Skip to main content

The Future of Work in the Age of AGI: Opportunities, Challenges, and Resistance

 In recent years, the rapid advancement of artificial intelligence (AI) has sparked intense debate about the future of work. As we edge closer to the development of artificial general intelligence (AGI), these discussions have taken on a new urgency. This post explores various perspectives on employment in a post-AGI world, including the views of those who may resist such changes. It follows on from others I've written on the impacts of these technologies.


The Potential for Widespread Job Displacement

Avital Balwit, an employee at Anthropic, argues in her article "My Last Five Years of Work" that AGI is likely to cause significant job displacement across various sectors, including knowledge-based professions. This aligns with research by Korinek (2024), which suggests that the transition to AGI could trigger a race between automation and capital accumulation, potentially leading to a collapse in wages for many workers.


Emerging Opportunities and Challenges

Despite the potential for job displacement, Tabbassum (2024) points out that the shift to AGI is expected to create new job roles, particularly in AI management, development, and ethical governance. While routine tasks may be automated, there will likely be an increased demand for skills related to overseeing and working alongside AGI systems.


Resistance to Change and Valid Concerns

It's crucial to acknowledge that not everyone views these potential changes positively. Many individuals and groups have valid concerns about the rapid advancement of AGI and its impact on employment:

1. Economic Inequality: There are fears that AGI could exacerbate existing economic disparities, with benefits primarily accruing to those who own and control the technology.

2. Skills Obsolescence: Workers, especially those in mid-career, worry about their skills becoming obsolete and the challenges of retraining for a radically different job market.

3. Cultural and Identity Issues: For many, work is deeply tied to identity and self-worth. The prospect of widespread unemployment or a fundamental shift in the nature of work could lead to significant psychological and social challenges.

4. Security and Privacy Concerns: As AGI systems become more prevalent in the workplace, there are valid worries about data privacy, surveillance, and the potential for these systems to be hacked or misused.

5. Ethical Considerations: Arel (2012) highlights the potential adversarial nature of AGI, especially if driven by reward-based reinforcement learning. This raises concerns about the ethical implications of relying on AGI for crucial decision-making in various industries.


Implications of Resistance

The resistance to AGI-driven changes in employment could have several significant implications:

1. Political Polarisation: The issue of AGI and employment could become a major political flashpoint, potentially leading to increased polarisation and social unrest.

2. Regulatory Challenges: Governments may face pressure to slow down or heavily regulate AGI development, which could impact the pace of technological progress.

3. Labour Movements: We might see the emergence of new labour movements focused on protecting human workers' rights in an AGI-dominated economy.

4. Education and Retraining Initiatives: There could be increased demand for large-scale education and retraining programs to help workers adapt to the changing job market.

5. Universal Basic Income (UBI) Debates: The prospect of widespread job displacement could fuel discussions about implementing UBI or other social safety net measures.


Balancing Progress and Concerns

While Balwit and others suggest that AGI itself might offer solutions to the challenges it creates, it's crucial to approach this transition with caution and empathy. We must consider the concerns of those who may be negatively impacted and work towards solutions that benefit society as a whole.


Conclusion

The advent of AGI promises to reshape our world in profound ways, with employment being a key area of impact. While there are potential opportunities for new forms of work and societal organisation, we must also address the valid concerns of those who may resist these changes.

Moving forward, it will be crucial to foster open dialogue between technologists, policymakers, workers, and other stakeholders. To be pragmatic, we may need to develop strategies that maximise the benefits of AGI while mitigating its potential negative impacts on employment and society.

The future of work in an AGI world remains uncertain, but by engaging in these discussions now and considering all perspectives, we can better prepare for the challenges and opportunities that lie ahead. It's clear that flexibility, lifelong learning, and a willingness to address societal concerns will be essential in navigating this brave new world.

Comments

Popular posts from this blog

The AI Dilemma and "Gollem-Class" AIs

From the Center for Humane Technology Tristan Harris and Aza Raskin discuss how existing A.I. capabilities already pose catastrophic risks to a functional society, how A.I. companies are caught in a race to deploy as quickly as possible without adequate safety measures, and what it would mean to upgrade our institutions to a post-A.I. world. This presentation is from a private gathering in San Francisco on March 9th with leading technologists and decision-makers with the ability to influence the future of large-language model A.I.s. This presentation was given before the launch of GPT-4. One of the more astute critics of the tech industry, Tristan Harris, who has recently given stark evidence to Congress. It is worth watching both of these videos, as the Congress address gives a context of PR industry and it's regular abuses. "If we understand the mechanisms and motives of the group mind, it is now possible to control and regiment the masses according to our will without their

What is happening inside of the black box?

  Neel Nanda is involved in Mechanistic Interpretability research at DeepMind, formerly of AnthropicAI, what's fascinating about the research conducted by Nanda is he gets to peer into the Black Box to figure out how different types of AI models work. Anyone concerned with AI should understand how important this is. In this video Nanda discusses some of his findings, including 'induction heads', which turn out to have some vital properties.  Induction heads are a type of attention head that allows a language model to learn long-range dependencies in text. They do this by using a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. For example, if a model is given the sequence "The cat sat on the mat," it can use induction heads to predict that the word "mat" will be followed by the word "the". Induction heads were first discovered in 2022 by a team of researchers at OpenAI. They found that induction heads were present in

Gemini LLM, an increase in benefits, and risks.

  Gemini LLM is being developed by Google Brain and Deepmind that was introduced at Google I/O 2023, and is expected to have a trillion parameters, like GPT-4. The project is using tens of thousands of Google's TPU AI chips for training, and could take months to complete. It may be introduced early next year. Gemini is being trained on a massive dataset of text, audio, video, images, and other media. This will allow it to 'understand' and respond to a wider range of input than previous LLMs. It will also be able to use other tools and APIs, which will make it more versatile and powerful. It's clearly looking to compete with a future GPT-5, this time Google are looking to get ahead of the curve. Training Gemini in a multimodal manner is significant because it allows the model to learn from a wider range of data. This should improve the model's accuracy and performance on a variety of tasks. For example, if Gemini is trained on both text and images, it can learn to as