Episode Details
Back to Episodes
219: "Why You Cannot Shortcut The Human Part Of AI Implementation" ft. Justin Coats
Episode 219
Published 2 months, 1 week ago
Description
Justin Coats breaks down the idea of “super apps” for AI and what that really means for corporate adoption, change management, security, and tokenomics.
🧭 Conversation Highlights
- Justin defines super apps as AI workspaces that combine model access, agents, and computer or browser use in one interface.
- Erik pushes on the human side of adoption and asks whether waiting for the tech to mature actually reduces the real cost.
- They discuss how change management needs education, executive buy-in, and AI champion teams that represent more than just IT.
- Justin explains context windows (AI attention span) and then tokenomics: tokens measure workload, credits measure billing, and measurement is still messy.
💡 Key Takeaways
- Super apps are less about a new tool and more about consolidating how work happens, which forces a new way to interact with your systems.
- Waiting for “fully baked” AI does not remove the human adoption problem. Humans are slower, so competitors build advantage while you stand by.
- Good adoption looks like: leadership using it, education for everyone, and an AI champion group embedded across functions.
- Tokenomics and context windows are becoming operational concerns. If you cannot measure and manage usage, costs and performance turn into guesswork.
❓ Questions That Mattered
- Will adopting super apps get easier from a change management standpoint, or will it keep demanding constant iteration from teams?
- How should companies think about their tech stack being partially commoditized and what pushback comes from the original tools?
- If a super app depends on a small number of operators, what happens when key people leave and the wiring breaks?
- What does it mean when an AI says the chat is too long, and how should teams manage context and summarize threads?
🗣️ Notable Quotes
- “Humans need their hand held until they don't.”
- “Context window is your AI's attention span.”
- “Tokens measure the underlying AI model's workload. Credits are the product's own usage and billing currency.”
🔗 Links & Resources