Attention Deficit Ep. 1 – Welcome to Attention Deficit

Two engineers sit down every week to talk about what actually happened in AI. Not press releases, but articles, tools, and topics that make you stop and think.

In the first episode of Attention Deficit, Alexa Griffith and Taylor Dolezal dig into agent failure modes. Zombie agents, rogue agents, and why a coding agent is really just six functions in a trench coat. They get into the OpenAI Jalapeno chip and what custom silicon means for NVIDIA’s hold on inference. And they talk about whether usage-based billing is about to make AI a lot more expensive for everyone.

They also demo a closet app built entirely with AI coding agents, debate whether the Codex Micro keyboard matters, and talk about what it means to build a community around learning in public.

Topics discussed

* Agent failure modes and why agents fail like distributed systems

* The “six functions in a trench coat” framework for understanding coding agents

* OpenAI Jalapeño chip and what custom Application-Specific Integrated Circuits mean for inference

* NVIDIA market dominance and Compute Unified Device Architecture (CUDA) lock-in

* On-prem vs cloud GPU economics and hardware optimization layers

* Usage-based billing and subscription fatigue across AI tooling

* Building personal projects with AI coding agents

* Forward Deployed Engineers as an emerging role

* Community-driven learning in the AI space

Key Takeaways

* A coding agent is six functions in a trench coat: read, write, run, edit, list, search. The intelligence is in the model, not the tooling.

* Agent failures look like distributed system failures because agents are distributed systems. The failure modes are not new, just repackaged.

* Agents make starting easy, so you end up with fifteen tasks that are each 20% done and nothing finished. Finishing is the bottleneck.

* OpenAI building custom inference silicon signals that compute costs are a permanent constraint, not a temporary growing pain.

* Even Microsoft, which owns its own hardware, has moved away from flat-rate pricing for AI tools. That is your sign.

* CUDA lock-in is NVIDIA’s real moat. The hardware is replaceable. The ecosystem is not.

Resources from this episode

Hadley Wickham, “A Coding Agent Is Six Functions in a Trench Coat”: https://tidydesign.substack.com/p/a-coding-agent-is-six-functions-in

Mahesh Balakrishnan, “Your Agent is a Distributed System”: https://maheshba.bitbucket.io/blog/2026/04/24/agentfailures.html

OpenAI Jalapeno announcement: https://openai.com/index/openai-broadcom-jalapeno-inference-chip/

Learn more about the hosts

Alexa Griffith

Website: https://alexagriffith.com/

LinkedIn: https://www.linkedin.com/in/alexa-griffith/

X/Twitter: https://x.com/alexa_griffith_

Taylor Dolezal

Website: https://onlydole.dev/

LinkedIn: https://www.linkedin.com/in/onlydole/

X/Twitter: https://x.com/onlydole

GitHub: https://github.com/onlydole