AI Summary
As the cost of AI learning dramatically increases and the structure centered around large corporations restructures, the cost of inference sharply decreases, making it accessible to everyone. Therefore, the key to utilizing AI shifts from model development to experimentation and optimization strategies based on low-cost inference. Ultimately, we are entering an era where rapid execution and iterative experimentation in areas like AEO and GEO determine competitiveness.
“The fact that developers can now use powerful AI almost for free. What used to cost dollars now costs pennies, and soon it will cost just a fraction of a penny.”
Will AI become more expensive, or cheaper?
Surprisingly, both answers are correct.
According to Mary Meeker’s 2025 AI Trends Report, the “training cost” of artificial intelligence is skyrocketing, while the “inference cost” is falling at an unprecedented rate. This ironic polarization in the cost structure is fundamentally shaking up the way AI is utilized.
💸 Training costs reach the billion-dollar era, becoming the exclusive domain of large corporations
The cost of training a single AI model has already reached 100 million to 1 billion dollars. Dario Amodei, CEO of Anthropic, states, “Soon, training costs could reach 10 billion dollars.” Over the past eight years, computing costs have skyrocketed by 2,400 times.
This rise in training costs means that AI model development is now a game for a select few large corporations. In fact, as of 2024, the AI-related capital expenditures (CapEx) of the “Big 6”—Apple, NVIDIA, Microsoft, Alphabet, AWS, and Meta—have increased by 63% year-over-year to 212 billion dollars. AWS, for example, is pouring half of its total revenue into building AI infrastructure.
✅ Implication: Creating new GPTs or proprietary LLMs is no longer within the realm of startups.
#⚡ Inference at the millisecond level: The dawn of the “unlimited AI usage era”
On the other hand, what about inference?
The story is different.
With advancements in hardware technology, the number of tokens that can be generated per GPU has increased 105,000-fold over the past decade, and the cost for users to request 1 million tokens from AI has decreased by 99.7%. Now, conversations, searches, summaries, and content generation with AI are practically free.
✅ Implication: Generative search (AEO) and content auto-generation (GEO) are now areas where you can experiment without worrying about money.
🎯 5 insights chainshift is focusing on
In this asymmetric cost structure, our team is capturing the following opportunities.
Learning for the few, inference for all
→ With large-scale learning difficult at present, a “customization pipeline” utilizing open-source, lightweight LLM is becoming more important.
API price decline = unlimited experimentation opportunities
→ With token prices falling from cents to milliseconds, various AEO experiments are now possible without billing concerns.
AI infrastructure diversification
→ New infrastructure providers like CoreWeave and VAST Data are emerging, sparking intense competition on cost-effectiveness.
Speed and efficiency directly impact SEO performance
→ Response speed and cost structure are beginning to influence search exposure algorithms, making API selection directly tied to content performance.
Cost Structure = Content Strategy
→ As inference costs decrease, brand-specific custom snippets and real-time automated response content strategies become more precise.
🚀 Now is the Golden Time for AEO/GEO Strategies
AI is caught in a contradictory trend of becoming both “more expensive” and “cheaper.” The key is choosing where to cut costs and where to focus.
ChainShift is maximizing brand AI search visibility and content production performance based on a strategy of “low-cost, high-speed inference stack + lightweight LLM optimization” amid this change.
What is more important than the direction of technology is
where and how to use that technology.
✍ ChainShift Amy
Reference:
https://www.bondcap.com/report/pdf/Trends_Artificial_Intelligence.pdf
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