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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.

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chaatị dị iche iche Mmekọrịta

VUCA

Critical uru nke mmekọrịta ọnụọgụ

Ngalaba nkịtị, site na William Stel r = 0.0874

Ntinye na-abụghị ọrụ, site na Spearman r = 0.0039

Gosiputa nsonaazụ > r = 0.0874

Nkesa	Na-abụghị nkịtị	Nke kwesiri	Nke kwesiri	Nke kwesiri	Nke kwesiri	Nke kwesiri	Nke kwesiri	Nke kwesiri
Ajụjụ niile Ajụjụ niile 1) Nchedo (ole ka ị kwenyere ma ọ bụ kwenye?) 2) Njikwa (ole ka ị kwenyere ma ọ bụ kwenye?)
1) Nchedo (ole ka ị kwenyere ma ọ bụ kwenye?)
Answer 1	-	Na-adịghị ike mma 0.0647	Na-adịghị ike na-adịghị mma -0.0402	Na-adịghị ike mma 0.1238	Na-adịghị ike na-adịghị mma -0.1305	Na-adịghị ike mma 0.0097	Na-adịghị ike na-adịghị mma -0.0535	Na-adịghị ike mma 0.0344
Answer 2	-	Na-adịghị ike mma 0.0497	Na-adịghị ike mma 0.0373	Na-adịghị ike mma 0.0295	Na-adịghị ike na-adịghị mma -0.0054	Na-adịghị ike na-adịghị mma -0.0046	Na-adịghị ike na-adịghị mma -0.0176	Na-adịghị ike na-adịghị mma -0.0596
Answer 3	-	Na-adịghị ike na-adịghị mma -0.0307	Na-adịghị ike na-adịghị mma -0.0330	Na-adịghị ike mma 0.0063	Na-adịghị ike mma 0.0822	Na-adịghị ike na-adịghị mma -0.0127	Na-adịghị ike na-adịghị mma -0.0159	Na-adịghị ike na-adịghị mma -0.0149
Answer 4	-	Na-adịghị ike na-adịghị mma -0.0069	Na-adịghị ike mma 0.0432	Na-adịghị ike mma 0.0293	Na-adịghị ike na-adịghị mma -0.0238	Na-adịghị ike na-adịghị mma -0.0470	Na-adịghị ike na-adịghị mma -0.0193	Na-adịghị ike mma 0.0315
Answer 5	-	Na-adịghị ike mma 0.0069	Na-adịghị ike na-adịghị mma -0.0237	Na-adịghị ike na-adịghị mma -0.0152	Na-adịghị ike mma 0.0131	Na-adịghị ike na-adịghị mma -0.0018	Na-adịghị ike mma 0.0608	Na-adịghị ike na-adịghị mma -0.0308
Answer 6	-	Na-adịghị ike na-adịghị mma -0.0388	Na-adịghị ike na-adịghị mma -0.0576	Na-adịghị ike na-adịghị mma -0.1067	Na-adịghị ike mma 0.0816	Na-adịghị ike mma 0.0191	Na-adịghị ike mma 0.0491	Na-adịghị ike mma 0.0162
Answer 7	-	Na-adịghị ike na-adịghị mma -0.0360	Na-adịghị ike mma 0.0619	Na-adịghị ike na-adịghị mma -0.0510	Na-adịghị ike na-adịghị mma -0.0486	Na-adịghị ike mma 0.0403	Na-adịghị ike mma 0.0061	Na-adịghị ike mma 0.0322
2) Njikwa (ole ka ị kwenyere ma ọ bụ kwenye?)
Answer 8	-	Na-adịghị ike mma 0.0254	Na-adịghị ike mma 0.0424	Na-adịghị ike mma 0.1084	Na-adịghị ike mma 0.0601	Na-adịghị ike na-adịghị mma -0.0356	Na-adịghị ike na-adịghị mma -0.1149	Na-adịghị ike na-adịghị mma -0.0695
Answer 9	-	Na-adịghị ike mma 0.0118	Na-adịghị ike na-adịghị mma -0.0446	Na-adịghị ike na-adịghị mma -0.0559	Na-adịghị ike mma 0.0325	Na-adịghị ike mma 0.0961	Na-adịghị ike na-adịghị mma -0.0228	Na-adịghị ike na-adịghị mma -0.0252
Answer 10	-	Na-adịghị ike mma 0.0535	Na-adịghị ike na-adịghị mma -0.0376	Na-adịghị ike na-adịghị mma -0.0089	Na-adịghị ike na-adịghị mma -0.0281	Na-adịghị ike na-adịghị mma -0.0308	Na-adịghị ike mma 0.0262	Na-adịghị ike mma 0.0309
Answer 11	-	Na-adịghị ike mma 0.0157	Na-adịghị ike mma 0.0103	Na-adịghị ike na-adịghị mma -0.0089	Na-adịghị ike na-adịghị mma -0.0482	Na-adịghị ike mma 0.0039	Na-adịghị ike mma 0.0010	Na-adịghị ike mma 0.0318
Answer 12	-	Na-adịghị ike na-adịghị mma -0.0377	Na-adịghị ike mma 0.0444	Na-adịghị ike mma 0.0304	Na-adịghị ike mma 0.0435	Na-adịghị ike na-adịghị mma -0.0755	Na-adịghị ike mma 0.0475	Na-adịghị ike na-adịghị mma -0.0432
Answer 13	-	Na-adịghị ike na-adịghị mma -0.1232	Na-adịghị ike na-adịghị mma -0.0353	Na-adịghị ike na-adịghị mma -0.0230	Na-adịghị ike mma 0.0330	Na-adịghị ike na-adịghị mma -0.0105	Na-adịghị ike mma 0.0883	Na-adịghị ike mma 0.0191
Answer 14	-	Na-adịghị ike mma 0.0124	Na-adịghị ike mma 0.0425	Na-adịghị ike na-adịghị mma -0.0572	Na-adịghị ike na-adịghị mma -0.1125	Na-adịghị ike mma 0.0256	Na-adịghị ike mma 0.0326	Na-adịghị ike mma 0.0763

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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

Onye nwe ngwaahịa Saas Passe Sdtest®

Valerii ruru eru dị ka onye ọkà mmụta sayensị na-elekọta mmadụ na 1993 ma tinyekwara ihe ọmụma ya na njikwa ọrụ.
Valerii nwetara ogo nke nna ukwu na oru ngo na ntozu nke 2013. N'oge atumatu nke onye nwe ya (GPM deutsche geslellchaft fü.) ma gbaa ọsọ.
Valerii mere nnwale di iche di iche di iche di iche ma jiri ihe omuma ya na ahụmịhe ya mee ihe iji gbanwee ụdị SDTEC dị ugbu a.
Valerii bụ onye dere nyocha nke Vs.UC.A. Echiche na-eji ike gburugburu na ọnụ ọgụgụ mgbakọ na mwepụ na akparamaagwa, ihe karịrị ntuli abụọ mba ofesi.

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