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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

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Critical uru nke mmekọrịta ọnụọgụ

Ngalaba nkịtị, site na William Stel r = 0.0759

Ntinye na-abụghị ọrụ, site na Spearman r = 0.0031

Gosiputa nsonaazụ > r = 0.0759

Nkesa	Na-abụghị nkịtị	Nke kwesiri	Na-abụghị nkịtị	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 0.0686	Na-adịghị ike na-adịghị mma -0.0028	Na-adịghị ike mma 0.1019	Na-adịghị ike na-adịghị mma -0.1091	Na-adịghị ike na-adịghị mma -0.0035	Na-adịghị ike na-adịghị mma -0.0537	Na-adịghị ike mma 0.0211
Answer 2	-	Na-adịghị ike mma 0.0264	Na-adịghị ike mma 0.0011	Na-adịghị ike mma 0.0402	Na-adịghị ike na-adịghị mma -0.0269	Na-adịghị ike mma 0.0477	Na-adịghị ike na-adịghị mma -0.0062	Na-adịghị ike na-adịghị mma -0.0667
Answer 3	-	Na-adịghị ike na-adịghị mma -0.0132	Na-adịghị ike na-adịghị mma -0.0459	Na-adịghị ike na-adịghị mma -0.0037	Na-adịghị ike mma 0.0453	Na-adịghị ike na-adịghị mma -0.0068	Na-adịghị ike na-adịghị mma -0.0071	Na-adịghị ike mma 0.0135
Answer 4	-	Na-adịghị ike mma 0.0196	Na-adịghị ike mma 0.0043	Na-adịghị ike mma 0.0139	Na-adịghị ike na-adịghị mma -0.0383	Na-adịghị ike na-adịghị mma -0.0353	Na-adịghị ike na-adịghị mma -0.0108	Na-adịghị ike mma 0.0504
Answer 5	-	Na-adịghị ike mma 0.0051	Na-adịghị ike na-adịghị mma -0.0098	Na-adịghị ike na-adịghị mma -0.0169	Na-adịghị ike mma 0.0501	Na-adịghị ike mma 0.0005	Na-adịghị ike mma 0.0345	Na-adịghị ike na-adịghị mma -0.0544
Answer 6	-	Na-adịghị ike na-adịghị mma -0.0367	Na-adịghị ike na-adịghị mma -0.0542	Na-adịghị ike na-adịghị mma -0.0768	Na-adịghị ike mma 0.0793	Na-adịghị ike na-adịghị mma -0.0082	Na-adịghị ike mma 0.0545	Na-adịghị ike mma 0.0107
Answer 7	-	Na-adịghị ike na-adịghị mma -0.0612	Na-adịghị ike mma 0.1082	Na-adịghị ike na-adịghị mma -0.0539	Na-adịghị ike na-adịghị mma -0.0045	Na-adịghị ike na-adịghị mma -0.0020	Na-adịghị ike na-adịghị mma -0.0084	Na-adịghị ike mma 0.0248
2) Njikwa (ole ka ị kwenyere ma ọ bụ kwenye?)
Answer 8	-	Na-adịghị ike mma 0.0279	Na-adịghị ike mma 0.0226	Na-adịghị ike mma 0.0680	Na-adịghị ike mma 0.0472	Na-adịghị ike na-adịghị mma -0.0219	Na-adịghị ike na-adịghị mma -0.0732	Na-adịghị ike na-adịghị mma -0.0540
Answer 9	-	Na-adịghị ike mma 0.0106	Na-adịghị ike na-adịghị mma -0.0276	Na-adịghị ike na-adịghị mma -0.0434	Na-adịghị ike mma 0.0318	Na-adịghị ike mma 0.0883	Na-adịghị ike na-adịghị mma -0.0171	Na-adịghị ike na-adịghị mma -0.0466
Answer 10	-	Na-adịghị ike mma 0.0190	Na-adịghị ike na-adịghị mma -0.0300	Na-adịghị ike na-adịghị mma -0.0366	Na-adịghị ike na-adịghị mma -0.0025	Na-adịghị ike na-adịghị mma -0.0111	Na-adịghị ike mma 0.0466	Na-adịghị ike mma 0.0122
Answer 11	-	Na-adịghị ike mma 0.0292	Na-adịghị ike mma 0.0087	Na-adịghị ike mma 0.0121	Na-adịghị ike na-adịghị mma -0.0572	Na-adịghị ike na-adịghị mma -0.0066	Na-adịghị ike na-adịghị mma -0.0174	Na-adịghị ike mma 0.0402
Answer 12	-	Na-adịghị ike na-adịghị mma -0.0094	Na-adịghị ike mma 0.0284	Na-adịghị ike mma 0.0510	Na-adịghị ike mma 0.0344	Na-adịghị ike na-adịghị mma -0.0755	Na-adịghị ike mma 0.0132	Na-adịghị ike na-adịghị mma -0.0292
Answer 13	-	Na-adịghị ike na-adịghị mma -0.1112	Na-adịghị ike na-adịghị mma -0.0444	Na-adịghị ike na-adịghị mma -0.0076	Na-adịghị ike mma 0.0029	Na-adịghị ike mma 0.0123	Na-adịghị ike mma 0.0772	Na-adịghị ike mma 0.0237
Answer 14	-	Na-adịghị ike mma 0.0009	Na-adịghị ike mma 0.0580	Na-adịghị ike na-adịghị mma -0.0327	Na-adịghị ike na-adịghị mma -0.0754	Na-adịghị ike na-adịghị mma -0.0265	Na-adịghị ike mma 0.0027	Na-adịghị ike mma 0.0832

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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 SDTEST®

Valerii tozuru oke dị ka onye nkuzi mmekọrịta mmadụ na mmụọ na 1993 wee tinye ihe ọmụma ya n'ọrụ na njikwa ọrụ.
Valerii nwetara nzere Master na oru ngo na mmemme njikwa mmemme na 2013. N'oge mmemme Nna-ukwu ya, ọ maara Project Roadmap (GPM Deutsche Gesellschaft für Projektmanagement e. V.) na Spiral Dynamics.
Valerii bụ onye dere nyocha nke ejighị n'aka nke V.U.C.A. echiche iji Spiral Dynamics na mgbakọ na mwepụ mgbakọ na mwepụ na akparamaagwa, na 38 mba ntuli aka.

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