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What is an AI model

What is an AI model

An AI model is a mathematical structure that has been trained on data to produce useful outputs: predictions, classifications, generated text, images, scores, or decisions. Think of it as a very complex "recipe" that a computer has learned from millions of examples. Just as a human chef studies thousands of dishes before creating their own recipes, an AI model absorbs patterns from data during training and then applies those patterns to new, unseen inputs during inference.

The model itself is not software in the traditional sense. It is a file (or a collection of files) containing numerical values called weights that encode the patterns the model has learned. Without its weights, a model is just an empty architecture with no knowledge. With them, it becomes the core artifact that turns raw input into an AI-driven result.

In short: an AI model is a "recipe" learned from data. It takes input, applies patterns it absorbed during training, and produces output. When a crypto AI project claims to "have AI," the first question is: what model?

How a model is created

Building an AI model follows a pipeline that starts with a blank structure and ends with a deployable artifact. Understanding this pipeline helps evaluate how deep and real a project's AI actually is.

Architecture

Every model begins as an architecture -- a blueprint that defines how data flows through the system. Common architectures include feed-forward neural networks, convolutional neural networks (CNNs), transformers, decision trees, and diffusion models. The architecture determines what kinds of patterns the model can learn and how efficiently it learns them. Choosing the right architecture for the task is one of the most important decisions an AI team makes.

Parameters and weights

An architecture contains parameters -- adjustable numerical values that the model tunes during training. A small model may have a few million parameters; a large language model can have hundreds of billions. Before training, all parameters are initialized with random or semi-random values and carry no useful knowledge.

After training, these parameters become weights -- specific values that encode the statistical patterns the model has extracted from its training data. The weights are what make a trained model different from an empty architecture. When people talk about "model size," they usually mean the number of parameters.

Training

During training, the model is exposed to a dataset and an optimization algorithm (such as gradient descent with backpropagation) adjusts the weights to minimize errors. The training process can take hours, days, or weeks depending on the dataset size, model size, and available compute. The result is a trained model: a set of weight files that can be loaded and used.

Evaluation

Before deployment, a responsible team evaluates the model against benchmarks -- standardized tests that measure performance on specific tasks. Evaluation answers the question: does this model actually work well enough to be useful?

Deployment

The final step is loading the trained model into an inference system that serves real requests. Deployment involves decisions about hardware, latency requirements, scaling, and monitoring. A model that exists only as a research prototype is not yet a product.

The simplified end-to-end flow:

  1. Choose an architecture -- transformer, CNN, decision tree, diffusion model, etc.
  2. Prepare data -- collect, clean, and label a dataset.
  3. Train -- run an optimization algorithm that adjusts model parameters based on the data.
  4. Evaluate -- measure performance against benchmarks.
  5. Deploy -- load the trained model into an inference system that serves real requests.

Model vs algorithm

These terms are often confused, but the distinction matters:

  • Algorithm -- the training method or procedure (e.g., gradient descent, backpropagation, reinforcement learning). It describes how the model learns.
  • Model -- the result of applying the algorithm to data. It describes what was learned.

An algorithm is reusable across many datasets and tasks. A model is specific to the data and task it was trained on. The same training algorithm can produce very different models depending on the architecture, data, and hyperparameters used.

Types of AI models

There is no single way to classify models, but several major families appear regularly in both mainstream AI and crypto AI projects.

Type How it works Typical tasks Crypto AI example
Transformer Processes sequences using self-attention to capture long-range relationships Text generation, translation, code generation, reasoning LLM-powered chatbots, AI agents, code auditing
CNN (convolutional neural network) Applies filters to detect spatial patterns (edges, textures, shapes) Image classification, object detection, video analysis NFT analysis, on-chain visual pattern detection
Diffusion model Learns to generate data by reversing a noise-adding process step by step Image generation, video synthesis, audio creation Generative NFT art, synthetic data for training
Decision tree / gradient-boosted tree Splits data along feature thresholds in a tree structure Classification, regression, tabular data Scam detection, risk scoring, on-chain anomaly detection
Reinforcement learning model Learns optimal actions through trial, reward, and penalty Strategy optimization, autonomous decision-making Automated market makers, trading strategy agents
Recommendation engine Ranks items by predicted relevance to a user or context Content curation, personalization Token discovery, protocol recommendation
Generative adversarial network (GAN) Two networks compete: one generates, one discriminates Realistic image synthesis, data augmentation Synthetic training data, deepfake detection

Most crypto AI projects use one or a combination of these model types. Understanding which type a project uses helps assess whether the claimed AI capability is realistic for the stated use case.

Pre-trained vs fine-tuned models

Two terms that frequently appear in project documentation:

  • Pre-trained model -- a model trained on a large, general-purpose dataset (such as internet text or millions of images). Pre-training is expensive and time-consuming. Organizations that do it (such as those behind GPT, DeepSeek, or Stable Diffusion) invest significant compute resources and typically release the resulting model for others to build on.
  • Fine-tuned model -- a pre-trained model that has been additionally trained on a smaller, task-specific dataset. Fine-tuning is cheaper and faster than training from scratch but still requires domain expertise, quality data, and appropriate compute.

A project that fine-tunes an open-source model on proprietary on-chain data has more AI depth than one that simply calls a third-party API. However, fine-tuning alone does not guarantee high quality -- the data, methodology, and evaluation all matter.

Open-source vs proprietary models

Open-source models (sometimes called "open-weight models") publish their architecture, weights, and often training details for anyone to inspect, reproduce, and build upon. Examples include DeepSeek, Qwen, Mistral, and Stable Diffusion. Open-source does not mean the model is free to use commercially -- licenses vary.

Proprietary models keep their weights, architecture details, and training data private. Users access them only through an API. Examples include GPT and Claude.

For verification purposes, open-source models offer a clear advantage: anyone can inspect the code and weights, run the model independently, and verify claims. A crypto AI project that uses or builds on open-source models provides more verifiable evidence than one that references a proprietary model behind a closed API.

Own model vs API to someone else's model

This distinction is critical for evaluating AI depth:

  • Own model -- the project trained or fine-tuned a model and controls its weights, data pipeline, and deployment. This implies deeper AI depth and can be verified through code, model cards, and benchmarks.
  • API call -- the project sends requests to a third-party model (OpenAI, Anthropic, Google) and returns the response. The AI belongs to the provider. See What is an API wrapper.

A project that calls GPT via API and a project that trained its own transformer on proprietary on-chain data are both "using AI" -- but the AI depth, ownership, and verifiability are fundamentally different. DecAIHub reflects this difference in the AI component field and the AI reality score.

Where it appears in DecAIHub project cards

The concept of an AI model touches several parts of a DecAIHub project card:

  • AI component often references a model type or architecture ("fine-tuned LLM," "proprietary classifier," "ML scoring model").
  • AI use case describes what the model does for the end user.
  • Evidence table shows whether model artifacts (code, weights, benchmarks, documentation) have been found.
  • ai_score reflects how well the model's existence and quality are supported by Tier-1 evidence.
  • DecAI Fit incorporates the ai_score into a broader assessment of how well the project fits the Decentralized AI thesis. A project with ai_score = 0 receives DecAI Fit = 0, regardless of other scores.

The presence and type of model a project uses is one of the most fundamental signals in determining AI reality. A project that documents its model architecture, publishes weights or benchmarks, and shows a working inference pipeline provides stronger Tier-1 evidence than one that simply states "we use AI."

What a model alone does not prove

  • Quality -- a model can exist and still perform poorly. Without benchmarks, there is no way to judge performance.
  • Proprietary value -- a model can be a lightly modified copy of an open-source checkpoint. Ownership and differentiation require evidence.
  • Real-world impact -- a model can produce correct outputs in a lab and fail in production. Deployment evidence matters.
  • Necessity -- having a model does not explain why the project needs a blockchain or a token. These are separate axes in DecAI Fit.
  • Ongoing maintenance -- a model trained once can become stale as data distributions shift. Without retraining or monitoring, quality degrades over time.

Do not confuse with

  • Algorithm -- the training procedure, not the trained result. The algorithm produces the model.
  • Inference -- the process of running a trained model to generate outputs. The model is the artifact; inference is its use.
  • AI component -- a broader term that may include the model plus the surrounding pipeline (data, preprocessing, deployment). The model is one part of the component.
  • LLM -- a specific type of model (large language model), not a synonym for "AI model" in general.
  • Dataset -- the data used to train the model. The dataset is the input to the training process; the model is the output.

Frequently asked questions

What is the difference between a model and an algorithm?

An algorithm is a procedure -- a set of rules that tells the computer how to learn from data (e.g., gradient descent). A model is the result of running that algorithm on a specific dataset. The algorithm is reusable; the model is specific to the data and task it was trained on.

How many parameters does an AI model need?

There is no universal answer. A simple classifier for tabular data may work well with thousands of parameters. A large language model may require billions. More parameters do not automatically mean a better model -- architecture design, data quality, and training methodology matter more than raw size. A well-trained small model can outperform a poorly trained large one on specific tasks.

What does "open-source model" mean?

An open-source (or open-weight) model is one whose architecture and trained weights are publicly available for inspection, reproduction, and modification. This does not necessarily mean the model is free for commercial use -- each release has its own license. For verification purposes, open-source models are easier to audit because anyone can run them independently.

Can a crypto project claim to have an AI model without publishing it?

Yes, and many do. However, without published code, weights, model cards, or benchmark results, there is no way to independently verify that the model exists, works as claimed, or was built by the project. In DecAIHub, such claims are reflected through the AI evidence flags -- if no Tier-1 artifacts support the claim, the ai_score remains low.

What is the difference between a pre-trained and a fine-tuned model?

A pre-trained model has been trained on a large general-purpose dataset to learn broad patterns (language structure, visual features, etc.). A fine-tuned model starts from a pre-trained checkpoint and is further trained on a smaller, domain-specific dataset to specialize it for a particular task. Fine-tuning is faster and cheaper than training from scratch but still requires quality data and careful evaluation.

See also

External references

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