Verizon CEO Says AGI Could Arrive Within 18 Months: Are Businesses Ready?

Verizon CEO Dan Schulman has put an unusually short timeframe on one of artificial intelligence's biggest uncertainties.

Speaking with Fortune's Diane Brady at Ford Pro Accelerate in Detroit on September 30, 2026, Schulman predicted that the industry could reach "some form of AGI" within the next six to 18 months. Fortune's report on Schulman's remarks records him arguing that today's models will be surpassed quickly as AI capabilities continue to advance.

The prediction was also picked up by Crypto Briefing, helping turn the comment into a broader discussion about how close artificial general intelligence may be.

But an essential distinction must be preserved: Schulman's forecast is not an established AGI timeline.

There is no universally accepted definition or test for AGI. Researchers disagree about which combination of reasoning, generality, autonomy and performance would qualify. That means businesses should not treat the prediction as a countdown to a known technological event.

The short answer: Businesses do not need to believe that AGI will arrive within 18 months to justify preparing for much more capable AI. The practical priorities are knowing where AI is used, identifying high-impact decisions, assigning accountability, strengthening controls, maintaining meaningful human oversight, improving AI literacy and planning for greater autonomy.

The more useful business question is therefore:

Are organizations prepared for rapidly advancing AI capabilities, whether or not AGI arrives within 18 months?

What Did Verizon CEO Dan Schulman Say About AGI?

The Six-to-18-Month AGI Prediction

Schulman told Fortune that he expects rapid improvements beyond today's AI systems and predicted that the industry could reach "some form of AGI" within six to 18 months.

The wording matters.

He did not announce that Verizon had verified an AGI breakthrough, nor did he establish a scientific deadline. His comments were an executive forecast about the speed of technological development.

That distinction should remain clear whenever the phrase AGI in 18 months is discussed.

Why Schulman's Prediction Matters

Schulman's comments are notable partly because they come from the leader of a major telecommunications company rather than an AI laboratory.

Verizon appointed Schulman CEO in October 2025 after his previous tenure as CEO of PayPal. Verizon's own announcement highlighted his experience across telecommunications, technology and financial services.

Executive expectations matter because they can affect workforce planning, investment decisions, organizational design and technology strategy even when the underlying forecast remains uncertain.

The Timeline Is a Forecast, Not a Fact

AGI itself is difficult to forecast because the destination is not precisely defined.

Stanford HAI's explanation of AGI describes it as AI with general human-level or greater ability to learn, reason and apply knowledge across a wide range of domains, while explicitly noting that there is no universally accepted test.

Researchers at Google DeepMind have proposed a more graduated approach. Their Levels of AGI framework distinguishes dimensions including performance, generality and autonomy rather than treating AGI as one binary threshold.

For business planning, Schulman's timeline is therefore better treated as a scenario, not a deadline.

What Is AGI and How Is It Different From Today's AI?

What Artificial General Intelligence Means

Artificial general intelligence generally refers to AI capable of performing effectively across a broad range of intellectual tasks rather than being limited to a narrow function.

That does not mean current generative AI systems are definitively AGI.

Today's systems can already perform diverse tasks, but there is no consensus that they satisfy the broader concept of artificial general intelligence.

AGI vs. Generative AI and AI Agents

Generative AI produces or transforms content such as text, software code, images, audio and video.

AI agents go further by combining AI models with tools and workflows that allow them to plan steps, interact with software, retrieve information or take actions with varying levels of autonomy.

The International AI Safety Report 2026 warns that agentic systems can create heightened reliability risks because autonomous actions may reduce the opportunities humans have to intervene before an error causes harm.

An AI agent should not automatically be called AGI. But an agent does not need to be AGI to create significant business risks.

Why the Definition Matters for Businesses

Organizations can become overly focused on whether a system deserves the AGI label.

For governance purposes, better questions are:

• What can the system do?

• What information can it access?

• What decisions can it influence?

• What actions can it take?

• How reliable is it?

• What happens if it is wrong?

• Who remains accountable?

A system connected to customer accounts, financial workflows, production environments or hiring decisions can create major consequences regardless of how researchers classify it.

Capability and impact are more useful business signals than the AGI label alone.

Why an 18-Month AGI Timeline Would Matter to Businesses

Knowledge Work Could Change Rapidly

More capable AI could expand automation and assistance across research, analysis, writing, coding, customer support, administration and decision support.

That does not establish that widespread job elimination is inevitable.

The International Labour Organization's 2025 research on generative AI and jobs found that, because many occupations still require substantial human input, job transformation is generally more likely than complete redundancy.

For many organizations, the immediate challenge is therefore likely to involve changing tasks, responsibilities and skill requirements rather than simply counting jobs that might disappear.

AI Could Reshape Organizational Structures

As AI systems perform more complex work, organizations may redesign roles around human-AI collaboration.

Employees may spend less time producing first drafts and more time directing, evaluating or integrating AI-generated outputs. Organizations may also need stronger capabilities in AI assurance, security, model evaluation, risk management and incident response.

AI adoption can therefore reshape responsibilities and workflows even before anything widely recognized as AGI exists.

AI Risks Could Scale Alongside AI Capabilities

Greater capability can create greater business value, but it can also increase the consequences of failure.

Relevant risks include:

• unreliable outputs

• bias and discrimination

• privacy failures

• cybersecurity threats

• intellectual-property and copyright issues

• unauthorized actions

• excessive automation

• unclear accountability

The International AI Safety Report notes that current systems still produce fabricated information, flawed code and misleading advice, while autonomous agents can amplify the consequences of reliability failures.

Governance and Compliance Could Become More Important

As systems gain capability and autonomy, organizations need stronger mechanisms for assessing risk, documenting decisions, assigning responsibility and monitoring performance.

The NIST AI Risk Management Framework offers one widely used approach through its Govern, Map, Measure and Manage functions. NIST describes AI RMF 1.0 as voluntary and states that the framework is currently being revised.

The core principle is straightforward: governance capacity should grow alongside AI capability and business impact.

Are Businesses Actually Ready for More Advanced AI?

Readiness varies widely.

A company using an AI assistant for low-risk drafting faces different challenges from an organization allowing AI agents to interact with customers, sensitive databases or operational systems.

Business readiness can be assessed across four dimensions.

Technology Readiness

Organizations need secure infrastructure, appropriate data quality, controlled integrations and robust access management.

AI systems should receive only the permissions they need, particularly when they can access sensitive data or take external actions.

People Readiness

Employees need role-specific AI literacy.

That includes understanding system capabilities and limitations, verification requirements, privacy and security concerns, organizational policies and when human judgment remains essential.

Governance Readiness

Organizations need defined AI policies, accountable owners, risk assessments, approval processes, monitoring, documentation and incident-response procedures.

Governance should also account for change. A vendor update or new model capability can materially alter a system's risk profile.

Organizational Readiness

AI readiness requires cooperation across technology, security, privacy, legal, risk, compliance, HR and business leadership.

Advanced AI is therefore not only a technology challenge. It is an organizational change challenge.

The AGC Advanced-AI Readiness Framework

A practical way to assess maturity is to examine whether governance becomes stronger as AI gains capability and autonomy.

The goal is not to achieve an abstract certificate of "AGI readiness." It is to ensure organizational controls remain proportionate to what deployed systems can actually do.

What Verizon's AI Training Efforts Reveal About Workforce Readiness

Verizon's own actions demonstrate why advanced-AI readiness is not only a technology investment.

On September 23, 2026, the company announced Verizon AI Skills for America, a $70 million workforce initiative aimed at job seekers, early-career professionals, displaced workers, educators and small businesses. Verizon says the commitment combines $50 million in additional investment with an existing $20 million Reskilling and Career Transition Fund.

Reuters independently reported the initiative, including Verizon's plans to collaborate with organizations such as Goodwill Industries and use resources from technology providers to provide practical AI training.

The program does not prove that AGI is imminent.

It illustrates a more defensible conclusion: organizations expecting meaningful AI-driven change are investing in human capability as well as technology.

What Businesses Should Do Now If AI Capabilities Accelerate

1. Map Where AI Is Already Being Used

Organizations should maintain an inventory of AI systems, models, agents, vendors and use cases.

For each system, document its inputs, outputs, users, affected processes, connected systems and permitted actions.

You cannot govern AI effectively if you do not know where it is operating.

2. Identify High-Impact AI Decisions

Not every AI use case requires the same level of control.

Prioritize systems involved in consequential areas such as hiring, financial decisions, customer eligibility, employee management and safety-sensitive operations.

Risk controls should be proportionate to potential harm.

3. Establish Clear AI Accountability

Every material AI deployment should have an accountable owner.

Define who approves the system, who monitors it, who receives escalations and who has authority to restrict or suspend its use.

Greater autonomy makes clear accountability more important, not less.

4. Strengthen AI Risk Controls

Organizations should apply appropriate testing, security review, privacy assessment, bias evaluation, risk assessment and ongoing monitoring.

Controls should continue after deployment.

A new model, broader integration or additional system permissions can change a previously accepted risk profile.

5. Build Human Oversight Into Critical Workflows

Human oversight needs to be meaningful.

A person who automatically approves AI recommendations without enough information, time or authority to challenge them is not providing an effective safeguard.

For critical workflows, organizations should define when human intervention is required and how automated decisions can be challenged or overridden.

6. Train Employees in AI Literacy and Responsible Use

Employees should understand AI capabilities, limitations, verification requirements, privacy risks, security concerns and organizational policies.

AI literacy also has legal relevance in some jurisdictions.

The European Commission's guidance on Article 4 of the EU AI Act states that providers and deployers must take measures supporting the development of AI literacy among relevant staff and other people operating AI systems on their behalf. The Commission also explains that amendments introduced through the 2026 Digital Omnibus retained the obligation while removing the requirement for a particular "sufficient" level of literacy.

7. Create AGI and Advanced-AI Scenario Plans

Organizations should not build strategy around one predicted AGI date.

Instead, prepare several scenarios:

• Best-case scenario: AI capability rises while reliability and governance mechanisms improve alongside it.

• Expected scenario: capability advances unevenly, producing productivity gains alongside new workforce and operational challenges.

• High-disruption scenario: autonomy and capability advance faster than organizational controls and workforce adaptation.

• Reassessment triggers: major model releases, significant increases in autonomy, new regulatory requirements, serious AI incidents or material changes in business use.

Scenario planning improves resilience without pretending the future is predictable.

Businesses Should Prepare for Capability Growth, Not Just AGI

This is the central business lesson.

AI does not have to cross a universally agreed AGI threshold before organizations need stronger controls.

A system that becomes capable of using more tools, accessing more information, making more decisions or taking more autonomous actions may require governance changes immediately.

Businesses can prepare for more capable AI without claiming to know when AGI will arrive.

That is more practical than designing strategy around a speculative date.

What Could Go Wrong If Businesses Move Too Fast?

Deploying AI Without Adequate Oversight

Rapid adoption can give AI systems operational importance before accountability, monitoring and controls have matured.

The problem is not speed alone. It is deployment speed that exceeds governance capacity.

Overestimating AI Capabilities

More capable systems are not automatically reliable systems.

The International AI Safety Report concludes that no current combination of techniques guarantees the reliability required in many high-stakes settings.

Organizations should therefore resist confusing impressive performance with guaranteed correctness.

Underestimating Workforce Impact

Poor transition planning can create skills gaps, unclear responsibilities and resistance to organizational change.

Workers need to understand not only how to operate AI tools but how their own responsibilities may change around them.

Scaling AI Before Risk Controls Scale

Organizations that rapidly multiply AI deployments without strengthening inventories, testing, security, monitoring and incident response can accumulate unmanaged dependencies.

Governance capacity needs to scale with adoption.

What Could Happen If Businesses Prepare Too Slowly?

Moving too slowly creates a different set of risks.

Organizations may develop productivity gaps, miss useful applications, face talent shortages or discover governance requirements only after employees have already adopted AI informally.

They may also find themselves reacting to new regulations or incidents rather than preparing in advance.

The objective is neither uncontrolled acceleration nor paralysis.

It is governed adoption.

AGI Readiness Is Ultimately a Governance Challenge

Whether future systems are described as generative AI, AI agents, general-purpose AI or AGI, businesses need mechanisms for deciding how those systems may be used and under what conditions.

That requires AI governance, risk management, security, privacy, accountability, monitoring, auditability, human oversight and workforce literacy.

The OECD AI Principles emphasize human agency and oversight, transparency, robustness, security, accountability and systematic risk management across the AI lifecycle.

Governance cannot eliminate every possible risk from highly capable AI.

It can, however, make organizations better able to recognize risks, assign responsibility, determine acceptable uses and respond when systems behave differently from expectations.

Should Businesses Believe the 18-Month AGI Prediction?

What We Know

Schulman made the six-to-18-month forecast on September 30, 2026.

AI systems are also becoming more capable across areas such as reasoning, tool use and autonomous activity, while reliability and control challenges remain.

Businesses are already adapting through new deployments, workforce training and governance measures.

What We Do Not Know

We do not know whether AGI will emerge within 18 months.

We also do not know which definition of AGI would become widely accepted, whether capability improvements will continue at the expected pace, or how quickly dramatically more capable systems could be safely deployed.

What Businesses Can Do Regardless

Organizations can improve workforce preparedness, AI governance, risk management, AI literacy, scenario planning and responsible deployment without making a prediction about AGI.

Those investments remain useful if Schulman's timeline proves wrong.

The Bottom Line: Are Businesses Ready for AGI?

There is no universal answer.

Some organizations already maintain AI inventories, formal governance structures, risk assessments, role-specific training and strong technical controls. Others are still working out where employees are using generative AI.

The key mistake would be assuming preparation should begin only after the industry agrees that AGI has arrived.

Schulman's forecast may prove accurate, premature or dependent on a definition of AGI that others do not share.

Businesses do not need to resolve that debate first.

The most useful form of AGI preparation is building an organization capable of governing increasingly powerful AI responsibly.

For business leaders, risk teams, compliance professionals and AI practitioners, strengthening AI governance, AI literacy and responsible AI capabilities now provides a more durable response than trying to predict the exact date of AGI.