AI Didn't Invent These Problems

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.

AI's Demos vs. Dev Reality: The Bill Is Coming Due

Tim and Paul dissect the real story behind Anthropic's locked-down Claude Mythos and OpenAI's public GPT-5.5 release, hint: it's about compute, not danger. They expose the coming end of AI's VC-subsidized era, where users burn $8 in compute for every $1 subscription, and why investors betting on AGI magic are ignoring what developers see daily: useful tools that still hit a hard ceiling. Tune in for a reality check on the gap between the sizzle reel and the merge conflict.