Educational research only — not investment advice.
AI agents could become the next major stage of the artificial-intelligence boom.
Chatbots mainly respond when a user asks a question. AI agents go further: they can receive a goal, decide what steps are needed, use software tools and perform multiple tasks with less human intervention.
That difference could have major implications for software companies, cloud providers, data centers and AI chip demand.
What Is an AI Agent?
An AI agent is software designed to pursue a task rather than simply generate an answer.
For example, instead of asking an AI:
“Find some flights to New York.”
An AI agent could potentially:
search flights → compare prices → check your calendar → prepare an itinerary → request approval
In business, agents could perform tasks such as:
- analyzing company data
- writing and testing code
- monitoring cybersecurity
- preparing reports
- handling customer requests
- managing repetitive workflows
The important feature is that the software can complete a sequence of actions.
Why Are AI Agents Becoming Important?
The technology is already moving beyond simple experimentation.
Anthropic said in September that Claude was leading about 26% of work involved in developing its next AI models, while around 30,000 AI agents were operating on its internal platform.
Anthropic stressed that these systems still operate under human supervision rather than independently.
That distinction matters.
Today’s agents are not fully autonomous digital employees.
But they are becoming capable of completing longer and more complex tasks.
Why Could Agents Create More Computing Demand?
A chatbot may process one request and produce one answer.
An agent can perform dozens or hundreds of steps.
It may repeatedly:
read information → reason → use a tool → check the result → make another decision
Each step consumes computing resources.
If millions of businesses eventually deploy agents that operate continuously, AI usage could rise far faster than the number of human users.
Huawei has projected that autonomous agents could eventually account for more than 90% of global AI token traffic by 2035, although that is a long-term industry forecast rather than a certainty.
The broader mechanism is straightforward:
more AI agents → more AI requests → more computing → more data-center demand
Why This Matters for AI Chips
More agent activity could support demand for the infrastructure that runs AI models.
That includes:
- GPUs and AI accelerators
- advanced memory
- networking equipment
- cloud computing
- data-center capacity
- electricity and cooling
Demand for computing is already strong enough that cloud provider Nebius recently announced another increase in prices for access to certain Nvidia chips, its second such increase in three months.
AI agents could add another source of demand if they become widely deployed.
Software Could Benefit Too
The first phase of the AI boom heavily rewarded companies supplying infrastructure.
Agents could shift more attention toward software monetization.
Companies may eventually charge businesses for agents that handle:
- accounting workflows
- sales processes
- coding
- research
- customer service
- cybersecurity
- administration
If agents save companies meaningful amounts of employee time, businesses may be willing to pay recurring subscription or usage fees.
That could create a new source of revenue for software companies.
But Productivity Still Has to Appear
The biggest uncertainty is whether AI agents actually generate enough economic value.
AI adoption has grown quickly, but broad productivity gains have so far been less dramatic than some early forecasts suggested. Reuters Breakingviews noted that AI was already being used in about 44% of U.S. workplaces by May 2026, while economy-wide productivity improvements remained relatively modest.
Companies still need to redesign workflows, train employees and integrate AI into existing systems.
So the real question is not:
“Can an AI agent perform a task?”
It is:
“Can it perform that task reliably enough and cheaply enough to create measurable value?”
What Are the Risks?
Greater autonomy also creates new problems.
Agents need access to software, data and sometimes external systems to perform useful work.
That creates risks involving:
- incorrect actions
- cybersecurity
- sensitive information
- unreliable decisions
- excessive computing costs
- lack of human oversight
Companies developing advanced agents are therefore adding monitoring and safety controls alongside greater autonomy. Anthropic, for example, says actions by its internal agents are screened before execution.
What Would Confirm an AI-Agent Boom?
Several signals would matter more than headlines.
Enterprise adoption: Are companies deploying agents beyond small experiments?
Revenue: Are customers paying meaningfully for agent-based products?
Compute usage: Does agent adoption materially increase cloud and GPU demand?
Productivity: Are businesses actually saving time or reducing costs?
Reliability: Can agents complete long tasks without frequent human correction?
If those metrics improve together, AI agents could become an important second phase of the AI investment cycle.
What Should Investors Watch?
The most useful indicators are AI-agent adoption, cloud-computing demand, software revenue, GPU utilization, data-center spending and enterprise productivity.
The key idea is simple:
AI agents could turn AI from something humans occasionally consult into software that works continuously in the background.
If that transition happens at scale, the computing requirements could be enormous.
But the size of the opportunity will ultimately depend on whether agents create enough real economic value to justify the infrastructure behind them.
Analyze Technology Trends With TradingSimuLab
TradingSimuLab’s Macro and market-analysis tools help users study changing investment themes, market regimes and risk conditions rather than relying on hype around a single technology.
For more quantitative market research and educational trading tools, sign up to TradingSimuLab.
TradingSimuLab is for educational and research purposes only and does not provide investment advice.