AI is becoming dramatically cheaper to use. That could create more—not less—demand for chips.
Silicon Data’s benchmark for the cost of one million AI tokens stood at about $0.97 on August 31, down from roughly $2.07 in May.
That is a decline of more than 50% in only a few months.
The important question is:
If AI becomes cheaper, will people simply spend less—or use far more AI?
For semiconductor demand, that distinction matters enormously.
Educational research only. This article is not investment advice.
What Is an AI Token?
AI models process information in tokens.
A token can represent part of:
- a word;
- a sentence;
- computer code;
- an AI response.
When someone uses an AI chatbot, coding assistant or AI agent, the model consumes tokens.
The cost per token therefore helps measure how expensive AI inference is.
Inference means actually running a trained AI model to answer questions, generate content or perform tasks.
Why AI Is Getting Cheaper
AI costs can fall because of:
- more efficient models;
- better chips;
- improved software;
- greater server utilization;
- smaller specialized models;
- competition between providers.
This improves the economics of deploying AI at scale.
A business that previously found millions of AI interactions too expensive may reconsider once the cost falls dramatically.
Cheaper AI Could Mean Much More AI
This is the most important part.
Suppose the cost of an AI task falls by 50%.
If usage remains unchanged, computing expenditure falls.
But what if cheaper AI causes usage to increase fivefold?
Then total computing demand still rises.
The chain becomes:
Lower token costs → cheaper AI applications → more adoption → more queries and agents → greater compute demand
This is similar to a broader economic idea sometimes called the Jevons effect: making a resource more efficient can sometimes increase total consumption because using it becomes cheaper.
It is not guaranteed.
But it is highly relevant to AI.
AI Agents Could Accelerate Demand
Traditional AI often waits for a person to ask a question.
AI agents can operate much more continuously.
An agent might:
- research information;
- write code;
- analyze files;
- monitor systems;
- communicate with other agents;
- perform repeated business tasks.
That can generate vastly more inference activity than a human occasionally using a chatbot.
DBS analysts have argued that falling token prices could support greater use of applications and agentic AI, creating another wave of computing demand.
This could shift the AI story from:
training bigger models
toward:
running AI everywhere.
Why This Matters for Chip Demand
Training advanced AI models requires powerful accelerators.
Inference also requires chips—especially when millions of users and automated agents operate simultaneously.
The industry is already preparing for that shift.
Chip startup d-Matrix, for example, is developing processors specifically focused on AI inference and is integrating them with Nvidia’s data-center technology.
ASML is also exploring how to increase production of its advanced EUV chipmaking machines beyond 110 units in 2028, with AI demand helping drive customer requirements.
The opportunity therefore extends beyond one chipmaker.
It can reach:
- foundries;
- memory suppliers;
- semiconductor equipment;
- networking;
- optical connectivity;
- power systems;
- cooling infrastructure.
Singapore Could Benefit Too
This is particularly relevant for Singapore investors.
AEM, UMS Integration and Frencken sit within parts of the semiconductor equipment and manufacturing supply chain.
Their shares have already rallied strongly in 2026 as investors price in AI-driven chip demand.
Analysts cited by The Business Times argue that falling token costs could create another phase of spending if cheaper AI increases overall model usage.
That does not guarantee their rallies continue.
It simply gives the semiconductor cycle another potential demand driver.
What the Macro Model Would Watch
TradingSimuLab’s Macro Model can help place the trend in a broader economic context.
Important questions include:
Is AI investment continuing to accelerate?
Are lower computing costs driving greater adoption?
Is infrastructure spending translating into productivity and revenue?
Are interest rates making large AI projects more expensive to finance?
The macro story is therefore not just about technology.
It is about whether AI adoption grows fast enough to justify enormous infrastructure spending.
What Trend Detector Would Watch
TradingSimuLab’s Trend Detector looks at the quality of the resulting stock-price trend.
Trend Strength
Is the semiconductor stock still moving in an organized direction?
Exhaustion Risk
Has enthusiasm pushed the price too far, too quickly?
EMA Slope
Is the broader trend base still improving?
Distance From Trend
Has price become unusually extended?
This matters because:
strong AI demand does not automatically mean every AI stock has a healthy entry point.
What Could Break the Thesis?
Cheaper AI will not automatically create unlimited chip demand.
Risks include:
- model efficiency improving faster than usage;
- companies reducing AI spending;
- weaker AI monetization;
- regulatory restrictions;
- excessive data-center capacity;
- already-stretched semiconductor valuations.
AI safety concerns also triggered a sharp global chip-stock selloff on September 14, showing how quickly market expectations can change.
Final Takeaway
Falling AI token costs could become one of the next major drivers of the semiconductor cycle.
The key relationship is:
Cheaper AI → More Usage → More Inference → More Compute → More Chip Demand
But only if usage grows faster than efficiency improves.
That is why the better question is not:
“Is AI getting cheaper?”
It clearly is.
The real question is:
“How much additional AI usage will cheaper computing unlock?”
That could determine whether the next phase of the AI boom is driven less by training enormous models—and more by running AI everywhere.
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