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Future of Jobs and Generative AI

The advent of large language models (LLMs) like ChatGPT promises to transform the workplace by automating or augmenting a wide range of occupational tasks. However, a single perspective cannot fully grasp both the opportunities and risks these technologies represent across industries, workers, businesses and society. This article analyzes the World Economic Forum’s recent white paper [1] assessing the impact of LLMs on jobs through the lens of Spiral Dynamics. This integral framework reveals how different value systems perceive threats and opportunities differently. Administrative roles face disruption but efficiency gains (Blue). Innovative businesses are pressured to adopt but see new revenue potential (Orange). Vulnerable workers require support amidst job transformations (Green). Policymakers struggle to holistically analyze systemic impacts (Yellow). Realizing the benefits of LLMs requires honoring multiple worldviews, evolving processes, encouraging innovation, caring for people and conducting systems analysis. The analysis provides insights into LLMs’ multi-dimensional impacts and underscores the need for inclusive dialogue and initiatives to shape the AI-enabled future of work.

Here are the key points:

LLMs could significantly impact many jobs due to their ability to automate or augment language-based tasks, which account for an estimated 62% of work time.
The analysis assessed over 19,000 work tasks across 867 occupations to assess their LLM exposure. Tasks with high automation potential are routine and repetitive clerical/administrative tasks. Tasks with high augmentation potential require more abstract reasoning and problem-solving. Tasks with lower exposure potential emphasize interpersonal interaction.
Occupations with the highest automation potential include credit authorizers, telemarketers, statistical assistants, and tellers. Occupations with the highest augmentation potential include insurance underwriters, bioengineers, mathematicians, and editors. Occupations with lower exposure include counselors, clergy, home health aides, and lawyers.
Adopting LLMs will also likely create new roles like AI developers, content creators, interface designers, data curators, and AI ethics specialists.
The financial services and information technology industries have the overall highest potential exposure. The finance and IT functional areas also have increased exposure.
Significant alignment exists between occupations this analysis identifies as having high augmentation potential and those the Future of Jobs Report found to have high expected job growth. Similarly, occupations with high automation potential align with declining occupations.
The report concludes LLMs will transform jobs and tasks, requiring strategies by businesses and government to prepare workforces for the change through training, transition support, and social safety nets. Overall, LLMs present opportunities to raise productivity and create new jobs, if managed responsibly.

Spiral Dynamics stages

What color are you Spiral Dynamics?

Color	Beige	Purple	Red	Blue	Orange	Green	Yellow	Turquoise
In a life	Survival	Family relations	The rule of force	The power of truth	Competition	Interpersonal relations	Flexible stream	The Global vision
In a business	Own farm	Family business	Starting up a personal business	Business Process Management	Project management	Social networks	Win-Win-Win behavior	Synthesis

Here is an analysis of the World Economic Forum white paper on large language models and jobs through the lens of Spiral Dynamics stages:

Spiral Dynamics Stage	Quotes from Document
Beige	No relevant quotes
Purple	No relevant quotes
Red	No relevant quotes
Blue	"With 62% of total work time involving language-based tasks, the widespread adoption of LLMs, such as ChatGPT, could significantly impact a broad spectrum of job roles." (p.4) This reflects the blue focus on structure, process and order.
Orange	"Adopting LLMs will transform business and the nature of work, displacing some existing jobs, enhancing others and ultimately creating many new roles." (p.19) This reflects the orange drive for innovation and progress.
Green	"Governments can also partner with and support employers and educational institutions to provide training programs that prepare workers for the jobs that will grow and benefit the most from LLMs. Additionally, social safety nets and assistance in transitioning to new roles will need to be reimagined and be more precisely targeted for those most likely to be affected." (p.19) This reflects the green concern for people and relationships.
Yellow	"To assess the impact of LLMs on jobs, this paper provides an analysis of over 19,000 individual tasks across 867 occupations, assessing the potential exposure of each task to LLM adoption, classifying them as tasks that have a high potential for automation, high potential for augmentation, low potential for either or are unaffected (non-language tasks). The paper also provides an overview of new roles that are emerging due to the adoption of LLMs." (p.4) This reflects yellow's emphasis on complex systems analysis.
Turquoise	No relevant quotes

The document overall reflects blue, orange, and green worldviews, with some elements of yellow systems thinking. There are no clear expressions of the beige, purple, red or turquoise value systems. This analysis illustrates how technology impacts different aspects of society and values.

Threats

Here is an analysis of threats and affected stakeholders through the lens of Spiral Dynamics stages:

Spiral Dynamics Stage	Threats	Affected Stakeholders
Beige	No major threats identified	N/A
Purple	No major threats identified	N/A
Red	No major threats identified	N/A
Blue	Disruption of administrative processes and routines	Organizations, administrative staff
Orange	Pressure to rapidly adopt new technologies	Businesses, managers
Green	Job losses, inequality, lack of support during transition	Individual workers, marginalized groups, society
Yellow	Complexity of analyzing and managing impacts	Policy-makers, business leaders
Turquoise	No major threats identified	N/A

In summary, the blue stage is threatened by disruption of established administrative processes, the orange faces pressure to innovate, the green risks job losses and inequality, and the yellow struggles with complex systems analysis. This highlights how different worldviews perceive threats and opportunities from the same technology trend. A holistic perspective is needed to understand the range of stakeholders and design responsible policies.

Elon Musk said about the danger of artificial intelligence (A.I.) in an interview with Tucker Carlson in April 2023. Below you can read an abridged version of the results of our VUCA poll "A.I. and the end of civilization". The full version of the results is available for free in the FAQ section after login or registration.

Изкуствен интелект и края на цивилизацията

Графики Корелация

VUCA

Критична стойност на коефициента на корелация

Нормално разпространение, от Уилям Сили Госет (студент) r = 0.0723

Не нормално разпределение, от Spearman r = 0.0029

Показват само резултати > r = 0.0723

Разпределение	Не нормално	Нормално	Не нормално	Нормално	Нормално	Нормално	Нормално	Нормално
Всички въпроси Всички въпроси 1) Безопасност (колко се съгласявате или не сте съгласни?) 2) Контрол (колко сте съгласни или не сте съгласни?)
1) Безопасност (колко се съгласявате или не сте съгласни?)
Answer 1	-	Слаба положителна 0.0711	Слаба положителна 0.0201	Слаба положителна 0.0947	Слаб отрицателен -0.1164	Слаба положителна 0.0003	Слаб отрицателен -0.0470	Слаба положителна 0.0172
Answer 2	-	Слаба положителна 0.0159	Слаб отрицателен -0.0085	Слаба положителна 0.0441	Слаб отрицателен -0.0249	Слаба положителна 0.0359	Слаб отрицателен -0.0004	Слаб отрицателен -0.0485
Answer 3	-	Слаб отрицателен -0.0230	Слаб отрицателен -0.0256	Слаба положителна 0.0113	Слаба положителна 0.0629	Слаб отрицателен -0.0309	Слаб отрицателен -0.0168	Слаб отрицателен -0.0003
Answer 4	-	Слаба положителна 0.0319	Слаб отрицателен -0.0053	Слаба положителна 0.0098	Слаб отрицателен -0.0489	Слаб отрицателен -0.0306	Слаба положителна 0.0038	Слаба положителна 0.0512
Answer 5	-	Слаб отрицателен -0.0137	Слаб отрицателен -0.0242	Слаб отрицателен -0.0243	Слаба положителна 0.0440	Слаба положителна 0.0317	Слаба положителна 0.0223	Слаб отрицателен -0.0552
Answer 6	-	Слаб отрицателен -0.0139	Слаб отрицателен -0.0531	Слаб отрицателен -0.0748	Слаба положителна 0.0654	Слаб отрицателен -0.0077	Слаба положителна 0.0435	Слаба положителна 0.0159
Answer 7	-	Слаб отрицателен -0.0605	Слаба положителна 0.0999	Слаб отрицателен -0.0608	Слаба положителна 0.0061	Слаба положителна 0.0061	Слаб отрицателен -0.0062	Слаба положителна 0.0186
2) Контрол (колко сте съгласни или не сте съгласни?)
Answer 8	-	Слаба положителна 0.0123	Слаба положителна 0.0027	Слаба положителна 0.0776	Слаба положителна 0.0570	Слаб отрицателен -0.0250	Слаб отрицателен -0.0770	Слаб отрицателен -0.0429
Answer 9	-	Слаба положителна 0.0244	Слаб отрицателен -0.0252	Слаб отрицателен -0.0396	Слаба положителна 0.0257	Слаба положителна 0.0841	Слаб отрицателен -0.0111	Слаб отрицателен -0.0565
Answer 10	-	Слаба положителна 0.0175	Слаб отрицателен -0.0400	Слаб отрицателен -0.0565	Слаб отрицателен -0.0163	Слаба положителна 0.0008	Слаба положителна 0.0586	Слаба положителна 0.0303
Answer 11	-	Слаба положителна 0.0181	Слаба положителна 0.0009	Слаба положителна 0.0309	Слаб отрицателен -0.0592	Слаб отрицателен -0.0316	Слаб отрицателен -0.0132	Слаба положителна 0.0573
Answer 12	-	Слаб отрицателен -0.0155	Слаба положителна 0.0357	Слаба положителна 0.0517	Слаба положителна 0.0424	Слаб отрицателен -0.0699	Слаба положителна 0.0055	Слаб отрицателен -0.0469
Answer 13	-	Слаб отрицателен -0.0937	Слаб отрицателен -0.0358	Слаб отрицателен -0.0191	Слаба положителна 0.0053	Слаба положителна 0.0295	Слаба положителна 0.0735	Слаба положителна 0.0056
Answer 14	-	Слаба положителна 0.0041	Слаба положителна 0.0906	Слаб отрицателен -0.0345	Слаб отрицателен -0.0679	Слаб отрицателен -0.0267	Слаб отрицателен -0.0104	Слаба положителна 0.0698

Експорт към MS Excel

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Opportunities

Here is an analysis of opportunities and affected stakeholders through the lens of Spiral Dynamics stages:

Spiral Dynamics Stage	Opportunities	Affected Stakeholders
Beige	No major opportunities identified	N/A
Purple	No major opportunities identified	N/A
Red	No major opportunities identified	N/A
Blue	Increased efficiency of administrative processes	Organizations, administrative staff
Orange	Creation of new business models and revenue streams	Businesses, entrepreneurs
Green	Upskilling workers, maintaining an inclusive workforce	Individual workers, marginalized groups, society
Yellow	Holistic analysis of technology's impact on work	Policy-makers, business leaders
Turquoise	No major opportunities identified	N/A

In summary, blue sees opportunities for improved efficiency, orange in innovation and profit, green in supporting workers, and yellow in systems analysis. This highlights how different worldviews perceive opportunities from the same technology trend. An integral perspective is required to balance opportunities for organizations and individuals.

GAP Analysis

Here is a GAP analysis from a Spiral Dynamics perspective:

Spiral Dynamics Stage	GAP Analysis
Beige	No major gap identified
Purple	No major gap identified
Red	No major gap identified
Blue	GAP: Lacks discussion of how to evolve administrative processes rather than just making existing ones more efficient
Orange	GAP: Could provide more examples of how new business models and industries could arise from LLMs
Green	GAP: More detail is needed on programs to support workers through transitions and ensure opportunities are inclusive
Yellow	GAP: Deeper analysis required on technological impacts across education, business, and government domains
Turquoise	GAP: Holistic vision absent - how could LLMs improve society and actualization beyond business impacts?

In summary, blue could be used more on process evolution, orange on business model innovation, green on worker support, yellow on cross-domain impacts, and turquoise on realizing higher human potential. This reflects common gaps faced when new technologies are viewed primarily through one worldview lens rather than holistically. An integral perspective is needed to fully understand impacts and opportunities.

Overcome Gaps

Here are some suggested measures to overcome the gaps through the lens of Spiral Dynamics perspective:

Spiral Dynamics Stage	Suggested Measures to Overcome GAPs
Beige	N/A
Purple	N/A
Red	N/A
Blue	Conduct process redesign workshops to evolve administrative workflows
Orange	Research case studies and build scenarios describing new LLMs-enabled business models
Green	Profile reskilling programs and multi-stakeholder partnerships to support workers
Yellow	Model impacts of LLMs on education, healthcare, government, and other complex systems
Turquoise	Envision how LLMs could advance human potential and consciousness evolution

In summary, suggested measures include:

Blue: Process redesign workshops
Orange: New business model research
Green: Reskilling program profiles
Yellow: Modelling systemic impacts
Turquoise: Envisioning advancing human potential

This highlights the value of taking a holistic perspective and utilizing tools and ways of thinking from multiple stages and worldviews to fully understand and act upon the opportunities presented by emerging technologies like large language models.

Conclusion

The Spiral Dynamics framework reveals that the opportunities and threats presented by large language models are perceived differently across value systems. Blue sees potential efficiency gains but disruption of administrative routines. Orange focuses on innovation possibilities but feels pressured to rapidly adopt. Green emphasizes supporting impacted workers but risks exacerbating inequalities. Yellow provides systems analysis but grapples with complexity.

Fully realizing the benefits of large language models in the workplace and society requires transcending any worldview. An integral approach that honors multiple perspectives is needed. This includes evolving processes, encouraging innovation, caring for people, and systemic analysis. Further, a holistic vision looks beyond business impacts to how emerging technologies can advance human potential and social actualization.

By understanding these different value perspectives, businesses, policymakers, and workers can collaboratively shape the future of work in the age of artificial intelligence. A shared vision arises when stakeholders cooperate across stages of psychological and social development. This white paper provides insights into the multi-dimensional impacts of large language models across industries, occupations, and societal roles. Yet more inclusive dialogue and initiatives are needed to proactively guide this technology for the benefit of all.

[1] https://www3.weforum.org/docs/WEF_Jobs_of_Tomorrow_Generative_AI_2023.pdf

2023.10.12

Valerii Kosenko

Собственик на продукта SaaS SDTEST®

Валерий получава квалификация социален педагог-психолог през 1993 г. и оттогава прилага знанията си в управлението на проекти.
Валерий получава магистърска степен и квалификация за ръководител на проекти и програми през 2013 г. По време на магистърската си програма той се запознава с Пътната карта на проекта (GPM Deutsche Gesellschaft für Projektmanagement e. V.) и Spiral Dynamics.
Валерий е автор на изследване на несигурността на V.U.C.A. концепция, използваща спирална динамика и математическа статистика в психологията, и 38 международни проучвания.

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