Everyone talks about AI capabilities. Fewer people talk about the costs. Let's fix that.
The Numbers
- GPT-4 training: estimated $100M+
- Latest models: $200M-$500M
- GPU costs: rising
- Energy costs: significant
What This Means
Training AI models is expensive. Very expensive. Only a few organizations can afford it. That's a problem.
The Implications
- High barriers to entry
- Concentration of power
- Economic pressure on labs
- Need for efficiency improvements
The Solutions
- More efficient architectures
- Better training methods
- Shared compute resources
- Open-source alternatives
Why This Matters
Cost determines who can build AI. If costs stay high, power stays concentrated. That's not ideal.
The Takeaway
Cost is a constraint. Understanding it helps you understand the AI landscape. The economics matter as much as the technology.