This week OpenAI had a GPT-6.1 Astra upgrade ready to ship. Then they shelved it.⁉️ Not because it wasn't smart enough. Because it didn't reliably follow instructions.🧑‍⚖️ Right after, they released GPT-6.1 Sol, a much cheaper model at $2 per million input tokens and $10 per million output. The message is clear: 🎞️ raw intelligence is getting cheap. Reliable results are getting valuable. Two other stories this week point the same way. 🪪 Security: Microsoft's new Digital Defense Report says phishing made up 23% of intrusions in 2026, up from 7% in 2025. The time from a vulnerability being found to being exploited is now under 24 hours. AI speeds up attackers too. 🕹️ Jobs: Stanford looked at millions of payroll records. No sign yet of mass AI job loss. But workers aged 22–25 in AI-exposed jobs are about 19% below where they'd otherwise be, mostly because companies are hiring fewer of them, not firing them. Indeed found entry-level postings in those fields dropped from 29% of listings in 2021 to 10% in 2026. To be fair, none of this proves AI caused it. Stanford says so directly. But here's what I take from it: - ✅ The value is shifting from knowing things to judgment. "I know how to use AI" is about to sound like "I know how to use Google." The questions that will matter: - Can you get a result that works every time? - Can you tell when AI is wrong? - Do you understand the problem before you hand it to AI? - Can you build something people actually trust? - Can you prove it? My rule: ✅ Proof over power. The most powerful AI model isn't automatically the best model. The person with the most AI certificates isn't automatically the most valuable employee. And the company deploying the most AI isn't automatically the smartest company. Capability without control is risk. Knowledge without execution is noise. AI without judgment is leverage without direction. If I were entering the workforce today, I wouldn't spend all my time collecting AI certificates.