AI vs. Generative AI: The Differences Explained

Artificial intelligence (AI) technology is quickly evolving. Since 2018, the global market has increased by more than $60 billion, and analysts at the investment banking company Goldman Sachs predict it will reach $200 billion by 2025 [1,2].

The world has already experienced AI at work thanks to chatbots like ChatGPT and image generators like DALL-E. In 2025 and beyond, generative AI will likely expand with new uses and applications that have the power to reshape various sectors, including entertainment and design. Although traditional AI and generative AI aren’t mutually exclusive, it’s essential to understand how they differ to ensure you apply the correct technology for various use cases in the workplace. 

In this article, you'll learn more about the difference between AI and generative AI, including how they work, what they do, and their advantages and disadvantages.

Artificial Intelligence (AI)

AI's roots span back to the 1950s when Alan Turing first published Computer Machinery and Intelligence, which later became a tool to measure computers' intelligence. In 1952, scientist Arthur Samuel created the first computer program to learn to play checkers, and AI has slowly developed ever since. 

At the time, computers could only execute commands and weren't capable of storing that information. Additionally, the technology costs were astronomical, with a single computer costing approximately $200,000 monthly to lease [3].

Modern technology has made incredible progress, with computer memory and speed facilitating the path forward. AI has garnered a lot of buzz, made great strides over the past decade, and continues to evolve quickly. 

In perhaps the first significant moment in modern AI, in 2016, Google’s AlphaGo artificial intelligence program bested international Go champion Lee Se-dol in a five-game match of the Chinese board game. Today, AI technology has become increasingly capable of handling speech recognition, image recognition, reading comprehension, and understanding language.

What is AI?

AI refers to a computer's ability to learn and make decisions independently without intervention from human experts. The field of artificial intelligence is a cross-discipline that includes elements of computer science, biology, mathematics and statistics, neuroscience, and philosophy.

While AI seeks to mimic human intelligence, AI research is also applied to study and solve real-world challenges using methods better suited to computers than humans. Although many modern AI-driven systems rely on methodologies such as neural networks that reflect the structure of the human brain, AI's decision-making processes are often different from humans and present their own unique benefits.

Read more: What Is Artificial Intelligence? Definition, Uses, and Types

How does AI work?

Much of the AI that people interact with every day is the result of machine learning (ML), which uses algorithms trained on datasets to power a computer's ability to understand information, "learn" from it and then make decisions. Before machine learning algorithms can become working machine learning models, though, humans must first direct them toward making the correct decisions when encountering input data by verifying and validating their initial answers.

For example, if the goal was to teach a computer to recognize the difference between a picture of a horse and a cow, humans would need to first help the machine identify the differences between them. Without initial direction from human experts, the algorithm doesn't necessarily know what to do with the input data and may even struggle to identify certain core features that define either animal. Over time, machine learning would allow the computer to learn the features inherent in each data set and apply them moving forward.

Several notable subsets of machine learning power much of AI today. Neural networks simulate the human brain to help machines learn using interconnected artificial neurons, which can help predict patterns. Deep learning uses multiple layers of artificial neurons that enable the machine to engage in sophisticated learning, such as that necessary for applications like self-driving vehicles, which must analyze factors like distance and depth for the machine to perform effectively.

Hear more about machine learning in this lecture from DeepLearning.AI's AI for Everyone course:

Advantages of AI

One of the primary reasons AI has gained so much traction over the past few years is its ability to improve our lives in various ways. Although many worry that AI will replace humans in multiple occupations, the technology cannot replace the empathy, creativity, and critical thinking people are capable of. Instead, it offers unique strengths that people can use in collaboration with their own to achieve improved results. 

Five essential advantages of AI include the following:

• Increased productivity and efficiency: Using AI to tackle lower-level, repetitive tasks allows people to focus on more complex tasks.

• Reduced errors: AI can handle certain tasks with flawless precision and greater accuracy than a human. For example, a computer could quickly parse through a large volume of data to derive the necessary information or locate mistakes. A human performing the same task would require more time and could inadvertently miss critical details and data. 

• Improved decision-making abilities: Humans ultimately make the decisions, applying context to the insights AI provides. However, AI efficiently delivers data-driven information that powers better decision-making. Additionally, AI may be able to remove human bias in some (but not all) areas, such as when approving loans or reviewing job applications. 

• Automation and 24/7 availability: Staying connected is easier with AI's ability to send reminders and auto-reply texts and emails. It can also handle otherwise monotonous tasks like data entry and collection and provide service day or night for an improved customer experience. 

• Improved health care: Just as AI can reduce human error in other fields, it can also enhance medical testing results by catching even the smallest irregularities that humans could potentially miss. For example, AI might note an anomaly in an X-ray or imaging scan, leading to potentially earlier diagnoses or better care. Additionally, wearable technology allows continual monitoring, and AI can support improved clinical care while aiding in making sound decisions regarding treatments and predicting health risks.

Read more: 5 Benefits of AI to Know (+ 3 Risks to Watch Out For)

Disadvantages of AI

Despite its many benefits, AI also has several potential downsides. It’s essential to consider these cons before investing in the technology. Five potential disadvantages include the following: 

• High implementation costs: To leverage the benefits, many companies will need to invest in costly hardware and other technology. The expense required for implementing AI may not be feasible for smaller businesses, and it may not produce an adequate return on the investment for a substantial amount of time.

• Wear and degradation: Machines break down with time and use. So, if you use an AI-powered machine in a manufacturing setting, the parts will eventually need replacing and require continual maintenance. Additionally, regular learning is critical to avoid using outdated AI.

• Lack of original thought: AI can’t duplicate human creativity. Although people can already use it in art, for example, it cannot "think" outside of the existing data pool it has to draw on. It lacks innovation and the necessary creativity to produce original ideas. 

• Cannot consider emotional ramifications: Humans have empathy and emotional intelligence, two qualities necessary to make certain critical decisions in both life and work. Machines work solely with the predefined rules you give them. Humans can apply kindness and compassion to make sounder decisions on sensitive issues or in areas like marketing, where you must consider buyers' emotions throughout the sales process.

• Ethical considerations: Just as AI can't factor emotions into decisions and insights, it also can't incorporate morality or ethics. Some of the leading concerns include data privacy, fair and impartial use of results, transparency among companies using AI regarding the principles they operate under, and willingness to collaborate with others toward creating ethically-minded AI regulations.

Generative artificial intelligence 

While traditional AI is excellent for accomplishing preset tasks, such as data analysis, pattern recognition, and predictive analytics, generative AI is capable of producing original content based on its inputs.

Like traditional AI, early forms of generative AI have been around for some time, but the technology has only gained sophistication and widespread use within the last few years. The first chatbots emerged in the 1960s. With the developments in machine learning and generative adversarial networks (GANs), generative AI has entered a new era. Today, generative AI can create realistic images, generate content such as graphics and text, answer questions, explain complex concepts, and convert language into code. 

ChatGPT's release in 2022 brought generative AI into the conversation. In 2025, many experts anticipate generative AI will become even more powerful, bringing AI-led transformation to various spaces. For example, we will likely see increased capabilities and capacity, more adept use of generative AI in product design, and greater integration in formats like video and audio. 

Read more: What Is Generative AI? Definition, Applications, and Impact

What is generative AI?

Generative AI is a type of artificial intelligence that uses large language models (LLMs), or machine learning models trained on massive data sets, to generate new content based on user inputs. Some people consider it the next generation of AI because, instead of relying on existing sources, it can produce new data, images, and other forms of content rather than just perform the same task repeatedly. 

Generative AI applications like ChatGPT, Microsoft Copilot, and Dall-E can produce human-like responses and generate original content. Engineers develop this technology using several approaches, including GANs, which include components for creating content and another component for evaluating that content for authenticity. 

In doing so, it provides feedback for the program to use to improve the content, continuing the process until the results are as realistic as possible. Autoencoders enhance the way generative AI programs store and process data while reducing noise. Developers incorporate variations into the encoding step of the process to help generative applications with content creation. 

Diffusion models, which DALL-E uses, train on images and apply the laws of diffusion to move pixels and blur the image to a static state. Doing so allows the program to learn by tracing the pixels back to the original image. For example, if you wanted DALL-E to generate an image of a tiger, it would take an existing image and use this process to create a new one. ChatGPT, on the other hand, uses a transformer-based model that focuses on the context and sequence found in data to analyze it and make predictions and translations or create new text.

Advantages of generative AI

Generative AI offers a significant array of abilities. It can respond to questions, revise content, correct code, and even create songs, among other things. Use cases apply across various industries, and developers continue exploring its advantages for numerous different businesses. Global research and consulting firm Gartner predicts that 30 percent of businesses will incorporate generative AI by 2025 [4].

Five significant advantages of generative AI include the following:

• Improved creativity and personalization: Traditional AI lacks generative AI’s ability to create new content. It can aid marketers in crafting personalized campaigns, creating engaging written, visual, and audio content, and making digital interactions more personal.

• Better customer service: Personalization can enhance the customer experience, as can the use of virtual assistants to provide 24/7 customer service. Generative AI also has the power to analyze customer data to identify trending topics and customer sentiment, which companies can use to provide a better experience.

• Time savings: Generative AI can perform data processing in real time and automate tasks, offering significant savings in both time and operational costs.

• Boost productivity: Generative AI can provide valuable employee support, including answering questions, finding information, and summarizing content. Additionally, because they are free from the repetitive and time-consuming tasks AI can handle, employees can focus more on other tasks. It can generate reports automatically to save time, minimize mistakes, and even help speed up the timeline for app development by making coding suggestions.

• Improved learning: Generative AI uses new data and feedback to refine its performance. This ability to engage in adaptive learning can help users learn more effectively, too. Models can adjust according to an individual's learning styles and preferences, enhancing education and knowledge discovery and summarizing complex information in an understandable manner.

Read more: Generative AI for Beginners: Understanding the Basics and Beyond

Disadvantages of generative AI

As with traditional AI, the ethical implementation and use of generative AI remains a concern. With generative AI, the potential for distributing intentionally or unintentionally harmful content, copyright issues, and data privacy concerns are among the potential disadvantages that must be addressed. Other potential drawbacks include the following: 

• Compromised brand reputation: Chatbots powered by generative AI can occasionally produce wrong, misleading, or offensive responses. This spotlights the potential for generative AI to have a harmful effect, which can significantly damage a brand's reputation.

• Inadequate sourcing: Generative AI can sometimes identify the sources it bases its results on, but not always. 

• Inappropriate use: Generative AI can be transformative when used responsibly, but it’s not the right choice for every situation. For example, any scenario that requires empathy or moral context or those where health and legality are on the line should have a human in charge, not AI.

• Possible bias: It’s easy to assume AI is always correct, but that’s not always the case, requiring careful use and sensitivity to the potential for bias. 

Taking the next steps with Coursera

Artificial intelligence is here to stay. Whether you’re interested in learning more about AI for personal reasons or your professional development, you can effectively deepen your understanding of AI with courses like AI For Everyone from DeepLearning.AI or the IBM Applied AI Professional Certificate. Alternatively, if you want to focus on generative AI, consider taking Google’s Introduction to Generative AI. You’ll find these courses and more on the Coursera platform.