Artificial Intelligence

What Is Artificial Intelligence?

Artificial intelligence, commonly called AI, is a broad field involving machine-based systems that can perform tasks associated with forms of perception, reasoning, learning, prediction, communication, planning or decision-making.

There is no single definition that captures every AI system. Different technical, regulatory and research contexts use somewhat different definitions.

The National Institute of Standards and Technology describes AI in terms that include machine-based systems capable of making predictions, recommendations or decisions based on human-defined objectives.

Stanford's AI research community describes modern AI more broadly as computer systems capable of performing tasks associated with human-like intelligence, including language understanding, image recognition, learning, reasoning and decision-making.

In practical terms, AI is best understood not as one product, but as a collection of technologies and approaches used to solve problems that previously required substantial human judgment or manual effort.

AI is not one machine or one algorithm. It is an evolving family of technologies that can transform data into predictions, content, recommendations or actions.

How Does Artificial Intelligence Work?

The way an AI system works depends on its design and purpose, but modern AI frequently involves data, algorithms, statistical methods, computational infrastructure and trained models.

A simplified AI workflow can look like this:

• Data is collected or provided to the system.
• Data is processed into a usable format.
• An algorithm or model identifies patterns or relationships.
• The model is trained or configured for a particular task.
• New information is provided as an input.
• The system produces a prediction, classification, generated output, recommendation or action.

Some systems are trained using labelled examples. Others learn patterns from very large datasets without every example being manually labelled.

More advanced systems can combine models with tools, external data, software applications and feedback loops.

This is one reason the definition of AI continues to evolve as new technical approaches become practical.

What Is the Difference Between AI and Machine Learning?

Artificial intelligence is the broader field. Machine learning is a major approach within AI.

Machine learning systems generally use data to learn patterns that can then be used to make predictions, classifications or other outputs.

For example, a machine learning system could be trained using historical examples to identify patterns associated with customer behaviour.

The system could then use those learned patterns to estimate the likelihood of a particular outcome for new information.

Machine learning therefore represents an important technical foundation for many modern AI applications, including recommendation systems, fraud detection, computer vision, language systems and predictive analytics.

What Is Deep Learning?

Deep learning is a subset of machine learning that uses multi-layered neural networks to process complex information.

Deep learning has played a major role in advances in areas such as computer vision, speech recognition, natural language processing and generative AI.

Instead of relying entirely on manually designed rules, deep learning models can learn increasingly complex representations from data.

This capability has helped enable systems that can work with text, images, audio, video and combinations of different data types.

Deep learning can require substantial computational resources, particularly when training very large models.

What Is Generative AI?

Generative AI refers to AI systems that can generate new content from instructions, prompts, examples or other inputs.

Generated outputs can include:

• Text
• Images
• Audio
• Video
• Software code
• Structured information

Large language models are one prominent category of generative AI. They can process language and generate responses based on patterns learned during training and subsequent system development.

Generative AI has expanded the number of people who can interact directly with sophisticated AI systems because natural-language interfaces make many capabilities accessible without traditional programming interfaces.

However, generated output can contain inaccuracies, unsupported statements or other errors. Human review remains important when the consequences of an incorrect output are significant.

The most important question is not simply what AI can generate, but whether the generated result is useful, accurate and appropriate for its intended purpose.

What Are AI Agents?

AI agents are systems designed to perform tasks with some degree of autonomy.

Depending on their architecture, an AI agent may interpret a goal, plan a sequence of actions, use software tools, retrieve information, make decisions and respond to feedback.

This differs from a simple question-and-answer interface because an agent can potentially take multiple steps toward completing a broader objective.

Examples of agent-style tasks can include:

• Researching information across multiple sources
• Organising information
• Generating and checking software code
• Interacting with business applications
• Monitoring defined events
• Supporting structured workflows

Agentic AI remains an evolving area, and the amount of autonomy appropriate for a system depends heavily on its use case and risk level.

Where Is Artificial Intelligence Used?

AI is already used across a wide range of industries and everyday digital services.

Healthcare

AI can support areas such as medical image analysis, research, administrative workflows, scientific discovery and patient-facing applications. The appropriate level of human oversight varies by application.

Finance

Financial institutions can use machine learning and other AI techniques for fraud detection, risk analysis, customer service, document processing and other analytical tasks.

Manufacturing

AI can be used for predictive maintenance, quality inspection, process optimisation, robotics and supply chain analysis.

Retail

Retail businesses can apply AI to recommendations, forecasting, customer analysis, inventory management and conversational support.

Software

AI-assisted software development can help developers generate code, explain existing code, identify potential problems, write tests and explore implementation options.

Science

Researchers increasingly use AI for tasks involving scientific literature, simulation, prediction, biological research, chemistry and other computational problems.

How Is AI Changing Business?

The commercial impact of AI extends beyond chatbots and content generation.

Companies can use AI to automate repetitive activities, analyse information, support employees, improve customer interactions and build entirely new products.

The 2026 Stanford AI Index reports that organisational AI adoption reached 88% in its latest assessment, illustrating how widely AI systems are being incorporated into business activity.

The same report describes rapid progress across language, vision, speech, reasoning, robotics and agentic systems.

Adoption alone, however, does not prove that every AI deployment creates economic value.

Businesses still need to consider implementation costs, data quality, infrastructure, reliability, security, employee workflows, regulation and measurable business outcomes.

Why Is AI Important for Investors?

Artificial intelligence has become an important area of investment research because its effects can extend across multiple layers of the economy.

Investors may encounter AI through companies developing models, semiconductor technologies, data-centre infrastructure, software platforms, cybersecurity products, robotics, enterprise applications and industry-specific solutions.

The investment landscape therefore extends far beyond companies that market themselves simply as "AI companies."

A business may use AI as an internal capability without making AI its primary product. Another company may build infrastructure used by thousands of AI applications.

Understanding these relationships can help researchers look beyond headlines and examine how capital, technology and commercial demand move through the ecosystem.

AI investment research is increasingly about understanding the ecosystem around the technology, not just identifying companies using the word "AI."

What Infrastructure Does AI Need?

Advanced AI systems depend on a substantial technology infrastructure.

Important components can include:

• Semiconductor processors
• Data centres
• Cloud computing
• Networking equipment
• Data storage
• Software frameworks
• Data pipelines
• Cooling and power systems
• Security infrastructure

This infrastructure layer is important because model development and deployment can require significant computational capacity.

Stanford's 2026 AI Index also highlights the expanding infrastructure footprint associated with AI, including data centres, chips, energy consumption and investment.

For investors, infrastructure can therefore represent an important part of the AI value chain.

What Are the Risks of Artificial Intelligence?

AI can create useful capabilities while also introducing risks that vary according to the system, data, deployment environment and consequences of failure.

Accuracy and Reliability

AI systems can produce incorrect or unreliable outputs. Generative systems can sometimes produce plausible-looking information that is not supported by evidence.

Privacy

AI systems may process sensitive or personal information, making data governance and privacy controls important.

Security

AI systems can introduce or inherit cybersecurity vulnerabilities. NIST's work on adversarial machine learning identifies areas including attacks involving model inputs, data and system behaviour.

Bias and Fairness

AI systems can reproduce or amplify harmful patterns in data or system design. The significance of this risk depends on how the system is used and who is affected by its outputs.

Transparency

Some AI systems can be difficult for users to understand, particularly when complex models produce decisions or recommendations.

Misuse

The same technology that enables useful applications can potentially be used for harmful purposes, including fraud, manipulation, cyberattacks or other forms of abuse.

What Is Responsible AI?

Responsible AI refers broadly to approaches designed to develop and deploy AI systems with appropriate attention to reliability, safety, security, fairness, privacy, transparency and accountability.

NIST's AI Risk Management Framework identifies several characteristics associated with trustworthy AI, including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy and fairness with harmful bias managed.

These considerations are not purely technical. AI systems operate within organisations and social environments, so governance, human decision-making and the intended use of the technology also matter.

Responsible AI therefore involves more than asking whether a model works in a laboratory environment.

It also involves asking whether the system is suitable for its intended purpose, whether risks can be managed, whether users understand relevant limitations and whether appropriate oversight exists.

Why Does Data Matter So Much in AI?

Data is a central component of many AI systems.

Machine learning models learn patterns from data, while AI applications often depend on data supplied during operation.

The quality, relevance, completeness and governance of data can therefore influence the usefulness of an AI system.

Data considerations can include:

• Accuracy
• Coverage
• Relevance
• Data provenance
• Privacy
• Security
• Licensing and usage rights
• Potential bias

For businesses building AI products, access to useful data can therefore become an important part of the technology and competitive landscape.

What Is an AI Model?

An AI model is a computational system trained or configured to perform particular tasks.

Depending on the model, it may classify information, generate content, predict outcomes, recognise objects, understand language or support more complex workflows.

Modern AI systems can contain very large numbers of learned parameters and may require substantial computing resources during development.

However, model size alone does not determine whether a system is useful.

Performance, reliability, cost, speed, security, domain suitability and integration with real-world workflows can all influence practical value.

What Are Foundation Models?

Foundation models are broadly capable models that can be adapted or used across multiple tasks.

Large language models are one prominent example, but the broader foundation-model concept can include systems operating across different forms of data.

Instead of building a completely separate model for every task, organisations can sometimes adapt a general model to specific applications.

This approach has contributed to the rapid expansion of AI applications because one underlying model can support many different products and workflows.

What Is Multimodal AI?

Multimodal AI refers to systems that can work with more than one type of information.

A system may combine modalities such as:

• Text
• Images
• Audio
• Video
• Structured data

Multimodal capabilities can allow AI applications to interpret richer real-world inputs.

For example, an AI system could potentially combine written instructions with visual information and other inputs to support a broader workflow.

Stanford's 2026 AI Index tracks progress across several modalities, including image, video, language and speech, illustrating how AI capability is expanding beyond text-only systems.

AI and Automation: What Is the Difference?

Traditional automation usually follows explicitly programmed rules.

For example, a software system might automatically send an email whenever a particular condition is met.

AI-based systems can introduce a different capability: they may infer patterns from data and produce outputs without every decision being explicitly programmed as a fixed rule.

In practice, businesses can combine traditional automation and AI.

An AI model might analyse information, while conventional software controls what happens after the model produces its output.

This combination can be especially useful in enterprise workflows.

How Is AI Affecting the Economy?

Artificial intelligence can affect the economy through productivity, investment, labour demand, new products, infrastructure spending and changes in how existing businesses operate.

The scale and distribution of these effects remain active areas of research.

Stanford's 2026 AI Index reports rapid growth in organisational adoption and significant increases in AI investment, while also highlighting differences between countries, industries and types of AI activity.

Economic effects should therefore not be reduced to a single headline number.

Investors and businesses can examine more specific questions:

• Which industries are adopting AI?
• What infrastructure is required?
• Which companies are supplying that infrastructure?
• Where is capital being deployed?
• Which business models are changing?
• Which AI applications are generating measurable commercial demand?

Why Are AI Startups Attracting Investment?

AI startups can attract investment because they may be developing new software products, infrastructure, industry applications or technology capable of changing established workflows.

The 2026 Stanford AI Index reports that nearly 2,000 newly funded AI companies were recorded in 2025 in its analysis, reflecting substantial entrepreneurial activity around the technology.

Startup investment, however, should not be interpreted as proof that every company or technology will succeed.

Early-stage businesses can face significant uncertainty involving technology, competition, customer acquisition, financing, regulation and execution.

For investment research, the useful question is often deeper than "Is this an AI company?"

Researchers can examine the company's product, customers, technology, investors, funding history, market, competitive environment and relationships across the wider ecosystem.

What Should Investors Research About an AI Company?

AI companies can vary dramatically in business model, maturity, capital requirements and technological dependency.

A structured research process can examine several areas.

Product

What does the company actually sell, and what problem does the product solve?

Technology

Does the company build its own models, integrate external models or provide infrastructure and applications around other AI technologies?

Customers

Who pays for the product, and how does the company demonstrate customer demand?

Funding

What capital has the company raised, when did it raise it and which investors participated?

Competition

Which companies offer similar products or technologies, and what differentiates the business?

Infrastructure

What computing, cloud, data or hardware resources does the company require?

Economics

How do infrastructure costs, pricing, margins and customer acquisition affect the business model?

Following AI Capital Flows

One of the most interesting ways to study the AI economy is to examine where capital is moving.

Capital can flow into:

• AI model developers
• AI application companies
• Semiconductor businesses
• Data-centre infrastructure
• Cloud platforms
• Robotics companies
• AI cybersecurity companies
• Industry-specific AI startups
• Research and technology businesses

Following these flows can reveal relationships between technology providers, investors, companies and emerging markets.

A funding event therefore contains more information than the amount of capital announced.

The participating investors, company history, sector, geography and subsequent activity can all become useful research signals.

What Is the Future of Artificial Intelligence?

AI development is moving quickly, but predicting exactly how the technology will evolve remains difficult.

Current development is expanding across several directions, including:

• More capable foundation models
• Multimodal systems
• AI agents
• AI-assisted software development
• Robotics
• Scientific AI
• Enterprise AI
• AI infrastructure
• Responsible AI and governance

Stanford's 2026 AI Index reports that AI capability continued to accelerate, with progress spanning reasoning, coding, multimodal systems, robotics and agentic applications.

At the same time, the report highlights challenges around evaluation, responsible AI, infrastructure, energy, investment and governance.

This combination of rapid technical progress and unresolved practical challenges is one of the defining characteristics of the modern AI ecosystem.

What Are the Limitations of AI?

AI systems can be powerful without being universally reliable.

Important limitations can include:

• Incorrect or incomplete outputs
• Sensitivity to input quality
• Difficulty explaining some model decisions
• Security vulnerabilities
• Data and privacy constraints
• Infrastructure costs
• Model evaluation challenges
• Domain-specific performance limitations
• Dependence on high-quality data

AI should therefore be evaluated according to the specific task it is expected to perform.

A system that performs well on one benchmark or workflow may not perform equally well in another environment.

This is particularly important when AI outputs influence financial, medical, legal, security or other high-consequence decisions.

Why Does AI Governance Matter?

As AI becomes more widely deployed, organisations need ways to identify and manage technology risks.

AI governance can include policies and processes covering areas such as:

• Data management
• Model evaluation
• Security
• Privacy
• Human oversight
• Documentation
• Monitoring
• Incident response

The regulatory environment also continues to develop in multiple jurisdictions.

Businesses operating in AI therefore need to consider not only what technology can do, but also the legal and governance environment surrounding its deployment.

How to Research the Artificial Intelligence Market

AI is broad enough that simple keyword searches can produce an overwhelming amount of information.

A more structured approach can make research considerably more useful.

Start With the Technology

Identify whether the company is developing models, infrastructure, applications, robotics, hardware or another AI-related technology.

Identify the Business Model

Understand how the company intends to generate revenue and who pays for its product.

Follow Investors

Examine which investors participate in financing rounds and whether those investors appear across related companies.

Examine Company Relationships

Partnerships, suppliers, customers and investors can reveal important connections within the ecosystem.

Track Funding Over Time

One funding round provides only a snapshot. Looking at financing history can reveal how a company has developed and which investors have remained involved.

Compare the Wider Market

Research becomes more useful when an individual company is considered alongside competitors, technologies, sectors and broader capital flows.

Artificial Intelligence Through the InveLedger Lens

Artificial intelligence is creating a network of companies, investors, technologies and capital flows that extends across industries and geographies.

An investor researching AI may want to know more than which companies are building models.

They may also want to understand:

InveLedger is designed around this broader investment intelligence perspective.

Rather than treating a company or funding announcement as an isolated event, investors can explore the relationships surrounding companies, investors, funding activity and markets.

This can help turn individual pieces of information into a more connected research picture.

Key Takeaways

Artificial intelligence is a broad and rapidly evolving technology field. Understanding the basic structure of the ecosystem makes it easier to evaluate both the opportunities and the risks.

• AI refers broadly to machine-based systems capable of performing tasks involving activities such as prediction, reasoning, perception, learning and decision-making.
• Machine learning is an important subset of AI that uses data to learn patterns.
• Deep learning uses multi-layered neural networks and has contributed to major advances in AI.
• Generative AI can produce text, images, audio, video, code and other content.
• AI agents can perform multi-step tasks with varying degrees of autonomy.
• AI is being applied across software, finance, healthcare, manufacturing, science, retail and many other industries.
• The AI economy includes models, applications, infrastructure, semiconductors, cloud computing, data centres and supporting technologies.
• AI also introduces risks involving accuracy, privacy, security, fairness, transparency and misuse.
• Investors can study AI by examining companies, technologies, capital flows, investors and relationships across the ecosystem.