"Productivity Increases with AI, But Will Jobs Decrease?" Why the UK Has Embarked on a "Two-Pronged Policy"

"Productivity Increases with AI, But Will Jobs Decrease?" Why the UK Has Embarked on a "Two-Pronged Policy"

"Will AI Take Our Jobs?" The UK government has begun to address this question head-on, which is quite rare. The answer is this: "Some jobs will decrease." However, instead of leaving it at that, they plan to boost productivity with AI to elevate the economy while simultaneously creating a system that doesn't leave workers behind. The UK's aim is a two-pronged strategy: making AI an "economic catalyst" while achieving a "soft landing for employment."


Breaking the "Long Stagnation" in Productivity with AI

The problem with the UK economy is simple: productivity growth has been sluggish for years. This makes it difficult to raise wages and living standards, and fiscal flexibility is hard to come by. Enter AI. It has the potential to accelerate many "intellectual tasks" such as document creation, summarization, research, analysis, customer service, and internal inquiries. Particularly in the UK, where the service industry holds a significant share, AI is seen as having a wide range of applications.


The on-the-ground image is straightforward. Internal reports and some reviews that used to take days or weeks can now be shortened to a few hours. People are freed from simple tasks and can focus on "processes where human value shines," such as customer service and decision-making. This is the general picture of the "AI-driven productivity revolution" as described by the government and companies.


However, jobs at the entry level will disappear. The significance of "acknowledging" this

On the other hand, the introduction of AI also means that operations can be run "without increasing manpower." In other words, employment growth will slow down. Those most affected will be in routine office work, back-office, and entry-level positions (new graduates and young workers). The fact that the government has acknowledged the possibility of "job reduction" is significant. The issue is not the number of jobs lost, but rather the shift towards "starting social design with the premise that there will be phases of reduction."


The core of the policy here is "nationwide retraining." The UK aims to expand free AI basic training and spread basic skills to 10 million people by 2030. Starting with short courses, graduates will receive government-endorsed digital badges (basic certification). The goal is to shift AI from being a "tool for specific professions" to "basic literacy for all workers."


Furthermore, within the government, there is a cross-organizational unit (dealing with AI and the future of work) to monitor the impact of AI on employment and wages and propose necessary policies. By involving labor unions, industries, and experts, the aim is to "anticipate changes" rather than "respond after chaos occurs." The notable feature this time is that the call for AI introduction is accompanied by a "side-effect monitoring system."


AI in Public Services: A Specialist Team Funded by Meta Begins

Productivity improvement is not just a private sector issue. The UK is also clearly aiming to use AI to improve public services. AI tools will be introduced in areas involving "judgment and allocation" within administration, such as road and transport network maintenance, public safety, and national defense. One example is the establishment of a specialist team funded by Meta. They aim to develop open-source AI tools over a year, allowing the government to avoid over-reliance on commercial closed systems. The design focuses on "sovereignty (control)" rather than just "usability," enabling operation and modification on the government side without externalizing data.


However, there are challenges here. The faster AI is utilized, the more pronounced the issues of "whose AI to use" and "which country's companies to depend on" become. In fact, there are criticisms that the government's AI strategy might increase dependence on American companies. While cutting-edge technology is needed to boost productivity, the responsibility for national accountability also becomes heavier.


The Decisive Battle of "Employment Distribution": The Key is "Replacement" or "Augmentation"

The impact of AI on employment varies significantly more by "how it is used" than by the technology's capabilities. Even with the same AI, if it leans towards (1) "replacement," reducing people to cut costs, employment is likely to decrease. If it leans towards (2) "augmentation," entrusting part of the work to AI and elevating human roles upstream, the total amount of employment is more likely to be maintained.


According to analysis by a UK think tank, while the worst-case scenario suggests that millions of jobs could disappear, if it leans towards augmentation, there is a path to "boost GDP without job loss." This is where policy plays a role. To prevent companies from leaning too much towards "replacement," the costs of training, relocation, and redeployment are spread across society, guiding towards augmentation. The UK's 10 million skills policy can be seen as a massive foundation for this guidance.


Social Media Reactions: Expectations and Concerns Coexist Under the Same Post

The atmosphere on social media regarding this move is not monolithic. Broadly speaking, three prominent reactions can be observed.


1) "Retraining is welcome. Those who can use AI will benefit."
On LinkedIn, there are posts that positively view the government's "10 million by 2030" policy, appreciating the trend of raising AI literacy through public-private collaboration. Some see the combination with women's skill support and regional tech job creation as "realistic."


2) "Who will conduct the training? And who will be left behind?"
On the other hand, there are comments on LinkedIn questioning "who will be responsible for the training," reflecting a reaction that, while the idea is correct, implementation is challenging. Questions continue about whether short courses will truly change the field and whether completion badges will have significance in wages and hiring.


3) "Where will the fruits of productivity go? If entry-level jobs disappear, distribution is necessary."
The government's acknowledgment that "some jobs will be lost" has, in fact, made the discussion more concrete. If entry-level opportunities narrow, the design of apprenticeships, internships, and vocational training must change, or the "ladder of experience" will be removed. How to allocate the surplus generated by AI to wages, working hours, and retraining is a concern. On social media, there is a strong sense of caution against discussing only "growth" while leaving this ambiguous.


Ultimately, the UK's Challenge is Balancing "Speed" and "Fairness"

The UK is not delaying AI introduction but rather showing a stance of "going for it at top speed." In return, they are simultaneously running skill policies and monitoring organizations, preparing with the premise that "pain will occur." Here lies one answer to the dilemma faced by many countries worldwide—wanting to win the technological race but not deepen social divides.


However, whether the policy succeeds is not determined by slogans. Whether AI leans towards "replacement" or "augmentation" in the field. If entry-level jobs decrease, where will young people gain experience? If the government uses AI, how will accountability and transparency be ensured?


The UK's gamble on "productivity with AI and a simultaneous soft landing for employment" is not a distant issue for other countries. Rather, it is worth seeing as a "rehearsal for ourselves" a few years down the line.


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