Round of Financial Transformation”
- Startup Commentary”In the 19.9-yuan roast duck, there lies Chinese magic that the West can’t learn.”
