A few days ago, Mary Meeker, known as the “Queen of the Internet”, collaborated with BOND to release an in-depth AI trend report, which not only reviewed the development history of AI in detail, but also delved into the many risks it faces in the commercialization process and its application prospects in different industries.
A few days ago, Mary Meeker, known as the queen of the Internet, collaborated with her colleagues at BOND to release a 340-page AI trend report. The report summarizes facts and refines opinions around the development history of AI, commercialization risks, and industry application directions. This article reflects and summarizes the content mentioned in the report that AI affects the transformation of human work.
AI’s disruption of human work is no longer at the theoretical level, but is a practical challenge and strategic choice that many business leaders are facing. In the report, Luis von Ahn, co-founder and CEO of the educational app Duolingo, talks about the need to create massive content to achieve quality teaching. It is difficult to scale content output with pure manual operations.Lusi talked about Duolingo’s most sensible decision in recent times, which is to use AI to reconstruct the originally slow manual content production process. Without AI, it could take decades for Duolingo to scale its teaching content to more learners。 At the same time, Luis emphasized that being AI-first means restructuring the work system, and there is no need to tinker with old systems based on manual processes, and it is impossible to wait for the technology to be perfect before acting. Duolingo would rather endure occasional fluctuations in the quality of work in the process of rapid progress than miss the opportunity of the times due to slow action.
Also based on industrial practice, NVIDIA explores industry areas that may be transformed by AI from a broader picture of human work.
(Source: BOND Trends – Artificial Intelligence)
At the same time, NVIDIA co-founder and CEO Jensen Huang pointed to the point,The direct source of the threat to employment is not AI itself, but those who are the first and proficient in AI tools。 This means that employees should value upgrading their own skills. Another insight of Huang is to reveal the enormous power of technological equality,That is, AI is lowering the threshold for computer expertise such as programming at an unprecedented rate, making it accessible to a wider range of people。 At the same time, AI has the ability to activate tens of millions of people around the world who have withdrawn from the job market to return to work, which will not only alleviate the global labor shortage, but also boost global GDP.
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Luis and Huang’s practical conclusions jointly point to an unavoidable direction, that is, whether it is an enterprise or an individual, effectively implementing AI is a must for sustainable development.It is worth noting that Internet queen Mary and others clearly pointed out in the report that from historical experience, the role of humans is irreplaceable and very important, technological progress often brings new employment opportunities, and this employment change will be more rapid in the AI era. The report points out that human work will shift to AI supervision, guidance and training, such as human feedback on AI implementation results and providing specific guidance for work.
So, how to effectively implement AI, this article tries to put forward three aspects of thinking:
First, AI should be deeply embedded in specific business processes, especially content production processes.For example, in the field of education, this is reflected in the processing process of embedding AI into teaching courseware, homework questions and other content; In the sales field, AI is embedded in the sales consultant’s speech generation and customer intent recognition process.In addition to paying attention to AI technology itself, the core is to standardize the operation process and clarify the division of responsibilities, that is, it must clearly define the evaluation criteria for AI users, trigger conditions, input specifications, and content output quality.Without a clear work organization framework, the improvement of work efficiency may be accompanied by the risk of loss of control of work quality.
Second, the effect of technological equality may be a double-edged sword in practice.On the one hand, non-professionals can use AI tools to complete tasks that previously required specialized knowledge, such as quickly generating product prototypes or conducting basic data analysis, in a very short period of time, which greatly improves individual work efficiency. But the other side of the coin is the sudden increase in competitive standards and the intensification of involution. Design drafts that used to take days to complete may be compressed into hours through AI. When you first master AI to draw product prototypes, your colleagues may have used it to develop software function prototypes.In the future of work, the efficiency improvement brought by AI may not be translated into individual leisure time, but due to the general upward movement of ability benchmarks, accepting higher-density and higher-intensity tasks will become the new normal.Expecting more leisure from AI may be too optimistic at this stage.
Third, in the face of the ever-changing AI tool upgrades, don’t be “tired of chasing the new”.At present, the speed of AI tool upgrades and iterations is dizzying, and new models and new applications are emerging one after another. Forcibly pursuing “full mastery” not only consumes a lot of energy, but may also fall into the anxiety and burden caused by overstudy.A more pragmatic strategy is to deepen your understanding of the industry and business you are engaged in, as well as master the basic ability to interact with AI tools.For individuals, no matter what industry they are in, maintaining and deepening their understanding of the essential needs of the business in this field (such as teachers’ insight into students’ cognitive laws and cognitive levels in the education industry) is the real moat. At the AI operation level, mastering basic prompting skills that are commonly used across platforms (such as asking questions correctly, etc.) is usually sufficient.The reason is that the ultimate direction of technological development is user convenience and experience friendliness.When truly easy-to-use, powerful “super tools” appear, just as a smartphone integrates countless functions on a touchscreen, the complex tricks we struggle to learn today can become obsolete and become as meaningless as practicing the five-stroke input method.
Luis’s insight and Huang Renxun’s judgment jointly show the grim picture and action direction of work change in the AI era. Businesses evolve by reimagining processes, embracing AI-first, and tolerating imperfections in exploration. The way for individuals to develop is to quickly master AI tools to enhance their competitiveness, stay awake to the fierce competition brought about by technological equality, and prepare for a real technological leap (super tool).
There are no bystanders in this work change, and only those who adapt will have a future.
