Home/Blog/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.

Bohlale ba maiketsetso le pheletso ea tsoelo-pele

lichate Correlation

VUCA

Mahlonoko tseo ho leng bohlokoa ba Correlation coefficient

Kabo e tloaelehileng, ke William Searly Gosset (seithuti) r = 0.0763

Kabo e tloaelehileng e sa tloaelehang, ka Spearman r = 0.0031

Bonts'a feela liphetho feela > r = 0.0763

TLHOKOMELISO	Se seng se tloaelehileng	Tloaelehileng	Se seng se tloaelehileng	Tloaelehileng	Tloaelehileng	Tloaelehileng	Tloaelehileng	Tloaelehileng
Lipotso tsohle Lipotso tsohle 1) Polokeho (o lumellana le bokae kapa ha o lumellane?) 2) Taolo (o lumellana le bokae kapa o sa lumellane?)
1) Polokeho (o lumellana le bokae kapa ha o lumellane?)
Answer 1	-	Fokolang positive 0.0709	Fokolang mpe -0.0028	Fokolang positive 0.1106	Fokolang mpe -0.0994	Fokolang mpe -0.0085	Fokolang mpe -0.0583	Fokolang positive 0.0110
Answer 2	-	Fokolang positive 0.0280	Fokolang positive 0.0016	Fokolang positive 0.0403	Fokolang mpe -0.0300	Fokolang positive 0.0444	Fokolang mpe -0.0020	Fokolang mpe -0.0658
Answer 3	-	Fokolang mpe -0.0147	Fokolang mpe -0.0480	Fokolang mpe -0.0065	Fokolang positive 0.0458	Fokolang mpe -0.0093	Fokolang mpe -0.0046	Fokolang positive 0.0184
Answer 4	-	Fokolang positive 0.0166	Fokolang positive 0.0057	Fokolang positive 0.0186	Fokolang mpe -0.0386	Fokolang mpe -0.0325	Fokolang mpe -0.0142	Fokolang positive 0.0482
Answer 5	-	Fokolang positive 0.0043	Fokolang mpe -0.0109	Fokolang mpe -0.0187	Fokolang positive 0.0505	Fokolang mpe -0.0008	Fokolang positive 0.0361	Fokolang mpe -0.0520
Answer 6	-	Fokolang mpe -0.0401	Fokolang mpe -0.0583	Fokolang mpe -0.0839	Fokolang positive 0.0803	Fokolang mpe -0.0010	Fokolang positive 0.0548	Fokolang positive 0.0139
Answer 7	-	Fokolang mpe -0.0560	Fokolang positive 0.1141	Fokolang mpe -0.0539	Fokolang mpe -0.0116	Fokolang mpe -0.0001	Fokolang mpe -0.0108	Fokolang positive 0.0246
2) Taolo (o lumellana le bokae kapa o sa lumellane?)
Answer 8	-	Fokolang positive 0.0279	Fokolang positive 0.0172	Fokolang positive 0.0623	Fokolang positive 0.0562	Fokolang mpe -0.0184	Fokolang mpe -0.0748	Fokolang mpe -0.0562
Answer 9	-	Fokolang positive 0.0074	Fokolang mpe -0.0274	Fokolang mpe -0.0412	Fokolang positive 0.0323	Fokolang positive 0.0853	Fokolang mpe -0.0162	Fokolang mpe -0.0452
Answer 10	-	Fokolang positive 0.0176	Fokolang mpe -0.0320	Fokolang mpe -0.0396	Fokolang mpe -0.0023	Fokolang mpe -0.0136	Fokolang positive 0.0492	Fokolang positive 0.0170
Answer 11	-	Fokolang positive 0.0321	Fokolang positive 0.0105	Fokolang positive 0.0134	Fokolang mpe -0.0615	Fokolang mpe -0.0101	Fokolang mpe -0.0137	Fokolang positive 0.0401
Answer 12	-	Fokolang mpe -0.0132	Fokolang positive 0.0326	Fokolang positive 0.0613	Fokolang positive 0.0349	Fokolang mpe -0.0699	Fokolang positive 0.0061	Fokolang mpe -0.0382
Answer 13	-	Fokolang mpe -0.1088	Fokolang mpe -0.0423	Fokolang mpe -0.0096	Fokolang mpe -0.0013	Fokolang positive 0.0092	Fokolang positive 0.0769	Fokolang positive 0.0302
Answer 14	-	Fokolang positive 0.0043	Fokolang positive 0.0611	Fokolang mpe -0.0311	Fokolang mpe -0.0806	Fokolang mpe -0.0219	Fokolang positive 0.0017	Fokolang positive 0.0791

Export ho MS ipabola

Ts'ebetso ena e tla fumaneha likhethong tsa hau tsa VUCA

You can not only just create your poll in the ditefiso «V.U.C.A phuputsong moqapi» (with a unique link and your logo) but also you can earn money by selling its results in the ditefiso «Lebenkele la Poll», as already the authors of polls.

If you participated in VUCA polls, you can see your results and compare them with the overall polls results, which are constantly growing, in your personal account after purchasing ditefiso «My SDT»

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

Motsamaisi oa Motsamaisi oa Sas Pet Projeke ea Pedtest®

Valerii o ne a tšoaneleha joalo ka setsebi sa pelo ea sepakapaka ka 1993 mme ho tloha ha a sebelisitse tsebo ea hae tsamaisong ea morero.
Valerii o ile a fumana lengolo la master le projeka le mookameli oa lenaneo, o ile a tloaela lenaneo la projeke e. V.) le li-matla tsa spiral.
Valerii o nkile liteko tse fapaneng tsa spirals mme a sebelisa tsebo le boiphihlelo ba hae ba ho mamella mofuta oa hajoale oa sdtest.
Valerii ke mongoli oa ho hlahloba ho hloka botsitso ha v.u.c.a. Khopolo e sebelisang matla a matla a pediral le lipalo tsa lipalo ho Precchagy, lipolao tse fetang 20 tsa machaba.

Poso ena e na le 0 Maikutlo

Karabo ho

Hlakola karabo

Tlohela maikutlo a hau