AI and Automation of Work

Describes the phenomenon of jobs disappearing and new jobs being created during technological advancements, often with temporary disruption and shifting employment demographics.

The belief that there is a fixed amount of work available, leading to the fear that automation will eliminate jobs without creating new ones.

A type of automation where software learns from data and generates creative outputs like text, code, or images, significantly changing the landscape of work.

The rapid adoption of new technologies and processes within a company, often without official approval.

The belief that the current wave of automation driven by generative AI is fundamentally different from previous waves, potentially leading to more lasting and unforeseen impacts on the workforce.

The temporary pain and disruption experienced as workers adapt to new jobs and technologies during periods of automation. It may involve retraining, relocation, or unemployment.

The period of transition where people adjust to new roles and technologies, sometimes leading to social and economic challenges.

The historical changes in how work is done due to advancements in technology, such as the rise of factories, computers, and now generative AI.

The paradoxical effect of automation where increased efficiency leads to higher demand for the same goods or services.

A technological innovation that made clerical work faster and more efficient, leading to a significant increase in the number of clerical jobs.

The ability to produce multiple copies of a document using carbon paper.

A machine that automated accounting tasks, like adding numbers, making accounting more efficient.

This term refers to how technology can make tasks like adding and calculating more convenient and efficient, leading to an increase in the number of tasks performed.

The idea that automation doesn't necessarily reduce employment. Instead, it might lead to new job opportunities and economic growth.

The early electronic spreadsheet program that revolutionized data analysis and allowed people to perform complex calculations quickly.

The widespread adoption of personal computers and spreadsheet software like Excel led to a significant increase in accounting jobs.

The rapid adoption of a technology, with a large user base acquired in a short time frame.

New technology often enables people to accomplish tasks more efficiently and produce more with the same resources.

The act of applying a new tool or technology to existing processes without fundamentally changing the way work is done.

When the adoption of a new technology leads to fundamental changes in how work is done and what is produced.

The process of automating tasks previously done by humans through the use of computers and software.

The gradual and continuous adoption of new technologies that leads to widespread changes in our society and work.

When new technology enables people to do significantly more work than before, leading to potential increased output.

LLMs are seen as a general purpose technology that can potentially replace many existing vertical applications.

LLMs are still in an early phase of development and require significant time to be fully implemented.

LLMs may lead to the creation of new applications and solutions by unbundling existing ones.

LLMs can generate incorrect or hallucinated responses because they do not truly understand the information they process.

LLMs can be viewed as tools for pattern recognition, finding answers that are likely to match patterns in human responses to similar queries.

LLMs can produce different outputs depending on the specific prompt and training data used.

Prompt engineering is a critical aspect of utilizing LLMs to optimize their output and obtain desired results.

The future of LLMs may involve a shift towards more user-friendly interfaces, such as GUIs, rather than relying solely on prompt boxes.

The tendency of AI models to produce outputs that appear correct but lack true understanding, relying on patterns and correlations from training data.

A mistake made when relying on AI generated content, often due to misunderstanding its limitations, resulting in using incorrect information as a basis for decisions.

The point where AI reaches a capability similar to or exceeding human intelligence, with implications for work, society, and ethics.

The tendency to think that technology alone can solve complex problems, often overlooking the complexity of human systems and behavior. This approach ignores the nuances of real people, organizations, and the economy.