This is speculation and transcript via Claude Sonnet 4 starting with “once the ‘free’ or loss leader phase of the LLM wave has ended, what is the likely pricing for a single user assuming the same allocation levels” For more of blog post style, see The End of the AI Gold Rush: Why Your Next Raise Might Come From an Unexpected Source

Executive Summary

This analysis examines the likely pricing trajectory for Large Language Models (LLMs) once the current “loss leader” phase ends, and explores how this will affect the market value of experienced software engineers. Key findings suggest current subsidies are 95-99% of true costs, with dramatic implications for market access and engineering compensation.

Current LLM Pricing Baseline (2025)

Most major providers currently charge around $20/month for premium individual subscriptions:

• ChatGPT Plus: $20/month
• Claude Pro: $20/month
• Higher-tier options: Claude Max ($100-200/month), ChatGPT Pro ($200/month)

The True Cost Reality

Initial Conservative Estimates Were Wrong

Originally estimated: 2-3x price increase to $40-60/month

• This assumed modest subsidy correction
• Focused on market-bearable pricing rather than cost recovery

Reality Check: The subsidy is much deeper

• Current $20/month likely covers only 10-20% of true infrastructure costs
• True cost recovery probably requires 5-15x current pricing
• Actual subsidy appears to be 80-90% of real costs

Infrastructure Cost Drivers

1. Compute costs: Running frontier models costs thousands per hour in GPU time
2. R&D amortization: Billions spent on model development need recovery
3. Infrastructure scaling: Massive data center investments required
4. Talent costs: AI researchers command extremely high salaries

“Best Practices” Usage Amplifies Costs Dramatically

Current Token Pricing Reality

• GPT-4o: $2.50 per million input tokens
• o1: $15 per million input tokens
• Premium models (GPT-4.5): $75 per million input tokens

Typical “Power User” Session Breakdown

Large Context Documents:

• Technical documentation: 50K-200K tokens per session
• Research papers with references: 100K-300K tokens
• Code repositories: 200K-500K tokens per analysis

Prompt Libraries:

• System prompts: 5K-20K tokens per interaction
• Few-shot examples: 10K-50K tokens
• Chain-of-thought templates: 20K-100K tokens

Conservative power user session: ~350K tokens total

Real Cost Per Session

• GPT-4o: $0.875 per session
• o1: $5.25 per session
• GPT-4.5: $26.25 per session

Heavy daily usage (10 sessions/day):

• GPT-4o: $260/month
• o1: $1,575/month
• Premium models: $7,875/month

The Actual Subsidy: 95-99%

Current unlimited subscriptions absorb costs that would actually be:

• Moderate power users: $500-2,000/month
• Heavy context users: $2,000-8,000/month
• Enterprise-grade patterns: $10,000+/month

Post-Subsidy Pricing Scenarios

Most Likely Future Pricing Structure

Basic Tiers ($50-200/month):

• Strict token limits (100K-500K tokens/month)
• Pay-per-token pricing above limits
• Suitable for light usage only

Premium Unlimited ($500-2,000/month):

• Current usage levels maintained
• Target market: enterprises and high-revenue consultants

Enterprise Tiers ($5,000-20,000/month per user):

• Advanced features and priority access
• Full cost recovery plus healthy margins

Market Impact on Software Engineering

Market Segmentation Post-Subsidy

Tier 1: Premium AI Users ($500-2000/month)

• Large enterprises with AI budgets
• High-revenue startups
• Premium consultants
• Access to AI handling 70-90% of coding tasks

Tier 2: Limited AI Users ($50-200/month)

• Mid-market companies
• Most individual developers
• Budget-conscious startups
• Basic AI with strict usage limits

Tier 3: Minimal/No AI Access

• Cost-conscious businesses
• Personal projects
• Developing markets
• Return to traditional development methods

Impact on Engineer Value by Experience Level

Highly Experienced Engineers: VALUE INCREASES

Conservative estimate: 20-40% salary premium

• Premium AI users still need experts for 10-30% of problems AI can’t solve
• Scarcity increases as fewer people access unlimited AI training

Moderate estimate: 50-100% premium

• AI handles routine work, but complex problems remain human domain
• Architecture, debugging, and system design become premium skills

Aggressive estimate: 200%+ premium

• True AI-resistant skills command exponential premiums
• Cross-domain expertise and novel problem-solving highly valued

Mid-Level Engineers: BIFURCATION

• Those with premium AI access become highly productive
• Those without fall behind rapidly
• Creates “have/have-not” dynamic in skill development

Junior Engineers: CHALLENGING POSITION

• Premium AI makes some junior roles redundant
• Limited AI access reduces learning acceleration
• Entry barriers potentially increase

The “AI Native” Skills Premium

Engineers who master these areas will see exponential value increases:

• Prompt engineering and AI workflow optimization
• Problems requiring deep system thinking
• Integration of multiple complex systems
• Novel algorithm development
• Performance optimization at scale

Key Implications

For Individual Engineers

1. Skill Strategy: Focus on AI-resistant complex problem-solving
2. Access Strategy: Consider premium AI subscriptions as career investments
3. Market Positioning: Differentiate on problems AI cannot solve

For Companies

1. Budget Planning: Factor realistic AI costs into development budgets
2. Talent Strategy: Premium for engineers who can work with limited AI
3. Competitive Advantage: Early adoption of realistic AI economics

For the Industry

1. Wage Stratification: Increasing gap between senior and junior compensation
2. Access Inequality: AI capabilities become privilege of well-funded entities
3. Market Dynamics: “AI native” skills command significant premiums

Conclusion

The current LLM pricing model represents one of the largest subsidies in technology history, with providers absorbing 95-99% of true costs. When this ends, market access will stratify dramatically, creating significant opportunities for experienced engineers who can solve the complex problems that remain beyond AI capabilities.

The irony: expensive AI access will likely increase rather than decrease the wage gap between senior and junior engineers, as the democratizing effect of unlimited AI assistance gives way to a more traditional scarcity-based market for advanced technical skills.

---

Analysis based on current market data as of August 2025, with pricing estimates derived from API costs and infrastructure requirements.