For years, a comfortable idea about artificial intelligence was widely promoted: broad access, open tools, democratization of knowledge, and technology at everyone’s service. It was a powerful narrative, especially in the early months of the explosion of generative models. Anyone could open a chat, ask for help with writing, coding, summarizing, or analyzing information, and feel that a capability once reserved for technical teams was now accessible with a free account.
That story hasn’t disappeared, but it’s evolving. Sam Altman, CEO of OpenAI, clearly expressed this during the BlackRock Infrastructure Summit: AI will eventually function like a basic utility—similar to electricity or water—with measured consumption and pay-as-you-go pricing. The phrase may seem like just another Silicon Valley metaphor, but it conceals a much deeper shift. If intelligence becomes a utility, it ceases to be just software. It becomes an economic infrastructure.
This difference matters. An app is installed, tested, and then abandoned. Infrastructure is consumed, measured, billed, and creates dependence. Electricity isn’t valued by the outlet but by what it enables you to do. AI is moving in the same direction: an invisible layer fueling processes, decisions, automation, programming, customer service, financial analysis, cybersecurity, marketing, operations, and business management.
Free AI was a phase, not the final destination
The initial free access made sense. It helped educate the market, attract users, test use cases, and prove that models could be integrated into daily life. It also helped develop habits. Millions began consulting AI before writing emails, preparing proposals, brainstorming ideas, or solving technical questions.
But maintaining that experience comes at a huge cost. Training advanced models requires GPUs, memory, high-speed networks, storage, data centers, energy, cooling, and research teams. Running them globally also costs money. Every conversation, image, detailed analysis, and agent working for minutes or hours consumes tokens, computing power, and electricity.
The industry already operates under this logic. APIs are billed per input and output tokens. Free tiers limit messages, context length, file uploads, image generation, advanced research, or access to more powerful models. Individual subscriptions expand capacity. Enterprise plans add security, management, connectors, and more usage. At the top tier, large companies negotiate capacity, privacy, integration, and tailored conditions.
The clear consequence: basic AI will remain available to many users, but the most useful, deep, fast, and connected intelligence will come with a price. You won’t just pay to “use AI,” but to access better models, more context, greater memory, more tools, additional agents, and deeper integration with proprietary data.
| Access Level | What it offers | What it may limit |
|---|---|---|
| Free | Basic use for general tasks | Messages, context, speed, advanced models |
| Individual Subscription | More capacity and tools | Enterprise integration, advanced controls |
| Business Plan | Security, management, connectors | Cost per user, increasing consumption |
| Token-Based API | Integration into products and processes | Variable billing based on usage |
| Advanced Agents | Automation of complex tasks | High computing consumption, supervision required |
| Own Infrastructure | Control and sovereignty | High investment in hardware, talent, and energy |
The new gap will be cognitive and productive
The big question will no longer be who can use artificial intelligence. It will be how much intelligence each person, company, and country can afford to consume. This nuance shifts the debate.
A small business might use AI to write proposals, reply to emails, or analyze spreadsheets. A large corporation could deploy agents connected to their ERP, CRM, code repositories, financial data, internal documents, and customer service systems. A freelancer might have a support tool. A multinational could have a distributed automation network across its organization.
The gap won’t only be about access but about the depth of that access. Those able to pay more for computing power will have access to more capable models, faster responses, greater context, deeper integration, and more automation. This could become a new form of competitive advantage—not just a digital divide, but a cognitive and productive one.
This difference manifests in specific tasks. A company with advanced AI access can analyze contracts, generate code, identify vulnerabilities, prepare campaigns, translate documents, simulate financial scenarios, and automate customer support. Another with limited access might do some of these but with less capacity, more human intervention, and slower responses.
The risks are cumulative. Greater AI access allows increased production, sales, better analysis, and process automation. This drives higher revenue, more data, and increased ability to continue paying for intelligence. Conversely, those without access to top capabilities will compete with more limited tools against organizations operating on a continuous layer of intelligence.
Infrastructure will decide who leads
The comparison with electricity also points to another reality: those who control the infrastructure control part of the market. In AI, that infrastructure isn’t abstract. It includes data centers, chips, energy, models, APIs, cloud agreements, networks, orchestration software, data, and distribution platforms.
That’s why the race between OpenAI, Microsoft, Google, Amazon, Anthropic, Meta, xAI, and major Chinese players isn’t just about product development. It’s a race to become the on-demand intelligence provider. The company whose models are integrated into office tools, enterprise systems, development environments, search engines, mobile apps, browsers, and cloud platforms will be hard to displace.
It also explains why the cost of computing has become a strategic concern. If demand for AI outpaces available capacity, access could become more expensive or prioritized. Altman has pointed out that computing will be one of the key factors determining who can access advanced intelligence. This has social and geopolitical implications: not all countries have cheap energy, data centers, sufficient chips, or local providers.
Europe should pay close attention to this debate. Regulating AI will be important, but it’s not enough. If the layer of intelligence relied upon by companies, governments, hospitals, universities, and citizens depends on external infrastructures, true autonomy will be limited. Technological sovereignty is not only about data residency but also about who provides the intelligence that interprets data and automates decisions.
Agents will make costs visible
The consumption will become even more apparent with AI agents. A chatbot answers a question. An agent can read documents, consult tools, execute tasks, write code, test solutions, self-correct, call APIs, and repeat until achieving a goal. This behavior resembles less a simple query and more a digital worker consuming resources over time.
This changes the economics of use. A brief reply may be inexpensive, but an agent flow analyzing thousands of lines of code, reviewing documentation, running tests, and proposing changes can consume many more tokens. In cybersecurity, software development, legal analysis, finance, or technical support, the bill can rise quickly if poorly managed.
Companies will start applying a logic similar to Cloud FinOps. It’s not about banning AI but measuring which use cases generate return. Which tasks justify costly models. Which processes can be handled with smaller models. Which agents need real autonomy. What limits should be set. What data can be shared. What costs are allocated to each department.
The enthusiasm phase will give way to accounting—no longer because AI ceases to be useful, but because it will become a structural expense. Just as companies learned to manage cloud spending, they will need to learn to manage the costs of intelligence.
From democratizing tool to economic resource
The initial promise of artificial intelligence was to democratize capabilities. And to some extent, it has. Many people can now write better, code faster, learn languages, summarize documents, or generate images with tools that a few years ago seemed like science fiction.
The pressure now is on what comes next. The most powerful AI is becoming a measured, billed, and concentrated resource. Basic access won’t disappear, but there will be a growing gap between using a limited version and having advanced agents connected to data, applications, and processes.
The fundamental question is not whether AI will have a cost. It already does. The real question is whether its distribution will be reasonable or if it will widen inequality between those who can consume almost unlimited intelligence and those limited to a scaled-back version.
Electricity transformed the economy because it reached homes, factories, cities, and public services. AI could have a similar impact, but its deployment will depend on industrial, regulatory, and commercial decisions. If it becomes a utility, discussions around tariffs, competition, sovereignty, universal access, reserved capacity, and usage rules will be necessary.
The key change is that AI no longer is just another application—it is now a fundamental layer of productivity. Whoever controls that layer will command a significant part of the digital economy. And those able to pay more for access will have greater power to analyze, create, code, automate, and decide.
AI began as a promise of democratization. Now, it enters a more mature and less comfortable phase: one of costs, infrastructure, and power.
Frequently Asked Questions
What does it mean that AI will be paid for like electricity?
It means that AI will tend to operate as a service priced by usage, with costs associated with tokens, models, agents, context, tools, and computational capacity.
Will free AI disappear?
Not necessarily. It’s likely that free or basic plans will continue to exist, but with limitations compared to advanced models, automation, broad context, and enterprise functions.
Why could this increase technological inequality?
Because those who can afford more AI will have access to better models, faster responses, greater automation, and higher productivity, while others will compete with more limited capabilities.
What should companies do?
Measure the true cost of AI, prioritize use cases with clear returns, control token consumption, set internal policies, and develop governance strategies for agents and models.