Generative AI: what is it good for?

The Economist
29 May 202306:19

TLDRThe transcript discusses the evolution and impact of generative AI, highlighting the technical breakthrough with Google's Transformer model in 2017 and the subsequent launch of GPT 3.5, which saw rapid adoption. The strengths of these AI models include processing vast unlabeled data and performing well on various tasks like text generation and pattern matching. However, weaknesses such as lack of transparency and reliability issues are noted, emphasizing the need for caution in fields requiring factual accuracy. The economic implications of AI are also explored, suggesting that full process automation is key to realizing its potential in boosting economic growth.


  • ๐Ÿš€ Generative AI is the driving force behind many new online tools, used globally for a variety of tasks, from answering queries on numerous topics to generating realistic images from text prompts.
  • ๐ŸŒŸ A significant advancement in AI occurred in 2017 with Google's development of the Transformer model, which improved system performance, particularly in producing longer, coherent outputs like text and computer code.
  • ๐Ÿ’ฌ The launch of GPT 3.5 as chat GPT marked a turning point, making AI accessible to the public and leading to rapid adoption and innovative uses.
  • ๐Ÿ“ˆ Large language models excel at processing vast amounts of unlabeled data, providing a broad understanding derived from hundreds of billions of words.
  • โœ๏ธ AI is particularly adept at text generation, pattern matching, and style transfer, as exemplified by its ability to write a love letter in a variety of complex and creative styles.
  • ๐ŸŽ“ AI's proficiency in standardized tests is notable, with instances of passing the U.S medical licensing exam and legal tests, showcasing its potential in specialized domains.
  • ๐Ÿ”ง One of the major opportunities for AI is in writing code, where its immediate feedback mechanism allows for quick identification and correction of errors.
  • ๐Ÿ”’ A key weakness of AI systems is the lack of transparency; they operate as 'black boxes,' making it difficult to understand their internal processes and decision-making.
  • ๐Ÿ” AI is not yet adept at discovering new facts, which is crucial for roles in government, intelligence services, and journalism that require accurate and verified information.
  • ๐Ÿ“ˆ Economic models suggest that for AI to significantly boost productivity and economic growth, it must automate entire processes, as partial automation may not yield the desired effects.
  • ๐ŸŒ The potential impact of generative AI on the workforce is substantial, with estimates suggesting that up to 50% of tasks performed by around 20% of the US workforce could be affected in the coming years.

Q & A

  • What is the significance of generative AI in the current technological landscape?

    -Generative AI represents a wave of new online tools that have gained widespread use globally. It encompasses a range of capabilities, from answering queries on various topics in conversational language to generating realistic images from text prompts. This technology has seen significant advancements and has become more accessible, leading to its rapid adoption and innovative applications.

  • What major breakthrough occurred in 2017 that improved AI systems?

    -In 2017, researchers at Google developed a more effective retention mechanism called the Transformer. This innovation is represented by the 'T' in GPT (Generative Pre-trained Transformer) and it allowed AI systems to produce more coherent and longer pieces of output, whether in text or computer code.

  • How did GPT-3.5 change the perception of AI?

    -GPT-3.5, launched as ChatGPT, made AI more visible by offering a chatbot interface that anyone could sign up to use. Within the first two months, it had over 100 million users, marking the fastest adoption of any consumer tech in history. This widespread use showcased the technology's capabilities and solidified its place in the public consciousness.

  • What are some of the strengths of large language models like GPT?

    -Large language models excel at processing vast amounts of unlabeled data, providing a broad understanding derived from hundreds of billions of words. They are particularly adept at generating convincing text, pattern matching, style transfer, and have even shown proficiency in standardized tests, demonstrating their versatility in various text-related tasks.

  • What is one of the notable applications of generative AI mentioned in the transcript?

    -One notable application discussed is writing code. Generative AI can assist in coding tasks, providing immediate feedback if the code is incorrect, which is beneficial for refining and debugging the code quickly.

  • What is a primary weakness of current AI systems?

    -A primary weakness is the lack of transparency and understanding of how these complex systems operate. They are often seen as 'black boxes' with billions of attention weights that are difficult to interpret, leading to challenges in fully comprehending their decision-making processes.

  • How does the transcript suggest AI systems may not be ideal for certain jobs?

    -The transcript indicates that AI systems may not be well-suited for jobs that require discovering new facts, as they are not inherently designed to verify the accuracy of the information they produce. This is particularly important in fields like government, intelligence services, and journalism, where the reliability of facts is paramount.

  • What potential economic impact does generative AI have according to the transcript?

    -Generative AI is expected to significantly impact economic activity, with estimates suggesting that around 20% of the US workforce could have about 50% of their tasks affected by AI in the coming years. This indicates a substantial shift in how day-to-day tasks are performed and the potential for increased efficiency in various industries.

  • What does the transcript imply about the future of AI and economic growth?

    -The transcript suggests that for economic growth to increase exponentially, the entire process needs to be automated. Since human involvement often slows down the process, AI could play a crucial role in achieving full automation and thus, driving an 'intelligent explosion' of economic growth.

  • How does the transcript relate the discussion of AI to the classic paperclip maximizer thought experiment?

    -The transcript humorously refers to the paperclip maximizer as an example of how AI, without human intervention, might continue to optimize a specific task to the exclusion of all else. It highlights the importance of human guidance in ensuring that AI development aligns with our broader goals and interests.

  • What can Economist subscribers do to access more content like the discussed AI discussion?

    -Economist subscribers can watch the full 45-minute discussion on the risks and opportunities of AI by clicking on the provided link, which offers exclusive access to in-depth conversations on relevant topics.



๐Ÿค– Advancements in Generative AI

This paragraph discusses the evolution of generative AI and its impact on society. It highlights the development of AI tools capable of answering a wide range of queries and generating realistic images. The introduction of the Transformer model by Google researchers in 2017 is noted as a significant breakthrough, leading to more coherent and longer outputs. The launch of GPT 3.5 as a chatbot is emphasized, with its rapid adoption by millions of users. The strengths of large language models in processing vast amounts of unlabeled data are praised, as well as their ability to generate convincing text and perform well on standardized tests. Challenges such as the lack of transparency and understanding of the AI's decision-making processes are also mentioned, along with concerns about the reliability of AI-generated facts for tasks that require accuracy.


๐Ÿš€ The Economic Impact of AI Innovation

The second paragraph delves into the economic implications of AI innovation. It discusses the concept of an 'intelligent explosion' and the need to automate entire processes for exponential economic growth. The human element is identified as a potential rate-determining step that could slow down progress. The paragraph also touches on the use of AI in research, suggesting that while it aids in scientific advancement, it has not yet reached its full potential. The discussion concludes with a hypothetical scenario where AI would become super intelligent, if not for human intervention, hinting at the balance between AI development and human oversight.



๐Ÿ’กGenerative AI

Generative AI refers to artificial intelligence systems that can create new content, such as text, images, or music. In the context of the video, it is the driving force behind the latest online tools that are being used by millions globally. These tools can perform a variety of tasks, from answering queries on a wide range of topics to generating realistic-looking photographs based on textual descriptions.


The Transformer is a type of retention mechanism developed by researchers at Google that serves as the foundation for the 'T' in GPT (Generative Pre-trained Transformer) models. It is a significant advancement in AI that enables systems to handle longer and more complex data sequences, leading to improved performance in tasks like language translation, text generation, and more.

๐Ÿ’กGPT 3.5

GPT 3.5 is a more advanced version of the Generative Pre-trained Transformer model, which is capable of understanding and generating human-like text based on the input it receives. It represents a significant leap in AI technology, as it can be used as a chatbot and has been adopted rapidly by millions of users, showcasing the potential of AI in consumer technology.

๐Ÿ’กLarge Language Models

Large language models are AI systems that process and analyze vast amounts of textual data to generate human-like language. These models are capable of understanding and producing text across a wide range of topics and styles, making them valuable tools for various applications, from writing assistance to content creation.

๐Ÿ’กUnlabeled Data

Unlabeled data refers to data that has not been categorized or tagged with specific information. In the context of AI, large language models can process such data to learn patterns and generate outputs without the need for human-provided labels, which was traditionally required for training AI systems.

๐Ÿ’กPattern Matching

Pattern matching is the process of identifying and utilizing regularities in data to make predictions or generate outputs. In AI, it is a crucial skill that allows systems to recognize and replicate various patterns, such as language structures, styles, or themes.

๐Ÿ’กStandardized Tests

Standardized tests are assessments that are administered and scored in a consistent manner across different settings. They are used to evaluate knowledge, skills, or abilities in a standardized way. In the context of AI, the ability to pass such tests indicates the system's capacity to understand and apply complex knowledge at a level comparable to human experts.

๐Ÿ’กCode Writing

Code writing, or coding, refers to the process of creating computer programs by writing and organizing source code. In the context of AI, the ability to write code suggests that the system can generate functional and accurate programming code, which can be immediately tested and corrected, providing a tight feedback loop for improvement.


Transparency in AI refers to the ability to understand and interpret the decision-making processes and mechanisms behind an AI system's actions. A transparent system allows users to see how it arrives at its conclusions, which is essential for trust, accountability, and ethical considerations.

๐Ÿ’กEconomic Activity

Economic activity refers to the actions and initiatives that people undertake to produce, distribute, and consume goods and services in an economy. In the context of AI, it relates to the potential impact of AI technologies on various industries, jobs, and economic growth.

๐Ÿ’กInnovation Economics

Innovation economics is the study of how new ideas, technologies, and processes are developed and implemented within an economy, and the impact they have on economic growth, productivity, and competitiveness. It examines the factors that drive innovation and the conditions that foster or hinder it.

๐Ÿ’กIntelligent Explosion

An intelligent explosion is a hypothetical scenario where artificial intelligence (AI) develops at an exponential rate, leading to rapid advancements and transformations in technology, society, and the economy. It is often discussed in the context of the potential risks and opportunities associated with the development of superintelligent AI.


Generative AI is the technology behind a wave of new online tools used by millions around the world.

Some AI tools can answer queries on a vast range of topics in conversational language.

Other AI tools can generate realistic-looking photographs from short text prompts.

The technology has seen significant improvements, making it more widely deployed.

The introduction of the Transformer model by Google researchers in 2017 marked a technical breakthrough.

GPT 3.5, launched as Chat GPT, allowed anyone to sign up and interact with the AI.

100 million people tried Chat GPT within the first two months, marking the fastest adoption of consumer tech in history.

Large language models can process vast amounts of unlabeled data, providing a broad understanding.

AI is adept at generating convincing text and is skilled at pattern matching and style transfer.

AI has shown the ability to pass standardized tests, such as the U.S. medical licensing exam.

One of the significant opportunities for AI is in writing code, with immediate feedback on errors.

AI's main weakness is the lack of transparency and understanding of its complex system.

AI is not well-suited for jobs requiring the discovery of new facts due to its current limitations.

The reliability of AI models needs improvement before they can automate large processes and businesses.

Economists predict that around 20% of the US workforce could have about 50% of their tasks affected by generative AI in the next few years.

To achieve exponential economic growth, the entire process needs to be automated, not just a part of it.

AI is used to assist with research, but it cannot fully automate the process yet.

The pace of progress continues as it has been, with humans playing a crucial role in the development of AI.