AI-First Playbook: Do a Team's Work With AI (2026) | Peter Yang

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: AI-First Playbook: Do a Team's Work With AI (2026) | Peter Yang
- URL: https://www.youtube.com/watch?v=Yu0z7-KMHpo
- Topic: Creative Automation
- My current learning frame: Turn AI-First Playbook into a working note from the transcript anchors: 0:48 sets up right now that everyone is talking about is self-improving.
- Why this matters: New playlist item from Silicon Valley Girl; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:48 / Evidence 1: "right now that everyone is talking about is self-improving. And you recently built cause self-improving skills and the whole narrative is like stop prompting your AI make it figure out what to do next. Can you talk about..."
- 5:39 / Evidence 2: "working and it just >> keeps processing and nothing happens. That happened to me a couple times. And it's not just Godex. I think it's the context field that is not designed for a 10-minute >> prompt. And..."
- 8:53 / Evidence 3: "builder cuz you know I I spent a decade of my career just building products inside big companies and I want to now that we have all these a agents and tokens we can use. I want to..."
- 11:05 / Evidence 4: "engineers wanted to actually read read. So, so now with all these tools like lovable and like um replet and you know codeex and everything else, you can actually build prototypes of your uh products. So like I..."
- 12:43 / Evidence 5: "like this is like a programming >> sounds very nerdy. Yeah. >> Yeah. But in reality, it's just like you're still just chatting with the AI, right? And like when I use Codex, like 80% of the time..."
- 14:59 / Evidence 6: "schedule stuff on the inventory and all that kind of stuff. Yeah. So so I guess the TRD is like uh these workflows are done by connecting different skills together that that you build and like for all..."
- 23:31 / Evidence 7: "one really build something like a strategic mindset. Yeah. behind all of your AIS and projects and agents. What are the next steps? >> So step number one is as I said like to just actually use codeex..."

Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A creative workflow board with critique criteria and review checkpoints.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Brief -> Source -> Generation -> Selection -> Edit -> Taste Review
   - 3 concrete examples that apply the video idea to real agentic work
   - 2 failure modes the video helps prevent
   - a checklist I can use the next time I run Codex or Claude
   - one practical exercise with a clear done signal
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.

Quality bar:
- Make this specific to "AI-First Playbook: Do a Team's Work With AI (2026) | Peter Yang", not a generic Creative Automation essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.