Justin McCarthy joins me to talk about what happened after he told a team to stop writing code—and then discovered they had to stop reading it too.
Justin is the founder of Diffusion and the former co-founder and CTO of StrongDM. His team at StrongDM built one of the clearest examples of a working software factory: humans define goals and the shape of the feedback system, while agents do the implementation work. The provocative rule was no human-written code. The consequential discovery was that production moved too quickly for human code review, so trust had to move somewhere else.
That is where the conversation starts: if nobody is reading the code, how do you know the factory is building the right thing? We get into goals, loops, scenarios, expensive tokens from the real world, and Justin’s idea of a decision theater—an environment designed to help a person build conviction and make a judgment quickly.
From there we talk about the gap between wall time and token time, why desire may matter more than job title, what Attractor taught Justin about deterministic control around open-ended model calls, and why the right response to cheaper production is not a smaller P&L. It is a much larger ambition.
Key Topics Covered
The agentic moment: Why Justin dates the shift to Claude 3.5 Sonnet’s second release and Cursor’s YOLO mode, when software first started getting built from another room.
No human-written code: How a hard constraint forced the StrongDM team to rethink software production from first principles.
No human code review: Why production speed made source inspection infeasible and pushed trust into goals, feedback loops, scenarios, and external validation.
Goals and expensive tokens: Why the richest signal is often a real customer response and how to build cheaper proxies before paying for it.
Decision theaters: How multimodal models can turn future scenarios into interfaces where human judgment operates in seconds rather than weeks.
Token time versus wall time: What should flow automatically after a decision and where deliberate human cognitive latency still belongs.
Desire strongly: Why Justin thinks obsession and the ability to depict a desired future matter more than a particular professional background.
Language and prior art: How vocabulary, voice, computing concepts, and concrete implementation references help people evoke better agent behavior.
Attractor and deterministic control: Why context-window-sized work, explicit state, and model-judged transitions remain useful around open-ended model calls.
Discovery versus ordering: Why Justin prompts when he is discovering what he wants, but hands off a finished outcome document once the vision is clear.
Natural-language specifications: Why StrongDM published the shape of a harness rather than committing to maintain another open-source implementation.
Ambition over efficiency: Why cheaper production should make larger goals possible instead of merely shrinking costs.
Timestamps
00:00 - Opening
00:10 - Justin McCarthy’s introduction
01:07 - From StrongDM and cybersecurity to the agentic moment
02:33 - Why October 2024 was the real agentic threshold
03:38 - Cursor’s YOLO mode and software built from another room
08:10 - Claude Code and model-market-harness fit
11:28 - Computation, companies, governments, and old management books
13:06 - No human-written code becomes no human code review
15:30 - Goals, loops, feedback, and definitions of done
16:32 - Expensive tokens and measurements from the real world
18:52 - Decision theaters and depicting possible futures
23:47 - Price signals, competition, and how large companies wake up
25:33 - Agent-written messages and why sending slop is disrespectful
27:23 - Craft, identity, exhaustion, and hope
28:18 - Software factories, software companies, and alignment
29:44 - Who is best equipped to work with agents?
31:38 - Desire strongly
33:40 - Vocabulary, Midjourney, and the Gell-Mann amnesia problem
34:59 - SICP, Redis, and speaking in the language of computation
35:57 - Attractor, context windows, and deterministic control flow
39:53 - Discovery mode versus ordering a known outcome
40:37 - Throwaway web pages and decision interfaces
43:53 - Never drag it back in: building the collaborative loop
44:55 - Natural-language specifications and disposable harnesses
47:14 - Deliberate cognitive latency and the Toyota Production System
49:14 - The Goal, ambition, and why efficiency cannot be the goal
Links & References
Justin and Diffusion
Software factories and agent systems
Books and concepts
About Forward Deployed
Forward Deployed is a podcast about the intersection of AI, software development, and the enterprise. Subscribe if you are trying to understand what it means to build AI systems that work in the real world: systems with context, evaluation, workflows, failure modes, and some theory of how people and agents stay aligned.
