AI Models – What Are They, How Are They Created, and Which Is the Best?

It seems like everyone wants AI to be a part of their business model. They aren’t always sure how it fits in with their operations, but they know they want it.

I’ve found that many businesses are afraid their competition might do something with AI that makes their entire product line obsolete overnight, or worse. When I ask what the worst is, I rarely get a real response, and that’s okay. The takeaway is always the same; business leaders don’t want to be left behind.

Unfortunately, what sometimes occurs is they will begin implementing new features or systems, not really understanding how they work just to say they have AI. But that can go wrong. Terribly wrong in fact.

Imagine this.

It’s 8AM. Your customer service manager calls you in a panic. Your team implemented a new AI chatbot for customer support that you didn’t fully understand, and it seemed to be working great. You thought. Unfortunately, you didn’t really understand what it was doing under the hood, or how it was making decisions.

Come to find out, that new AI chatbot spent the whole weekend telling customers that your flagship product causes cancer. It doesn’t, but there was a random Reddit thread in your training data and the chatbot was trained to believe that someone’s sarcastic joke was fact.

Three news outlets have picked up the story. Your stock is down 12%. Your CFO asks how this happened, and you can’t answer them.

Now, of course, this is a made up scenario, but these things can happen when leadership doesn’t understand what they are buying when investing in AI.

The chatbot that caused this crisis? It’s powered by something called an AI model. If you want to avoid playing Russian roulette with your brand, it’s important to understand how they work.

What Is an AI Model?

An AI model is a computer program that has gone through either supervised, unsupervised, or reinforced training to identify patterns in large datasets and produces predictions, classifications, or decisions with minimal human involvement. It uses a variety of algorithms to learn from the patterns in the data it is provided, and applies what it learns to new data that it hasn’t been previously trained on.

It helps to think of an AI model like teaching a child to recognize animals. If you show them pictures of cats and dogs, the child will learn the difference between them and then can later pick out cats and dogs in pictures that they’ve never seen before. The same goes for AI models. Once trained, when given new information it will produce an output based on what it previously learned.

How Do AI Models Work?

AI models work through a four step process:

• Data Collection: Raw data is gathered from various sources. This data matches the type of data that the mode will later have to make predictions based on. It can be text, images, IoT sensor readings, etc.
• Data Preprocessing: After the data is collected it must be cleaned, labeled, and transformed into a usable training format for the AI model.
• Model Training: Model training is where the AI learns to detect patterns, correlations, and relationships from the collected data. Training is done via algorithms, neural networks, pattern identification, or learning from examples.
• Inference: Once an AI system has been trained it can make predictions or decisions from new, previously unseen data. In the case of generative AI, it can create entirely new content like text, images, or audio.

What Are the Main Characteristics of an AI Model?

The main characteristics of an AI model are that it is autonomous and able to make predictions on its own, that it learns from data rather than using explicit logic, it has task specific expertise, and loosely mimics human cognition. AI models are designed to think in a similar manner as humans, just much faster and with vastly superior pattern recognition capability.

The main characteristics of AI models can be different depending on the type of model being used.

Can AI Models Make Mistakes?

Yes, AI models can make mistakes. Typically, the errors produced by AI models are caused by poor train data quality, algorithmic constraints, or ambiguous inputs. The 2020 paper ‘Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It’ by Bishop, J. found that AI models can make mistakes due to their inability to understand causality or grasp basic concepts like time and space.

The most common types of mistakes that AI models make are:

• Hallucinations: Plausible but false or fabricated information produced by generative AI models.
• Bias Outcomes: When an AI model has been trained on biased data, it may unintentionally make unfair predictions or decisions due to issues in the original training corpus.
• Misinterpretations: AI models can occasionally provide incomplete or inaccurate answers due to omitted information, incorrectly interpreted intent, or entity misidentification.
• Context Loss: In multi-step workflows or conversations, the AI model may lose track of context, which will lead to erroneous or disconnected responses.

How AI Models Are Created

AI models are created through a process that starts with a structured series of AI project discovery workshops. Those workshops will clearly define the problem or goal, gathering and labeling initial training data, choosing the right machine learning algorithms and tools, training the model, and evaluating it’s performance to determine if it is ready for real-world use. Continuous monitoring and updates over time will be needed to fine-tune the model iteratively, improving it’s accuracy over time.

How Often Are AI Models Updated?

AI models are updated on varying schedules ranging from daily to annually depending on the application’s needs, rate of changes to the data, and resource constraints. Higher risk applications, such as those for cybersecurity, will often see much more frequent update cycles to evolve with changing intelligence while high-stakes applications such as healthcare will normally see fewer updates to maintain stability and validation.

Common Update Strategies

Can I Create My Own AI Model?

Yes, you can create your own AI model, but depending on the application, it will normally make more sense to hire a developer who specializes in AI model creation.

Key Considerations

Are You Able To Collect, Clean, and Prepare Data Properly?

Research has shown that data quality is likely the single most important factor when creating an AI model. The 2022 paper ‘Advances, challenges and opportunities in creating data for trustworthy AI’ by Weixin Liang found that data design, sculpting, and evaluation are foundational for the creation of reliable AI models. If you’re unable to do this confidently, any attempt to create your own AI model may be wasted effort.

Are You Able To Ensure The Security and Privacy of Your Data?

AI models may unintentionally leak sensitive information such as personal information on your customers or their transaction history. There are also risks of data poisoning, model theft, and backdoor attacks that come with AI model creation and deployment. If you’re unsure of how to defend against those threats, it is advisable to hire a firm that does.

Can You Integrate An AI Model on Your Own?

After your create an AI model, you will have to integrate it for your business purposes. AI integration can be a daunting task unless you are a developer and understand the ins and outs of your business application and it’s workflows. If your attempted integration goes wrong, you could end up creating instability in your business operations that would be avoided by hiring a development firm.

How To Hire an AI Development Company?

To hire an AI development company you need to identify your business goals for the project, specify any technical requirements you may have (if any), source and screen candidates, and finalize an agreement. When looking for AI development services it’s important to assess their problem-solving skills, carefully gauge their communication and collaboration style to be sure it matches your culture, and look for real-world experience in solving problems like yours.

Can a Custom AI Model Be Developed for Just My Needs?

Absolutely! Custom AI model development is often needed by companies that require specially trained algorithms to handle problems requiring a high degree of accuracy, specialized integration or scaling requirements, or need a differentiating competitive edge that a generalized solution is unable to deliver.

Generally speaking organizations that are high-stakes, regulated, or use unique data and workflows are more likely to be innovators and early adopters in the AI space and require custom model development. These companies often have specific data or problems that pre-trained models are simply unable to address effectively.

How Much Does AI Model Cost?

The cost of a new AI model can range from $10,000 to $500,000+ depending on needs, size, and complexity. Here’s a breakdown of some estimates for budget planning:

How Many AI Models Are There?

There is no definitive count of all AI models because new models are being released weekly, and the term model can be interpreted in different ways. As of September 2025 Epoch AI is tracking over 2,971 AI models in their database, but the actual number is likely much higher.

Here’s a breakdown as of September 2025:

• 529 “Notable AI Models
• 131 “Frontier” AI Models
• 294 “Large Scale” Models

Which AI Model Is Best?

Which AI Model Is Best for Coding?

Based on an analysis of benchmark performance, features, and user feedback, the best all-around AI model for coding in 2025 is Anthropic’s Claude Sonnet.

An effective evaluation of AI coding tools requires analysis of use cases, integration capabilities, The model’s capacity for contextual understanding, and the specific nature of the coding tasks it is meant to address.

AI Coding Model Benchmark Comparison

Which AI Model Is Best for Writing?

For writing, especially professional and long-form content where the quality is the most important factor, Anthropic’s Claude Sonnet is the best model available.

Picking an AI model for professional or creative writing will often come down to a trade-off between content quality and feature availability.

AI Writing Model Feature Comparison

Which AI Model Is Best for Math?

For state of the art performance on the most challenging math problems, Google’s Gemini 2.5 Pro is the leader.

Large language models have a known problem. They are not reliable calculators. They can often determine the logical steps to take, but fail at the final computation. They often need to rely on techniques such as Program-Aided Language Models (PaL) to handle this problem in their reasoning engine.

Which AI Model Is Best for Image Generation?

The best AI image generator is a tie between Midjourney and DALL-E 3.

Midjourney remains the best choice for artists, designers, and creatives who need the highest possible artistic quality from their AI model while DALL-E 3 is the better choice to most business and casual users who will benefit from an easier to use conversational interface.

Which AI Model Is Best for Research?

The single best AI model for all around research needs is Google’s Gemini 2.5 Pro with Deep Research.

AI models that perform research aren’t monolithic systems, but rather a collection of tools. Their roles are equally diverse, from models that just augment human research to other agentic models that completely replace the user when it comes to tactical research execution.

Which AI Model Is Best for Data Analysis?

The best overall AI Model for Data Analysis is GPT-4o with its Advanced Data Analysis feature.

AI models have democratized data science, extending the accessibility of analytics to non-coders. Leading solutions are differentiated by they interfaces, workflows, and technical capabilities.

Which AI Model Is Best for Financial Analysis?

In this case, an AI model isn’t the best choice, but a platform is. AlphaSense combines complex workflows, purpose-built tools, and built-in transparency.

Generalist tools are capable of processing and summarizing financial documents, but lack access to trusted financial data sources to be the best choice.