Why 95% of AI Pilots Fail (And How to Fix It)

Fable 5 Banned: The Multi-Model Escape Plan

Anthropic launched Claude Fable 5 with huge expectations, only to see the US government order it pulled globally three days later. Tim and Paul dig into the swirling conspiracy theories: was it retaliation for refusing to arm the Pentagon? Did a competitor exploit a jailbreak report to kneecap a rival? And did Anthropic’s own transparency accidentally hand over the rope? Then the conversation pivots to token anxiety, ballooning API costs, and the open-source models like GLM 5.2 and DeepSeek V4 Pro that now rival proprietary giants at a fraction of the price. The episode’s core insight: a three-stage workflow—planning with a flagship model, implementing with a cheap or local one, and reviewing with a third—lets developers escape single-point-of-failure risks and spiraling bills, and it's already taking shape across the coding community.

AI Didn't Invent These Problems

Tim and Paul break down Anthropic's Fable 5 pricing disconnect, the dav1d assembly decoder that outraced higher-level implementations, and why Agile's 2001 playbook stumbles when agents build apps in hours. They critique the hype around autonomous agent loops, highlighting the real constraints—budgets, tests, and decision quality—that determine whether AI accelerates value or just incinerates tokens. It's a tight hour on the shifting boundaries of craft, process, and the problems AI reveals but can't solve on its own.

Why 95% of AI Pilots Fail (And How to Fix It)

Tim kicks things off with an AWS agent nightmare that couldn't tell dev from prod, sparking a deep dive into where deterministic pipelines end and true LLM reasoning begins. Using a clever flight-tracking case study, the hosts map out when to use frontier models, local open-weight models, or no AI at all—then connect it all to an MIT study showing 95% of generative AI pilots fail to deliver profit, often because companies treat the API bill itself as a success metric. If you're wrestling with agentic vs. scripted workflows, bloated AI spend, or just an editor that can't keep up, this conversation offers a clearer lens for building with intention.

The Tools That Got Us Here Won't Get Us There

Tim and Paul dissect why CI/CD pipelines are buckling under the speed of AI-generated code, sharing strategies like pre-commit hooks, intelligent test selection, and ephemeral preview environments to survive the new velocity. Then Paul makes the case that Cursor’s reliance on VS Code could doom it; and they debate whether SpaceX’s $60 billion option to acquire the editor will be a lifeline or a chaos bomb. This episode is a candid look at how the entire developer toolchain, from pipelines to editors, is being forced to reinvent itself for the AI era.

RAG Isn't Dead, You're Just New Here

In this episode, Tim and Paul dismantle popular 'RAG is dead' and 'MCP is over' hot takes, revealing what these declarations actually say about the author's place on the AI learning curve. They explore the overlooked depth of retrieval-augmented generation and model context protocol, weigh CLI-first agent workflows against structured tool use, and share practical tips like using tmux as an agent dashboard. The takeaway: every tool has limits, but declaring a paradigm dead often means you've stopped learning—and there's a smarter way forward.