# AI Gets Cheaper and Smarter – Why Your Cost Model No Longer Works

> Author: Chris Jon Graf (AI Strategist & CEO)
> Updated: 2026-10-19
> URL: https://ai-outsourcing.ch/insights/ai-gets-cheaper-and-smarter-why-your-cost-model-no-longer-works

## Summary

The cost of a given level of AI performance has dropped about 47% per quarter since 2023 while capability rises. Your 2023/24 cost model is obsolete. The real question is not whether AI is a bubble, but whether your calculation keeps pace with deflation and whether you use model routing or outsourcing now.

## The Double Deflation: What’s Happening to Costs

Since 2023, the cost of a given level of AI performance has fallen by about 47% per quarter. That’s not a dry statistic – it’s the most important cost driver in your business. What costs 100 dollars today will cost a small fraction of that in a year, at equal or better quality. This deflation outpaces DNA sequencing, classical compute, and batteries.

**~47%** — Cost decline per quarter (Epoch AI)

Andreessen Horowitz measured the effect back in 2024: reaching GPT-3-level capability fell from about $60 to $0.06 per million tokens – a 1000x drop in three years. HFS Research confirmed the dynamic in October 2026: effective frontier intelligence costs fell 59% in two weeks without any provider lowering list prices. Prices fall through new model generations, not discounts.

## Why Your 2023 Cost Model Is Obsolete

Most mid-market companies still calculate with pilot-phase numbers: a few hundred dollars for API calls, a budget line somewhere between zero and five figures. That misses the exponential shift in price-performance. If you don’t update your calculation quarterly, you’re comparing apples to rockets.

- Unit costs fall exponentially, not linearly.
- New models deliver more capability per token, a hidden quality improvement.
- Agents use more tokens per task, but token prices fall faster than consumption rises.

## The Bubble Question Is the Wrong Question

Whether AI is a bubble is irrelevant to your cost model. Even if some vendors are overvalued, the observed deflation remains: value per dollar grows disproportionately. Waiting for a bubble to burst means missing the moment when the technology becomes available at commodity prices.

> **The Hidden Cost Trap**
>
> Token price is only 10–30% of real project costs. Integration, orchestration, and data preparation dominate. Focusing only on model price means saving on the wrong end.

## Why Waiting Now Costs More Than Acting

Gartner expects LLM inference costs to fall by over 90% by 2030 compared to 2025. At the same time, agent models need 5–30x more tokens per task. Total consumption rises even as unit costs fall. If you don’t build now, you pay double later: higher integration costs and a missed learning curve.

The strategic implications for Swiss SMEs in the global race are discussed in [this KI podcast](https://www.ki-podcast.ch/ki-standort-schweiz-kmu-strategie-und-globales-rennen).

## Three Ways to Exploit the Price Curve

| | |
| --- | --- |
| **In-house team (3–6 months build-up)** | High fixed costs, slow adaptation to price curve, full control |
| **Direct API usage without orchestration** | Low model costs, but 70–90% of project costs fall in integration and operations |
| **AI outsourcing (external unit)** | Scales with price curve, delivers rapid productivity, minimal fixed costs |

## Your Next Step: Rethink the Calculation

Start with a simple quarterly question: How much intelligence do I get for 100 dollars today – and what would I have gotten a year ago? Once you understand this curve, budgets can no longer be static.

## FAQ

### Why is the AI price falling so fast?

Because new model generations use more efficient architectures and hardware. The cost of a given capability has fallen about 47% per quarter since 2023; the effect comes through generation replacement, not discounts.

### Do I need to switch to a new model right now?

No. What matters is that your orchestration allows model changes. Model routing that automatically picks the most cost-effective model delivers more than manual switching.

### How much budget should I allocate for AI if tokens are so cheap?

Token costs are only 10–30% of total project costs. Allocate the largest share to integration, data preparation, and operations – not to API calls.

### Is this just hype or a real cost revolution?

The deflation is measurable and long-term. Even if individual vendors are overvalued, the value per dollar remains real and grows disproportionately.

### What is the biggest mistake in cost calculation?

Focusing on token price and ignoring downstream costs. The real drivers are integration, orchestration, and data preparation.

### How can I benefit from deflation without building my own team?

By using AI as an external unit. A partner handles model routing, orchestration, and maintenance, so you automatically benefit from falling prices.

## Sources

- [Epoch AI — The plunging price of thought](https://epoch.ai/publications/the-plunging-price-of-thought)
- [HFS Research — The AI frontier has become 59% cheaper in two weeks](https://www.hfsresearch.com/quicktakes/the-ai-frontier-is-moving-faster-and-has-become-59-cheaper-in-two-weeks/)
- [Gartner via HPCwire — 90% Drop in LLM Inference Costs by 2030](https://www.hpcwire.com/aiwire/2026/03/25/gartner-forecasts-90-drop-in-llm-inference-costs-by-2030/)
- [a16z LLMflation (via Optinest)](https://optinest.de/ai-infrastructure/inference/token-economics/will-inference-become-cheaper-the-token-cost-trajectory)
- [Alvin Foo — LinkedIn-Quelle (Inspiration)](https://www.linkedin.com/posts/alvinfsc_ai-isnt-just-getting-smarter-its-getting-activity-7516287091748626432-YwzE)