What is Generative AI?
Generative AI refers to a type of artificial intelligence designed to create new content such as text, music, videos, codes, graphics, images, and so on. It produces content based on patterns learned, they are trained on large data sets and run on sophisticated algorithms to resemble human creativity and help in various tasks and fields, such as-
| Fields of use | How generative AI is used |
| Content creation | Used to create content for multiple uses like digital marketing, blogging, social media, customer communication, creative writing, e-commerce, etc., with its ability to create text, audio, and video content resembling human creativity. |
| Healthcare | Generative AI, with the help of all the data it has collected, is used to research various drugs and medicines, run simulations, and analyze reports of patients, increasing the efficiency of doctors and improving their precision. |
| Education | Researching lectures, optimizing study data, creating an education plan, providing quick help on questions, solving doubts, summarizing lectures etc. |
| Finance | Fraud detection, analyzing trading data and patterns, risk management, investment analysis, financial report generation, credit scoring, organizing large datasets, marketing research, and insights. |
| Software development | Code development, fixing errors, reviewing and optimizing the given code, testing. |
What is the history of generative AI?
EARLY HISTORY-
The 1950s were the time when the concept of machine learning and generative AI started to catch on. Then, later in the 1970s, the first stage of development began, and finally, in the 1980s, Neural networks gained attention with the backpropagation algorithm, enabling simple pattern recognition tasks.
FIRST GENERATIVE MODELS-
In the 1990s, Hidden Markov Models (HMMs) and Bayesian networks were used to generate text, music and speech. Later in 1997, LSTM was introduced to the world.
DEEP LEARNING-
In 2014, GANs (Generative Adversarial Networks) were introduced. This discovery allowed AI to generate realistic images and videos.
In 2017, transformers were introduced, giving it the ability to understand context over long sequences.
LARGE LANGUAGE MODELS AND MODERN AI-
In 2018, OpenAI released GPT, a transformer-based model for text generation.
From 2019 to 2021, other GPT models were introduced alongside the concept of generative AI, catching traction.
From 2022 to the present day, generative AI has become an accepted part of the modern world, with it being implemented in various other sectors and tons of new AI agents being created, such as Gemini, Dell-E, Deepseek, Stable Diffusion and the list goes on and on.
| Year | Milestone | Significance |
| 1950s | Turing Test | Concept of machine intelligence |
| 1980s | Neural networks + backpropagation | Early pattern recognition |
| 1997 | LSTM | Handling sequential data |
| 2014 | GANs | Realistic image/video generation |
| 2017 | Transformers | Context-aware text generation |
| 2018 | GPT | Modern text generation |
| 2021 | DALL·E, MidJourney | Text-to-image generation |
| 2023 | ChatGPT | Mass adoption of conversational AI |
| 2024+ | Multimodal AI | Generating text, image, audio, video together |
How does Generative AI works?
Take image generation as an example, this will be a step-by-step description of how generative AI works and functions.
Step 1: Data collection and learning phase-
For image generation, AI is fed large chunks of images alongside their text-based description, so when someone gives it a prompt, it will break it down and, from the data it has learned, it will build will understanding of the prompt and compare it to the description of images it has and create a new and original image.
Step 2: What are neural networks and transformers?
When you give a prompt to AI, it will break it down and understand it. For example, “A swimming Panda” will be broken down into “A”, “Swimming”, and “Panda”. Then it will match these to the image descriptions it has in its database. Then it will combine the results to give the output of the prompt with the help of the transformers.
Step 3: Tokens and context-
In the prompt, it broke down in the previous step; the pieces of the prompt are called tokens, and the understanding of the demand is called context.
Step 4: Feedback Mechanism-
When it gives out the result of a prompt, the user gives a rating of how well it executed the ask. It learns from multiple instances, and whenever it gets a good rating, it means the execution was correct, and it remembers that for the future.
Step 5: Reinforcement learning-
How AI learning works is whenever it gives out a result that doesn’t meet the criteria, it is told to try over and over again, and whenever it gets the answer correct, its score is increased. Its goal is to get the highest possible score, and over time, it learns and improves in the process.
Top generative AI tools and technologies 2025
Not all AI tools are built the same, and therefore, each AI tool has its own niche at which it performs the best. And the list is as follows-
For text and language, we have tools like ChatGPT, GPT-4, Jasper AI, copy.ai, and Bard (from Google). For image and visual content, we have DELL.E-3, Midjourney, Adobe Firefly, and Stable Diffusion. For video and animation generation, Sora, Synthesia, Runaway ML, and Pika Labs. For coding, GitHub Copilot, Tabnine, and Codewhisperer.
What is the future of Generative AI?
Generative AI is constantly being worked on and improved exponentially daily. After the release of ChatGPT in 2022, it has become a mainstream and accepted part of our day-to-day lives. And so what its future is looking like is a hard question to answer, as many AI ethics and safety communities are fighting back because it could pose a risk to humanity in the long run, as it may cause mass unemployment in various sectors and hypothetically become virtually superior at any human task, rendering human jobs useless.
On the other hand, it has revolutionized multiple industries and is helping in making consistent breakthroughs in medical, research, software, etc., and is also improving the efficiency of the workforce in those industries in parallel.
As of now, there seems to be no stopping, and companies like Microsoft, OpenAI, and Google are still planning to pump Billions of dollars into setting up even more AI databases and improving their intelligence and efficiency.
CONCLUSION-
Generative AI is still a super new breakthrough and has maximised everyone’s potential in countless fields. Avoiding it is no longer an option in the commercial space, and the best way for now is to adapt around it and find new ways to effectively use it. There is currently no saying if it will end us as the dot com bubble burst in 2002, or will become a mainstream part of our culture just like the internet.
FAQs-
- What are the applications of generative AI in industry?
It is being implemented in all types of industries as it greatly improves the company’s efficiency and is super cheap. While also having the ability to come up with new and creative uses.
- Ethical challenges in AI.
Currently, AI and machine learning is facing a lot of public backlash as it could lead to mass unemployment and monopoly over multiple creative sectors like digital art, content creation, writing, etc., while also some assumptions being made about the future risk of superintelligence.
- Is ChatGPT a generative AI?
Yes
- What is the current market size of AI?
As of 2025, it is $390 billion and is predicted to be about $1.8 trillion by 2030.
- Can Generative AI create images, videos, or music?
Yes absolutely, AI models like Gemini, Sora, DELL-E, ChatGPT, etc. are capable of creating images, videos, and music