Memory is Cheap, Storage is Cheaper, Tokens are NOT
The weird product paradoxes when a TB of storage costs less than dinner but every AI thought burns real money. Welcome to the token economy.
We can buy TBs of storage for less than a nice dinner and 100GB of RAM costs what I used to spend on a single textbook. All this hardware to store and process data is practically free.
But every time my AI agent actually thinks, every token it processes, I am paying real money that adds up faster than my AWS bill after a weekend hackathon.
The Hardware vs Intelligence Cost Curve
| Resource | Cost Today | Cost 10 Years Ago | Change |
|---|---|---|---|
| 1TB Storage | $20 | $200 | -90% |
| 100GB RAM | $300 | $3,000 | -90% |
| 1M Tokens (GPT-4) | $10-30 | N/A | New Cost |
This creates the weirdest product development paradoxes I've ever experienced:
The Four Paradoxes of Token Economics
✅ The Logging Paradox
Cheap: I can store every user interaction, every failed attempt, every debugging trace forever. Perfect observability costs pennies.
❌ Expensive: But letting my AI actually read and learn from all this data? That burns through my token budget fast.
Real example: Storing 1 year of logs = $5/month. Having AI analyze those logs = $500/analysis.
✅ The Context Window Prison
Cheap: My database holds endless user history for almost nothing. Every conversation, preference, past project is sitting right there.
❌ Expensive: But I am constantly playing Tetris with my prompts, deciding what to forget so I don't hit token limits.
The math: 100MB of user data stored = $0.02/month. Same data in context window = $100+ per request.
✅ The Inverse Engagement Problem
Traditional SaaS: Loves users who stick around. More usage = better metrics = happier investors.
❌ AI Products: I am celebrating efficient usage instead. The best interaction solves the problem in fewer tokens. "Tokens per solve" is now a KPI that investors actually grill you on. More usage can tank your margins if you haven't optimised for efficiency.
Traditional: Time on site ↑ = Success ✅ AI Product: Tokens per task ↓ = Success ✅
✅ The Documentation Dilemma
Cheap: I can embed every piece of documentation into my vector database for pennies.
❌ Expensive: But the moment I want to query and analyse this knowledge? That's when the bills start flowing.
Vector DB costs: 1M embeddings = $10 one-time. Querying those embeddings with LLM = $5-50 per complex query.
How This Changes Product Development
This isn't just an optimisation problem. It's reshaping how we think about product design entirely. Features get ranked by token-per-value ratios, not just user impact.
New Metrics That Matter
- →Tokens per solve: How efficiently can we resolve user problems?
- →Token ROI: Revenue generated per token spent
- →Cache hit rate: How often can we avoid token usage entirely?
- →Context efficiency: Information density per token
The companies winning in AI aren't just the ones with the smartest models. They are the ones who figured out how to be brilliant efficiently.
We are back to the optimisation days of the 90s, but instead of counting CPU cycles, we are counting tokens.
💰 Every prompt is now a budget decision.
Strategies We Use to Survive
Aggressive Caching
Cache everything. Semantic caching for similar queries. Response caching for common patterns. Even cache partial computations.
Smart Model Routing
Not every task needs GPT-4. Route to smaller models when possible. Check out our analysis on using smaller models effectively.
Context Window Management
Compress, summarize, and prune aggressively. Every token in the context costs money. Be ruthless about what stays.
Batch Processing
Group similar requests. Share context across multiple queries. Amortize the cost of system prompts.
For a deep dive into token optimization strategies, see our guide on building a token economics framework.
The Light at the End of the Tunnel?
Costs are dropping 100x in places (In places we can't legitimately visit), so there's light at the end of the tunnel. Full freedom from token budgets? Ask me again in 2030.
Until then, we're all playing the same game: How to build magic while counting every token like it's 1999 and we're counting kilobytes again.
Welcome to the token economy, where intelligence is the new scarce resource.
Fighting the token cost battle?
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