Episode Numberr: L011

Titel: AGI Stages: From Narrow AI to Superintelligence

The development of Artificial Intelligence (AI) is progressing rapidly, with Artificial General Intelligence (AGI)—defined as cognitive abilities at least equivalent to human intelligence—coming increasingly into focus. But how can progress towards this human-like or even superhuman intelligence be objectively measured and managed?

In this episode, we illuminate a new, detailed framework proposed by leading AI researchers that defines clear AGI stages. This model does not view AGI as a binary concept but as a continuous path of performance and generality levels.

Key Concepts of the AGI Framework:

1. Performance and Generality: The framework classifies AI systems based on the depth of their capabilities (Performance) and the breadth of their application areas (Generality). The scale ranges from Level 1: Emerging to Level 5: Superhuman.

2. Current Status: Today's highly developed language models like ChatGPT are classified within this framework as Level 1 General AI (Emerging AGI). This is because they currently lack consistent performance across a broader spectrum of tasks required for a higher classification. Generally, most current applications fall under Weak AI (ANI) or Artificial Narrow Intelligence, which is specialized for specific, predefined tasks (e.g., voice assistants or image recognition).

3. Autonomy and Interaction: In addition to capabilities, the model also defines six Autonomy Levels (from AI as a tool up to AI as an agent), which become technically feasible with increasing AGI levels. The conscious design of human-AI interaction is crucial for responsible deployment.

4. Risk Management: Defining AGI in stages enables the identification of specific risks and opportunities for each phase of development. While "Emerging AGI" systems primarily present risks such as misinformation or faulty execution, higher stages increasingly focus on existential risks (X-risks).

Regulatory Context and the Future:

Parallel to technological advancement, regulation is progressing. The EU AI Act, the world's first comprehensive AI law, which provides for concrete prohibitions starting February 2025 against high-risk AI systems (such as social scoring), establishes a binding framework for human-centric and trustworthy AI.

Understanding the AGI stages serves as a valuable compass for navigating the complexity of AI development, setting realistic expectations for current systems, and charting a course towards a secure and responsible future of human-AI coexistence.

(Note: This podcast episode was created with the support and structuring provided by Google's NotebookLM.)

Episode Numberr: Q011

Titel: AGI Stages: From Narrow AI to Superintelligence

The development of Artificial Intelligence (AI) is progressing rapidly, with Artificial General Intelligence (AGI)—defined as cognitive abilities at least equivalent to human intelligence—coming increasingly into focus. But how can progress towards this human-like or even superhuman intelligence be objectively measured and managed?

In this episode, we illuminate a new, detailed framework proposed by leading AI researchers that defines clear AGI stages. This model does not view AGI as a binary concept but as a continuous path of performance and generality levels.

Key Concepts of the AGI Framework:

1. Performance and Generality: The framework classifies AI systems based on the depth of their capabilities (Performance) and the breadth of their application areas (Generality). The scale ranges from Level 1: Emerging to Level 5: Superhuman.

2. Current Status: Today's highly developed language models like ChatGPT are classified within this framework as Level 1 General AI (Emerging AGI). This is because they currently lack consistent performance across a broader spectrum of tasks required for a higher classification. Generally, most current applications fall under Weak AI (ANI) or Artificial Narrow Intelligence, which is specialized for specific, predefined tasks (e.g., voice assistants or image recognition).

3. Autonomy and Interaction: In addition to capabilities, the model also defines six Autonomy Levels (from AI as a tool up to AI as an agent), which become technically feasible with increasing AGI levels. The conscious design of human-AI interaction is crucial for responsible deployment.

4. Risk Management: Defining AGI in stages enables the identification of specific risks and opportunities for each phase of development. While "Emerging AGI" systems primarily present risks such as misinformation or faulty execution, higher stages increasingly focus on existential risks (X-risks).

Regulatory Context and the Future:

Parallel to technological advancement, regulation is progressing. The EU AI Act, the world's first comprehensive AI law, which provides for concrete prohibitions starting February 2025 against high-risk AI systems (such as social scoring), establishes a binding framework for human-centric and trustworthy AI.

Understanding the AGI stages serves as a valuable compass for navigating the complexity of AI development, setting realistic expectations for current systems, and charting a course towards a secure and responsible future of human-AI coexistence.

(Note: This podcast episode was created with the support and structuring provided by Google's NotebookLM.)

Episode number: L010

Titel: Is the Career Ladder Tipping? AI Automation, Entry-Level Jobs, and the Power of Training.

Generative AI is already drastically changing the job market and hitting entry-level workers in exposed roles hard. A new study, based on millions of payroll records in the US through July 2025, found that younger workers aged 22 to 25 experienced a relative employment decline of 13 percent in the most AI-exposed occupations. In contrast, older workers in the same occupations remained stable or even saw gains.

According to researchers, the labor market shock is concentrated in roles where AI automates tasks rather than merely augments them. Tasks that are codifiable and trainable, and often taken on as the first steps by junior employees, are more easily replaced by AI. Tacit knowledge, acquired by experienced workers over years, offers resilience.

This development has far-reaching consequences: The end of the career ladder is postulated, as the "lowest rung is disappearing". The loss of these entry-level positions (such as in software development or customer service) disrupts traditional competence development paths, as learning ladders for new entrants become thinner. Companies are therefore faced with the challenge of redesigning training programs to prioritize tasks that impart tacit knowledge and critical judgment.

In light of these challenges, targeted training and adoption become a crucial factor. The Google pilot program "AI Works" showed that just a few hours of training can double or even triple the daily AI usage of workers. Such interventions are key to closing the AI adoption gap, which exists particularly among older workers and women.

The training transformed participants' perception: while many initially considered AI irrelevant, users reported after the training that AI tools saved them an average of over 122 hours per year – exceeding modeled estimates. The increased usage and better understanding of application-specific benefits lead to the initial fear of AI being replaced by optimism, as employees learn to use the technology as a powerful tool for augmentation that creates space for more creative and strategic tasks.

In this episode, we illuminate how the AI revolution is redefining entry-level employment, why the distinction between automation and augmentation is critical, and what role continuous professional development plays in equipping workers with the necessary skills for the "new bottom rung".

(Note: This podcast episode was created with the support and structuring of Google's NotebookLM.)