Feature
With artificial intelligence models such as DeepSeek and Alibaba’s Qwen challenging the status quo of models like OpenAI’s Generative Pre-training Transformer (GPT), it raises the question of what exactly is an AI model?
People often get confused about this issue, because they don’t realize that underneath the AI application they’re using is a large amount of intelligent software that does the actual work. That’s the AI model.
Table of Contents
- What Is an AI Model?
- How Does an AI Model Work?
- How Are AI Models Trained?
- What Are the Main Features of AI Models?
- What Are the Benefits of AI Models?
- What Are Use Cases for AI Models?
- What Are the Most Popular AI Models?
What Is an AI Model?
An AI model is a mathematical framework trained on data to recognize patterns, make predictions and/or generate content by processing and analyzing inputs.
Let’s take ChatGPT as an example. ChatGPT is not an AI model in and of itself. It’s an interface. And that interface runs on top of the AI model — in this case, a particular type of large language model (LLM) called GPT.
GPT, broken down, means:
- Generative: Creates outputs, such as written content and images, based on an input.
- Pre-trained: Trained on large amounts of data, including websites like Wikipedia, news articles and books.
- Transformer: Stands for transformer, the deep learning architecture that powers the model.
Before the rise of DeepSeek and AI, it was taken as a given that AI model training would require a lot of data, a lot of expensive hardware to run it on and a lot of electricity to power it and cool it. That’s why only big companies like Meta and OpenAI — which partners with companies like Microsoft — were thought to be able to develop AI models.
According to McKinsey & Co, “Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt.” OpenAI, the company behind ChatGPT and DALL-E, has billions in funding from a number of donors. And DeepMind is a subsidiary of Alphabet, Google’s parent company.
“These companies employ some of the world’s best computer scientists and engineers. But it’s not just talent. When you’re asking a model to train using nearly the entire internet, it’s going to cost you. OpenAI hasn’t released exact costs, but estimates indicate that GPT-3 was trained on around 45 terabytes of text data — that’s about one million feet of bookshelf space, or a quarter of the entire Library of Congress — at an estimated cost of several million dollars. These aren’t resources your garden-variety start-up can access.”
Then DeepSeek and Qwen came along. DeepSeek, which is open source, manages to use much less data and requires much less processing, which in turn costs less and uses less power. It remains to be seen, however, the extent to which these lower-cost options can replace AI models trained on larger data sets.
Related Article: How to Evaluate and Select the Right AI Foundation Model for Your Business
How Does an AI Model Work?
An AI model functions by processing input data through a series of computational steps to identify patterns, make decisions or generate outputs. At its core, it involves creating algorithms and models that can analyze data, identify patterns and make decisions based on that analysis.
The development of an AI model typically involves several steps, including:
- Data collection: Gathering relevant data the AI will learn from.
- Data processing: Clearing and organizing data to ensure quality and consistency.
- Model selection: Choosing an appropriate algorithm or architecture suited to the task.
- Training: Feeding the processed data into the model and adjusting parameters so it can learn to perform the desired task.
- Evaluation: Assessing the model's performance using test data and metrics.
- Deployment: Implementing the model in a real-world environment where it can process new data.
How Are AI Models Trained?
AI models can be trained in a few different ways, including supervised, unsupervised or reinforcement.
Supervised Learning
Supervised learning is when humans provide labeled training data from which the algorithm can learn. When the model is provided with a new set of examples, it can use the learned features from the training stage to predict the outcome accurately. Because of this, supervised learning allows models to solve various problems with high accuracy.
Unsupervised Learning
In unsupervised learning, humans provide training data that is not labeled at all. The input is unsorted information or data, and grouping can be processed based on the shape, differences, similarities and different patterns of the data. Unsupervised learning is used for clustering, feature learning, anomaly detection and dimensionality reduction.
Reinforcement Learning
Reinforcement learning is where the models are allowed to learn from their own experiences and errors by interacting with an environment. It receives awards and penalties based on its actions and optimizes its actions to maximize long-term rewards.
What Are the Main Features of AI Models?
AI models have four major features, according to Abhishek Agrawal, senior software developing engineer at Amazon. These include:
- Pattern recognition in training data
- Mathematical algorithms that adjust based on feedback
- Neural networks that simulate human brain connections
- Parameters and weights that get optimized during training
These features can appear in several types of architectures, Agrawal explained, ranging from neural networks, transformer models like GPT, convolutional networks for images or recurrent networks for sequential data.
In addition, AI models are designed to be adaptive, scalable and context-aware, said Peter Lewis, founder and CEO at Strategic Pete. In some cases, AI models can offer edge computing features that run directly on a local device for speed, as well as “explainability layers that add a layer of transparency in decision-making, critical to onboarding for enterprises,” he said.
Once trained, said Benjamin Carle, CEO at FullStack Labs, AI models can analyze vast amounts of data in very little time. “They’re extremely adaptable, typically very scalable and can handle a large variety of tasks, depending on the data available.”
While some AI models are general, others can be more specialized, with some highly specialized models that tackle niche challenges. One example Carle pointed to is AlphaFold, developed by Alphabet. “AlphaFold is revolutionizing the medical field by accurately predicting complex protein folding structures,” he said.
What Are the Benefits of AI Models?
AI models support user-facing applications such as ChatGPT. That’s when the power and functionality of the AI model really comes into play.
One major benefit of AI models is automating routine or mundane tasks — often at speeds higher than a human can accomplish. But there’s a lot more to it than that.
“AI allows for mass personalization,” said Lewis. For instance, a retail AI model can analyze real-time customer behavior and change recommendations, adjust pricing and even change layouts of websites in milliseconds.
Agrawal broke down the benefits of AI models into three areas:
- Efficiency: AI models process large amounts of data quickly, automate repetitive tasks and run continuously without getting tired.
- Accuracy: AI models reduce human error, perform consistently and tend to improve as they get more data.
- Adaptability: AI models can be retrained for new tasks, get updated with new information and scale easily.
Webinar
Sep
25
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Register
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
Webinar
On demand
From Hype to High-Impact CX Strategies That Actually Scale
Turn buzzworthy AI and outsourcing trends into measurable CX wins with fresh 2025 data.
Watch Now
Webinar
On demand
Insights to Action Rethinking the Contact Center for Real Business Impact
Join our exclusive webinar to hear CX executives share their innovative strategies for transforming service delivery.
Watch Now
Webinar
On demand
Accelerating Healthcare Ops with AI
Sign up to see how your peers are putting AI to work — and making it stick.
Watch Now
In particular, AI models “save significant time and resources, especially when it comes to monotonous tasks,” Carle said. “They’re also exceptional at predictions, picking up patterns that humans might miss and using those patterns to make data-driven forecasts.”
What Are Use Cases for AI Models?
Use cases for AI models depend to a certain extent on the industry, as well as on the type of AI application based on the AI model.
“Natural Language Processing (NLP) models are among the most common AI models,” said Carle. “They include LLMs like ChatGPT, which ingest natural language and produce responses based on patterns in human conversation. NLP models are versatile, powering applications ranging from customer service chatbots to advanced linguistic data analysis and predictive text generation.”
Generally, use cases for AI models fall into several categories, Agrawal said. They include:
- Business: Customer service chatbots, sales prediction, fraud detection and market analysis
- Healthcare: Disease diagnosis, drug discovery, medical image analysis and patient care optimization
- Technology: Speech recognition, language translation, image/video processing and recommendation systems
- Science & Research: Climate modeling, genetic research, particle physics, and chemical synthesis
And new use cases are developing all the time, according to Lewis. These include dynamic content creation, where AI-driven marketing campaigns adapt based on real-time performance data, predictive healthcare, where AI analyzes genomic information to determine the risk of possible diseases and advanced robotics with logistical autonomous drones that are self-routed with one AI system and complex routing in real time.
Related Article: Evaluating Gemini 2.0 for Enterprise AI: Practical Applications and Challenges
What Are the Most Popular AI Models?
According to the 2024 State of AI Security Report, the most popular AI models include:
- GPT-3.5: Developed by OpenAI, which runs applications such as ChatGPT
- Ada: Also developed by OpenAI, which generates text and is faster and less expensive than GPT, but cannot do as much
- GPT-4o: A more advanced version of GPT that performs more tasks
- GPT-4: A more advanced version of GPT
- DALL-E: Also developed by OpenAI, which generates images
- Whisper: Also developed by OpenAI, which converts audio into text
- Curie: Also developed by OpenAI, which is based on GPT-3 and performs tasks such as sentiment analysis
- Llama: Developed by Meta, which performs text analysis and summarization, coding, translation generation, advanced reasoning and decision-making and multi
- step tasks
- DaVinci: Developed by OpenAI, which solves logic problems, performs cause and effect analysis, produces creative content and summarizes complex content
- Text to Speech: Developed by OpenAI, which converts text to natural-sounding, real-time audio
Other well-known models include Google’s BERT (Bidirectional Encoder Representations from Transformers), Anthropic’s Claude, AlphaGo from DeepMind, Midjourney, Stable Diffusion, Google’s Gemini and ResNet (Residual Networks).
New AI models — like DeepSeek and Qwen — continue to pop up all the time.
