Trends

Definition

Trends coverage in this archive spans 4 posts from Jan 2025 to Nov 2026 and links technical decisions to margin, distribution, and execution durability. The strongest adjacent threads are ai, predictions, and future. Recurring title motifs include ai, cost, trends, and headed.

What the archive argues

• The posts consistently push for explicit unit economics and practical tradeoffs over narrative hype.
• The consistent theme from 2025 to 2026 is disciplined execution over hype cycles.
• This topic repeatedly intersects with ai, predictions, and future, so design choices here rarely stand alone.

Execution checklist

• Tie roadmap bets to measurable outcomes: cost, throughput, risk reduction, or revenue impact.
• Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
• When boundary questions appear, cross-read ai and predictions before committing implementation details.

Common failure modes

• Treating technical strategy as branding instead of an operating constraint.
• Running broad experiments without clear stop conditions or budget discipline.
• Applying guidance from 2025 to 2026 without revisiting assumptions as context changed.

Suggested reading path

• Start here (current state): What I Actually Expect from AI in 2026
• Then read (operating middle): AI Inference Cost Trends 2026: Model Pricing and Token Costs
• Finish with (foundational context): AI in 2025: The Year Discipline Wins

Related posts

• What I Actually Expect from AI in 2026
• AI Inference Cost Trends 2026: Model Pricing and Token Costs
• AI in 2025: The Year Discipline Wins

References

• The Twelve-Factor App
• Google SRE Book
• RFC 9110: HTTP Semantics