Baile/Blagáil/Future of Jobs and Generative AI

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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Dáileadh Gnáth, le William Sealy Gosset (Mac Léinn) r = 0.0763

Dáileadh Neamh -Ghnáth, le Spearman r = 0.0031

Taispeáin torthaí amháin > r = 0.0763

Imdháileadh	Neamhghnách	Gnáth-	Neamhghnách	Gnáth-	Gnáth-	Gnáth-	Gnáth-	Gnáth-
Gach ceist Gach ceist 1) Sábháilteacht (cé mhéid a aontaíonn tú nó a n -aontaíonn tú?) 2) Rialú (Cé mhéid a aontaíonn tú nó a n -aontaíonn tú?)
1) Sábháilteacht (cé mhéid a aontaíonn tú nó a n -aontaíonn tú?)
Answer 1	-	Dearfach lag 0.0713	Diúltach lag -0.0025	Dearfach lag 0.1106	Diúltach lag -0.0997	Diúltach lag -0.0087	Diúltach lag -0.0586	Dearfach lag 0.0113
Answer 2	-	Dearfach lag 0.0287	Dearfach lag 0.0022	Dearfach lag 0.0402	Diúltach lag -0.0306	Dearfach lag 0.0441	Diúltach lag -0.0024	Diúltach lag -0.0654
Answer 2	-	Diúltach lag -0.0141	Diúltach lag -0.0475	Diúltach lag -0.0065	Dearfach lag 0.0452	Diúltach lag -0.0095	Diúltach lag -0.0050	Dearfach lag 0.0188
Answer 3	-	Dearfach lag 0.0171	Dearfach lag 0.0062	Dearfach lag 0.0186	Diúltach lag -0.0391	Diúltach lag -0.0327	Diúltach lag -0.0145	Dearfach lag 0.0485
Answer 4	-	Dearfach lag 0.0046	Diúltach lag -0.0106	Diúltach lag -0.0187	Dearfach lag 0.0501	Diúltach lag -0.0010	Dearfach lag 0.0358	Diúltach lag -0.0517
Answer 5	-	Diúltach lag -0.0396	Diúltach lag -0.0579	Diúltach lag -0.0839	Dearfach lag 0.0797	Diúltach lag -0.0012	Dearfach lag 0.0544	Dearfach lag 0.0142
Answer 6	-	Diúltach lag -0.0588	Dearfach lag 0.1109	Diúltach lag -0.0537	Diúltach lag -0.0085	Dearfach lag 0.0010	Diúltach lag -0.0088	Dearfach lag 0.0224
2) Rialú (Cé mhéid a aontaíonn tú nó a n -aontaíonn tú?)
Answer 7	-	Dearfach lag 0.0286	Dearfach lag 0.0178	Dearfach lag 0.0623	Dearfach lag 0.0555	Diúltach lag -0.0186	Diúltach lag -0.0752	Diúltach lag -0.0557
Answer 8	-	Dearfach lag 0.0081	Diúltach lag -0.0268	Diúltach lag -0.0412	Dearfach lag 0.0315	Dearfach lag 0.0850	Diúltach lag -0.0166	Diúltach lag -0.0447
Answer 8	-	Dearfach lag 0.0182	Diúltach lag -0.0315	Diúltach lag -0.0396	Diúltach lag -0.0029	Diúltach lag -0.0138	Dearfach lag 0.0488	Dearfach lag 0.0174
Answer 9	-	Dearfach lag 0.0325	Dearfach lag 0.0109	Dearfach lag 0.0134	Diúltach lag -0.0619	Diúltach lag -0.0103	Diúltach lag -0.0140	Dearfach lag 0.0404
Answer 10	-	Diúltach lag -0.0129	Dearfach lag 0.0328	Dearfach lag 0.0613	Dearfach lag 0.0346	Diúltach lag -0.0700	Dearfach lag 0.0059	Diúltach lag -0.0380
Answer 11	-	Diúltach lag -0.1120	Diúltach lag -0.0454	Diúltach lag -0.0095	Dearfach lag 0.0026	Dearfach lag 0.0106	Dearfach lag 0.0788	Dearfach lag 0.0272
Answer 12	-	Dearfach lag 0.0047	Dearfach lag 0.0614	Diúltach lag -0.0311	Diúltach lag -0.0810	Diúltach lag -0.0221	Dearfach lag 0.0015	Dearfach lag 0.0793

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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

Úinéir an Táirge SaaS Pet Project Sdtest®

Bhí Valerii cáilithe mar shíceolaí oideolaíoch sóisialta i 1993 agus ó shin i leith chuir sé a chuid eolais i bhfeidhm i mbainistíocht tionscadail.
Fuair Valerii céim mháistreachta agus cáilíocht an tionscadail agus an bhainisteora cláir in 2013. Le linn a chláir mháistir, bhí sé eolach ar threochlár Project (GPM Deutsche Gesellschaft Für Projektmanagement e. V.) agus dinimic Spiral.
Ghlac Valerii tástálacha éagsúla dinimic bíseach agus d'úsáid sé a chuid eolais agus taithí chun an leagan reatha de SDTest a oiriúnú.
Is é Valerii údar iniúchadh a dhéanamh ar neamhchinnteacht an V.U.C.A. Coincheap ag baint úsáide as dinimic bíseach agus staitisticí matamaiticiúla i síceolaíocht, níos mó ná 20 vótaíocht idirnáisiúnta.

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