The cost of software production is collapsing. But the outcome isn’t less software — it’s more ambitious software, built by smaller teams. The trillion-dollar one-person company is real. The question is whether you have the judgment to lead it.

Gemini image generation went down at 2AM and is still down at 9:22AM—and counting. For engineering leaders building AI-augmented workflows, a multi-hour AI service outage isn’t just annoying—it’s a business continuity gap. Here’s how async design, LiteLLM/OpenRouter fallbacks, and local models close it.

Part 3 of a conversation with Claude Opus 4.6. After mapping the scenarios and the odds, I pushed for something more actionable: what actually moves the needle?

Part 2 of a conversation with Claude Opus 4.6. We got past the developer questions and into the bigger ones: UBI, blue collar automation, and what odds the AI gives us of coming out okay.

I asked Claude Opus 4.6 what happens when building software costs nothing. The answer was more honest — and more uncomfortable — than I expected.

When the Stack Stops It’s 2:00 PM on February 9th, 2026. I’m in flow—code is shipping, tests are green, the AI assistant is humming along. Then: nothing. I can’t push or pull code—the entire AI-augmented development workflow is frozen. By 2:30 PM, service is restored. Thirty minutes of downtime. But here’s the thing: this isn’t really about GitHub. It’s about how AI-augmented development changes the calculus of vendor reliability. When your workflow depends on AI assistance maintaining context across your entire codebase, vendor downtime isn’t just inconvenient—it disrupts the exponential productivity gains that make AI development transformative. ...

After 2 failed attempts in early 2024, Claude 3.5 Sonnet (October 2024) finally had the instruction-following capability needed. Built a production deployment orchestrator in 3 days that saved weeks per use.

TL;DR: When humans validate AI output, diverse perspectives catch diverse errors. When AIs validate each other, they converge—because similar training produces similar weights, which produces similar reasoning. Temperature adds surface-level noise, not new capabilities. Genuine novelty requires evolutionary mutation: artificial DNA. Expert vs. Researcher: Two Modes of Validation I recently published a two-part series on space-based AI infrastructure . I’m not an aerospace engineer—I’m a software developer. That distinction defines how I validate AI output. ...

📌 This is Part 2 of a 2-part series: ← Part 1: Economic Analysis Part 2 (this post): Manufacturing, regulatory, and physical constraints Last Updated: February 5, 2026 ⚠️ Accuracy Disclaimer: This analysis synthesizes data from regulatory filings, manufacturing precedents, and aerospace industry reports. While we’ve made every effort to verify production rates, regulatory approvals, and physical constraints, the space manufacturing landscape evolves rapidly. Launch capacity numbers reflect current FAA approvals as of February 2026. Timeline projections are based on historical precedents from Tesla, Apollo, and Starlink programs. Readers are encouraged to verify critical details independently. ...

📌 This is Part 1 of a 2-part series: Part 1 (this post): Economic viability and cost analysis Part 2: Manufacturing Reality Check → Last Updated: February 5, 2026 ⚠️ Accuracy Disclaimer: This analysis synthesizes data from 60+ sources including SpaceX filings, FAA approvals, academic research, and industry reports. While we’ve made every effort to verify claims and cite primary sources, the rapidly evolving space industry means some figures may become outdated. Launch capacity approvals, cost projections, and timeline estimates should be treated as point-in-time assessments. When specific claims are unverified or based on company projections, we note this explicitly. Readers are encouraged to verify critical details independently. ...