The answer is more complicated than naming a handful of technology companies. AI involves developers, cloud infrastructure providers, chip manufacturers, businesses deploying AI systems, governments, researchers, and individual users. Each group has different responsibilities and levels of influence.

Nevertheless, concerns about concentrated power have become increasingly visible. In October 2026, actor and United Nations digital governance advocate Joseph Gordon-Levitt attracted attention for questioning whether a small number of companies should have so much influence over the future of AI.

Understanding who controls AI matters because these technologies are increasingly integrated into everyday services and important business decisions.

What Does It Mean to Control Artificial Intelligence?

Control can refer to several different things. One company might own an AI model, another might provide the computing infrastructure required to operate it, and a third might decide how the model is used in a customer-facing product.

For example, a business could purchase access to a commercial AI service, connect it to an internal database, and allow employees to ask questions about company information.

In that arrangement, the model developer determines many of the system’s underlying capabilities. The cloud provider may manage important infrastructure. The business decides what data to connect and which employees can access the tool.

No single participant necessarily controls every part of the system.

This distinction matters when something goes wrong. A misleading answer might originate from model behavior, inaccurate company data, poorly designed instructions, or insufficient human review.

Effective accountability requires understanding which organization is responsible for each part of the workflow.

The Companies Developing Advanced AI Models

Several organizations develop advanced general-purpose AI models. They include companies such as OpenAI, Google, Anthropic, and Meta, alongside other commercial and research organizations around the world.

These developers make important decisions about model architecture, training methods, available features, access policies, and safety measures.

They also decide how models are distributed. Some systems are offered through consumer applications, others through developer APIs or enterprise products, and some model weights are made available under specific licenses.

Different distribution models create different forms of control.

A proprietary service may provide centralized updates and managed security features, but users can become dependent on the provider’s pricing and policies. A model that can be deployed independently may provide greater operational flexibility, although it also places more responsibility on the organization running it.

Why Computing Infrastructure Matters

Training and operating advanced AI systems requires substantial computing resources.

High-performance processors, memory, networking equipment, storage systems, and data centers all contribute to the infrastructure supporting AI.

Organizations with access to large computing budgets may have advantages in training powerful models and serving large numbers of users.

This creates a relationship between AI developers, semiconductor manufacturers, cloud providers and data center operators.

Control over infrastructure does not automatically mean control over every AI application. However, infrastructure availability can influence which organizations are able to compete and how much their services cost.

For businesses, this is one reason to consider the infrastructure and vendor dependencies behind an AI service rather than evaluating only its interface.

Why Are People Concerned About Big Tech’s AI Influence?

Technology markets often reward scale. Companies with large user bases, established distribution channels, and substantial financial resources may be able to expand faster than smaller competitors.

In AI, these advantages can include access to computing capacity, specialized talent, customer relationships, and the ability to integrate AI features into existing products.

Concerns arise when these advantages make it difficult for competing products to gain meaningful access to customers or infrastructure.

For example, if a company relies on one AI provider for search, document processing, and customer service, replacing that provider could require significant changes to workflows and employee training.

This is known as vendor lock-in. It is not unique to artificial intelligence, but the growing number of AI integrations can make the issue more significant.

Competition can encourage innovation, lower prices, and improve services. Concentrated markets may create different incentives. The effects depend on market conditions and should be evaluated using evidence rather than assuming every large technology company behaves identically.

Joseph Gordon-Levitt’s Warning About Corporate Control

In October 2026, Joseph Gordon-Levitt discussed the growing influence of AI companies in an interview with The New York Times.

His argument focused on the people and organizations deciding how AI is developed, deployed, and monetized.

He raised concerns about the possibility that commercial incentives could shape technologies affecting work, communication, and personal relationships without sufficient public oversight.

His perspective reflects a broader debate about the role of private organizations in determining the future of widely used digital infrastructure.

Read our full explanation of Joseph Gordon-Levitt’s AI warning for the context behind his remarks.

What Is AI Governance?

AI governance refers to the rules, processes, and responsibilities used to guide the development and deployment of artificial intelligence.

Governance can operate at several levels. Governments may establish legal requirements. Companies may adopt internal policies. Industry organizations may develop technical standards. Independent researchers may evaluate systems and publish findings.

Within a business, governance can be as practical as deciding which AI applications employees are allowed to use and what information they may share.

A useful governance framework generally addresses questions about responsibility, transparency, privacy, security, fairness, and human oversight.

It should also define what happens when an AI system makes a serious mistake.

Why Transparency Matters

Transparency helps users and organizations understand the capabilities and limitations of AI systems.

That does not necessarily require every company to disclose proprietary source code or confidential technical information.

Useful transparency may include clear descriptions of intended uses, known limitations, privacy practices, data retention, system updates, and available mechanisms for reporting problems.

For example, a company adopting AI-generated customer support should understand whether conversations are stored, how long records are retained, and whether employees can review disputed responses.

Without this information, customers and businesses may struggle to evaluate the risks of using a service.

Can Governments Regulate Artificial Intelligence?

Governments can regulate AI-related activities through privacy laws, consumer protection rules, competition law, sector-specific requirements and dedicated AI legislation.

Different jurisdictions take different approaches, and the requirements applicable to a product may depend on its purpose and where it is offered.

AI used to generate entertainment content does not necessarily create the same risks as AI used in healthcare, employment or financial decisions.

Regulation therefore involves balancing innovation with safeguards appropriate to the potential consequences.

Supporters of stronger oversight argue that companies should not be the sole judges of their own products’ safety and societal impact.

Others caution that poorly designed rules could impose disproportionate costs on smaller developers, limit useful experimentation, or entrench existing large providers.

Both concerns deserve consideration when evaluating specific policies.

Open-Weight AI vs Proprietary AI

One important part of the control debate concerns how models are distributed.

Proprietary AI services generally provide access through interfaces controlled by the developer or service provider.

Open-weight models make trained model parameters available under specified conditions. Depending on their licenses and technical requirements, organizations may be able to deploy or modify them independently.

Open-weight does not automatically mean fully open-source. Training datasets, code, documentation, and licensing rights may differ.

Independent deployment can offer more flexibility over infrastructure and data handling, but it also requires technical expertise, security management, and ongoing maintenance.

For many businesses, the choice is not simply between freedom and restriction. It involves trade-offs among cost, reliability, support, customization, and operational responsibility.

How AI Control Affects Privacy

AI systems frequently process information provided by users, employees, and connected applications.

Depending on the product, this information may include questions, documents, customer records, or other business data.

Privacy depends on what data is collected, how it is processed, who can access it, and which contractual or technical controls apply.

Users should not assume that every AI service stores information in the same way.

Some products offer enterprise controls or different data-handling arrangements. Others may provide consumer settings for managing history and personalization.

Before adopting a tool, review the actual terms and privacy documentation for the relevant product and plan.

For practical guidance, visit our AI Privacy Risks in 2026 guide.

Who Is Responsible When AI Makes a Mistake?

Responsibility depends on the circumstances, applicable law, and the role of each organization.

Consider an AI system used to summarize internal business reports. If it generates an incorrect figure, the organization using the system should have a process for verifying important information before acting on it.

If the underlying service experiences a security incident, the provider’s obligations may be governed by contractual and legal requirements.

In higher-impact applications, responsibility can be more complicated because developers, deployers, and human decision-makers may each influence the outcome.

This is why organizations should define who reviews AI-generated recommendations, who approves important decisions, and how incidents are reported.

What Businesses Should Do Before Adopting AI

Businesses can reduce unnecessary risks by treating AI adoption as a technology management decision rather than simply activating a new software feature.

• Identify the tasks AI will perform.
• Classify the data employees may submit.
• Review the provider’s privacy and security documentation.
• Confirm contractual terms and retention policies.
• Define which outputs require human verification.
• Evaluate alternative providers and data portability.
• Train employees on acceptable use.
• Review performance and incidents periodically.

These steps are useful for organizations of many sizes. They help establish accountability without assuming that every business needs an elaborate governance program.

What Role Do Consumers Have?

Consumers influence technology markets through their choices, feedback, and participation in public discussion.

Individuals can compare privacy policies, evaluate subscription terms, and select products that provide appropriate controls.

They can also support meaningful transparency and participate in public consultations where available.

However, individual choices cannot solve every structural problem. Issues involving market concentration, competition and public accountability may require collective responses from institutions and policymakers.

The Future of AI Control

The organizations controlling AI infrastructure and products may change as technology develops.

New models, more efficient computing methods, alternative deployment approaches, and regulatory developments could influence the competitive landscape.

It is therefore premature to assume that the current distribution of influence will remain unchanged indefinitely.

The enduring question is how to ensure that people and organizations using AI retain meaningful choices and protections.

Final Thoughts

Artificial intelligence is not controlled by a single company or institution. Its development involves a complex network of model developers, infrastructure providers, businesses, governments, and users.

Nevertheless, concentrated resources and commercial influence make governance an important issue.

Understanding the organizations behind AI products can help consumers and businesses evaluate privacy, reliability, accountability, and long-term dependence.

For additional context, read Joseph Gordon-Levitt’s warning about AI companies and our practical AI privacy guide.

Frequently Asked Questions

Who controls artificial intelligence?

Control is distributed among AI developers, infrastructure providers, organizations deploying AI, governments, and users. Their influence depends on the system and how it is used.

Do a few companies control all AI?

No. Several large organizations have significant influence, but universities, smaller companies, open-weight projects, and public institutions also shape AI research and development.

What is AI governance?

AI governance refers to the policies, processes, and responsibilities intended to ensure AI is developed and used appropriately.

Why is AI market concentration a concern?

Concentration can raise questions about competition, pricing, user choice, infrastructure access, and accountability.

Are open-weight AI models safer?

Not automatically. Open-weight models offer different deployment options, but safety depends on their design, implementation, security controls, and intended use.

How can businesses reduce dependence on one AI provider?

Businesses can evaluate alternative services, document integrations, maintain data portability, and avoid unnecessary reliance on proprietary workflows.

His comments have attracted attention at a time when generative AI is becoming part of search engines, workplaces, creative tools, customer service and personal communication. AI can be useful, but its rapid adoption raises questions about accountability, privacy, ownership, and the public’s ability to influence decisions that affect them.

So what did Joseph Gordon-Levitt actually say about AI, and why does it matter to people who use these tools? Understanding his argument requires looking beyond dramatic headlines about robots and examining the companies, business models, and policies behind today’s AI products.

What Did Joseph Gordon-Levitt Say About Artificial Intelligence?

In an October 2026 video interview with The New York Times, Gordon-Levitt encouraged audiences to focus on the organizations creating and deploying AI rather than treating artificial intelligence as an independent villain.

His comments reflect a concern that decisions about the future of AI could become concentrated among a small number of companies with significant financial and technological resources.

Those decisions include what information AI systems are trained on, how products are designed, which safeguards are implemented, what users are encouraged to share, and how generated content is monetized.

Gordon-Levitt’s position is not that every AI application is inherently harmful. Instead, he argues that the public should question the incentives and accountability of organizations that increasingly influence how people communicate, work, and create.

Readers can watch the New York Times video featuring Joseph Gordon-Levitt for the original discussion.

Why Is Joseph Gordon-Levitt Speaking About AI?

Gordon-Levitt is known for acting in films including Inception and 500 Days of Summer, but his involvement with technology and creative collaboration extends beyond acting.

He founded HITRECORD, a collaborative creative platform, and has publicly discussed the relationship between technology companies and the people whose work gives those platforms value.

In 2026, he also took on a United Nations advocacy role focused on human-centric digital governance. The role involves raising awareness of how digital technologies affect people and encouraging public discussion about their development and oversight.

His background in creative industries is particularly relevant because generative AI can produce writing, images, audio and video using patterns learned from large datasets. This raises questions about the treatment of artists, writers, performers and other contributors whose work may be included in training material.

However, being a prominent actor does not automatically make someone a technical authority on machine learning. His arguments should be assessed on their evidence and reasoning, alongside research from computer scientists, economists, policymakers and privacy experts.

The Real Concern: Who Controls AI?

Artificial intelligence requires substantial resources. Advanced systems depend on computing infrastructure, specialized chips, large datasets, engineering teams, and ongoing investment.

These requirements can create advantages for organizations with access to capital, cloud computing and distribution networks.

When a small number of companies control important infrastructure, users and businesses may become dependent on their products. That dependence can influence prices, access to technology, privacy choices, and the availability of alternatives.

For example, a business might integrate an AI assistant into its customer support workflow. Over time, employees may rely on that assistant for answering questions, finding information, and creating reports. If the provider changes its pricing, privacy practices or product features, switching to another system could become difficult.

These concerns are not unique to AI. Similar questions have emerged around search engines, social networks, app stores and cloud platforms. However, AI may intensify them because it can become involved in a wider range of decisions and tasks.

For a broader explanation, read our guide to who controls artificial intelligence and why it matters.

Why AI Companies’ Business Models Matter

AI systems do not exist independently of the organizations that build and operate them. Their features reflect engineering decisions, commercial strategies, legal requirements, and organizational priorities.

Some AI products are sold through subscriptions. Others are integrated into larger software services or offered through enterprise agreements. Different business models can create different incentives.

A subscription provider may prioritize retaining customers and expanding paid usage. An enterprise provider may emphasize reliability, security, and administrative controls. A company offering a consumer application may focus on convenience, engagement or growth.

None of these incentives automatically makes a company irresponsible. The important question is whether commercial objectives are balanced with transparent policies, meaningful safeguards and user control.

Responsible product evaluation therefore involves more than asking whether an AI system gives useful answers. Users should also examine what data it collects, whether information is retained, what controls are available, and how the provider handles mistakes.

AI Chatbots and Emotional Attachment

One particularly notable part of Gordon-Levitt’s recent comments concerns the possibility that conversational AI products can encourage emotional attachment.

Modern chatbots can respond naturally, maintain context, and adapt their language to a user’s preferences. These features can make interactions feel comfortable and personalized.

For many people, conversational interfaces are simply convenient. They can help explain difficult subjects, organize thoughts, or provide writing assistance.

However, highly personalized conversations can also encourage users to disclose sensitive information. Someone who treats a chatbot as a trusted companion might share financial problems, workplace information, personal relationships, or other private details without understanding the provider’s data practices.

The potential risk is not that every chatbot deliberately manipulates users. It is that design choices affecting trust, engagement, and disclosure deserve careful examination.

Users should understand whether conversations are stored, whether they can be deleted, what memory features do, and whether submitted information may be used for additional purposes.

Our AI privacy risks guide explains practical steps for reducing unnecessary exposure.

What About Artists, Copyright and Compensation?

Gordon-Levitt has also emphasized the relationship between generative AI and human creativity.

AI models can generate text, images, music, and other material by learning statistical patterns from training data. That data may include human-created works, depending on the model and its development process.

This has prompted disputes about consent, licensing, attribution, and compensation. Some creators argue that companies should obtain permission or provide compensation when copyrighted works are used for training. AI developers and other stakeholders have advanced different legal and technical arguments concerning permitted uses of training data.

The legal landscape varies by jurisdiction and continues to develop. It would be inaccurate to suggest that every AI model uses the same datasets or that every training practice has already been determined lawful or unlawful.

For content creators, the practical questions include whether their work has been licensed, whether they can control certain uses, and how they should evaluate AI-generated material in their own projects.

These questions also matter to businesses that use AI for marketing, design and documentation. Organizations should consider intellectual property policies and review generated outputs before commercial use.

Is Gordon-Levitt Against Artificial Intelligence?

His recent remarks are better understood as criticism of concentrated corporate influence and inadequate accountability than as a blanket rejection of AI technology.

There are legitimate reasons to be optimistic about AI. It can assist with accessibility, research, software development, education, translation, and many other activities.

At the same time, useful technology can create risks when implemented without appropriate safeguards.

The two positions are not mutually exclusive. Someone can support advances in AI while advocating stronger transparency, privacy protection, and public oversight.

Gordon-Levitt’s argument encourages readers to separate the capabilities of a technology from the incentives of the organizations deploying it.

What Does Human-Centric AI Governance Mean?

Human-centric governance means designing and regulating digital systems around people’s rights, interests, and ability to make meaningful choices.

In practice, this can involve transparency about automated decisions, mechanisms for reporting harm, appropriate human oversight and clear responsibility when systems fail.

For example, an organization using AI to screen job applications should consider whether the system produces unfair outcomes, how candidates can challenge decisions and who is responsible for reviewing mistakes.

A business deploying an AI customer service tool should consider data protection, accuracy, accessibility and the circumstances in which a human employee should take over.

Good governance is not necessarily a demand that every AI decision require government approval. It is a framework for identifying risks, assigning responsibility and providing meaningful accountability.

How Could Stronger AI Oversight Affect Businesses?

Organizations adopting AI may face growing expectations concerning privacy, documentation, vendor evaluation, and responsible use.

For small and medium-sized businesses, this does not necessarily require creating an extensive compliance department. A practical starting point is to understand where AI is used and what information employees provide to it.

Businesses can establish clear policies for sensitive data, review vendor agreements, train employees, and define when human verification is required.

They can also avoid depending entirely on a single provider without considering alternatives and data portability.

These measures can improve operational resilience even when specific regulations do not apply.

What Can Everyday AI Users Do?

Consumers do not need to understand every technical detail of machine learning to make more informed choices.

• Review the privacy settings of AI applications before sharing sensitive information.
• Avoid entering passwords, financial credentials, or confidential workplace data into tools without appropriate authorization.
• Check important AI-generated claims against reliable sources.
• Understand whether an AI tool is intended for entertainment, general assistance, or professional use.
• Compare providers rather than assuming every AI product has the same policies.
• Use available controls for conversation history, personalization, and data retention.
• Pay attention to independent research and public discussion about AI governance.

These steps will not resolve every issue involving corporate power, but they can help individuals make more informed decisions about the products they use.

Why This Debate Matters Beyond Hollywood

Gordon-Levitt’s involvement has brought additional attention to issues that extend far beyond the entertainment industry.

AI is being integrated into office software, smartphones, search tools, creative applications, and business systems. The decisions made by AI developers can therefore influence millions of users who may never follow technology policy closely.

His comments provide an accessible entry point into a complicated debate: how can society benefit from powerful AI while ensuring that organizations remain accountable to the people affected by their products?

Answering that question will require contributions from researchers, companies, policymakers, workers, and users.

Final Thoughts

Joseph Gordon-Levitt’s warning about AI companies highlights a central challenge of the technology’s rapid growth: the importance of human oversight and accountability.

The debate should not be reduced to choosing between excitement about AI and fear of its consequences. AI can provide substantial benefits while still requiring scrutiny of how it is built, financed, governed and deployed.

For readers interested in understanding the broader issues, continue with our guide to AI ownership and corporate control and our practical AI privacy guide.

Frequently Asked Questions

What did Joseph Gordon-Levitt say about AI?

He warned that people should pay close attention to the companies building and controlling AI, including their incentives, influence, and accountability.

Why is Joseph Gordon-Levitt involved with the United Nations?

He has taken on an advocacy role focused on human-centric digital governance, encouraging discussion about the relationship between technology and people’s rights and interests.

Is Joseph Gordon-Levitt opposed to AI?

His recent comments focus on corporate accountability and the risks of concentrated power rather than arguing that all AI technology should be rejected.

What is human-centric digital governance?

It is an approach to technology policy that emphasizes human rights, transparency, accountability, and meaningful public oversight.

Why are AI chatbots a privacy concern?

Users may disclose sensitive information during natural conversations. Privacy risks depend on the provider’s policies, product design, and the information shared.

What can consumers do about AI risks?

Consumers can review privacy controls, limit sensitive disclosures, verify important outputs and compare the practices of different AI providers.