The conversation explores why regional SMEs face a different AI adoption gap than metro businesses.

Why AI enables his advisors to get out of the office and sit at kitchen tables having real conversations.

The paradox: more AI means hiring more humans, because human judgment and attention don't scale with processing power. The conversation goes deep on corporate extraction vs SME capitalism, the "tidal wave going out" before the tsunami hits regional Australia.

**Key Moments:**

- [03:50] "In regional Australia, finding work's not the issue. The challenge is how do you engage the right people."

- [06:05] The tsunami metaphor: "The tide's going out and everyone's walking on the beach going, look at the seashells"

- [12:59] "For us as a society, we need to decentralize leadership"

- [17:05] X Plan dominance: "$110 million of $200 million in fintech revenue. One login can cost $12,000 per year."

- [22:24] "There's not many things in your business more valuable than your data now"

- [29:11] The Hispanic Trump voter question from Harvard: "Why would somebody in regional Australia vote for a female version of Donald Trump?"

- [37:17] "Your core value generating part of your business now sits in Anthropic's data center and the guy that runs it is telling you he will take your job"

- [42:05] "All I've seen in our business is we need more humans that can use AI"

- [51:10] "I'd rather be wrong and human than right and AI"

- [53:37] Kane's constraint theory: "Shoot a bullet before you get the bazooka out"

- [1:00:03] NVIDIA stat: "Same size as the top 324 Australian companies combined by market cap"

- [1:02:28] "I wouldn't take the pill. Why would I want to cut the heads of ten of my favorite work brothers and sisters?"

**Friends of the Pod:** Kane (guest), Gabe (Adapt/Lumia connection), Bill Withers (SME capitalism conversations), Daniel (Kane's tech mate), Professor Boris Gruwski (Harvard), Professor Rari (the Hispanic Trump voter question)

Yo joins from Sovereign Engineering to talk about how AI has transformed freedom tech development since SEC-04, when Paul demoed a wallet vibed into existence in 30 minutes and blew everyone's minds.

The conversation covers the euphoria wearing off, the slot machine addiction problem, why LLMs are "extremely confident, extremely good at English usage, but extremely dumb," and the coming IPO exit liquidity dump. Uber burned their entire annual AI budget in four months and shipped nothing. Claude Code still has that terminal flicker bug from week two. Coding is not solved. But used as tools within their constraints? These things are genuinely great. FIPS, the peer-to-peer internet architecture routing to Nostr identities instead of IPs, was largely written by Claude, but every single line got human review. That's the model: Claude as a team member with specific jobs, not the entire dev team. YOLO++ kicks off July 20th.

**Key Moments:**

- [02:47] Paul's 30-minute wallet demo at SECO 4: "Everybody was blown away by this capability"

- [04:14] "There's no hope if I am to come back to programming by hand. I'm not going to produce anything."

- [07:11] Cryptography and AI: "It really, really fucks up. It always wants to do its own cryptography."

- [08:13] "If you are a reasonably good programmer, you would always trust yourself more than you would trust the AI"

- [10:59] The study where LLMs overtook a codebase: "Absolutely unrecognizable, unmaintainable without the LLM"

- [12:52] "As far as Dario is concerned, coding is largely solved. I'm not a good programmer, but I don't see it solving my problems at all."

- [23:25] "The euphoria is wearing off. The slot machine is just too addictive. People are tired."

- [25:11] IPO bubble discussion: "It should be known by now that it's exit liquidity"

- [27:35] 401k rule changes: waiting periods reduced from months to 15 days to force passive buying

- [31:01] SECO 5 prediction: specialized local models for Git, bash, commits, PRs—"We didn't really get those, did we?"

- [38:02] Uber's AI disaster: "Their year's budget, they finished in four months. What did we actually ship? Nothing."

- [42:17] Claude Code terminal flicker: "After coding's been solved for nine months, they still haven't fixed it"

- [48:57] FIPS written with Claude: "Every single line of code is getting reviewed. Claude is one of the members of the development team."

- [55:21] FIPS architecture: Nostr identities convert to IPv6, devices identify and connect peer-to-peer

- [59:10] Cross-pollination at cohorts: "Everybody on Wednesday is talking about their AI setup, their workflow"

**Friends of the Pod:** Yo (guest), Paul (OG wallet demo), Gigi (pipeline workflow, phone recordings), Marty Malmi (laptop wanderer, Nostr VPN), Mitchell Hashimoto (spending money to figure out where AI works), Lighthazard (AI for examples, not library code), Jonathan (FIPS creator), Aryan (FIPS collaborator), PrimaGene (Claude Code flicker video), James Checkmatey (bubble commentary)

**Projects Mentioned:** FIPS (peer-to-peer Nostr-addressed internet), Tollgate, Wingman (now on v4 with declarative workflow system), Zap Store

**Quote:** "These LLMs are extremely confident, extremely good at English usage, but extremely dumb. They have a lot of information, but they don't know how to weigh that information, how to use that information, and what are the consequences of taking certain actions."

# The Good Stuff, Episode 61: Loop Engineering

Boris (Claude Code creator) and Steinberger both tweeted this week: "I don't prompt anymore, I just build loops that prompt for me." Pete's response: they've discovered being a team leader.

Loop engineering is organizational design with new hype marketing terms—triggers, processes, business rules, the stuff we've been doing for a thousand years. The conversation explores where humans actually fit in these loops (spoiler: you can't be hands-off), the coin flip problem of compounding agent decisions, and why running agents for a day with no human interference produces drift toward suboptimal forks. Vision, values, and principles aren't just for humans-they're how you scale decision-making when you can't review every choice.

Also covered: the bubble phase of AI where we're shitting money into the pool instead of making things efficient, Apple's WWDC local LLM play (MDX protocol, neural accelerators), and why the $50/month product gap is so hard to close.

**Key Moments:**

- [01:01] Boris tweet: "I don't prompt anymore. I just build loops that prompt for me."

- [01:26] "They've discovered being a team leader. Basically."

- [06:07] "Business intelligence layer—it sounds sexy. But for 20 years there's always been a project in every business that was 'we should just have one big database.'"

- [07:50] "It's discovering that instead of coding, maybe we should be thinking about coding methodologies"

- [09:04] "What's interesting about agents is they move quick enough that you can put more iterations in. So it does feel loopy."

- [12:20] The coin flip problem: "If you've got 50 sub-agents, each making a judgment call, that's another coin toss. Your likelihood of getting heads nine times out of ten has just been obliterated."

- [14:21] "OpenAI pays his bills. At that point it doesn't matter what you're spending."

- [16:37] Vision, values, principles: "Here's how we make decisions. Every decision, you need to be thinking about these."

- [18:22] "Your AI does not have the same intuition for where you're going that you do. When you sit with it, it steers right. When you don't, it doesn't."

- [24:03] Shopify's five-person team ideal: "It's just because you can have tons of people doesn't mean you should"

- [29:29] "It's not all about the volume. It's always been about differentiation and taste and specific useful output."

- [40:47] Mythos/Fable: "It told me it was going to burn credits at twice the rate"

- [51:24] The bubble: "We're in this bubbly cash grab. We haven't done any of the interesting engineering work about making this efficient instead of making it bigger."

- [55:44] "I don't need more intelligence. I need better application to the problem."

**Friends of the Pod:** DeadmanOz, Gav, Mark, Peter Levels, Toby from Shopify.

**New Terms Coined:** The Right Porridge (Goldilocks context management), Loop Engineering (what we're calling it for now)

**Quote:** "We seem to be speed-running, the realization that the all of the organizational management infrastructure we've developed over the last thousand years was useful. It's there for a reason. We've not arrived there accidentally, we're now just reinventing it for agents with new names."

Pete and Andy are joined again by Deadman Oz / AM to talk through the latest AI trends, starting with the argument over whether progress is really coming from better models or better harnesses.

They cover Claude Code workflows, agent orchestration, context management, reasoning models, open source local models, inference economics, and the business strategies of OpenAI, Anthropic, Mistral, and the Chinese labs.

They close by taking the same question into robotics: where automation pays off, whether humanoid robots make sense, and why space robots feel less unsettling than house robots.

## Chapters and Themes

- `00:00-02:18` Deadman returns and the group sets up the core argument: is AI progress mostly model capability, or the harness around the model?

- `02:18-05:50` Pete explains why he is bullish on pipelines and declarative workflows, while Anthony describes using workflows for Bitcoin-adjacent research.

- `05:50-10:44` Workflows, agents, and context windows: where they help, where they fail, and why parallel work can make humans lose the thread.

- `10:44-14:59` Pete argues that agent-driven control loops introduce randomness, so humans still need to keep agents on the motorway.

- `14:59-21:55` Anthony makes the case for stronger reasoning models, using recent AI-assisted maths results as an example of cross-field insight.

- `21:55-24:15` Pete questions whether models can self-correct without a clear internal goal, especially when the human cannot fully specify the destination.

- `24:15-32:14` OpenAI, Anthropic, product focus, influencer hype, doom messaging, enterprise lock-in, and regulatory self-interest.

- `32:14-37:54` Local LLMs, Chinese open models, Mistral's enterprise focus, and whether frontier models commoditise too quickly to justify the spend.

- `37:54-40:58` Auto-research, self-improving training loops, and whether ever-higher intelligence might become too abstract for ordinary tasks.

- `40:58-49:47` Robotics, ports, unions, mining, household chores, and whether automation is blocked more by politics than technology.

- `49:47-56:05` Humanoid robots versus specific tools, open robot ecosystems, space robots, and the more optimistic version of an AI future.

## Key Takeaways

- The harness, workflow, and orchestration layer shape what models can actually do.

- Parallel agents are useful for breadth, but they create attention and coordination problems.

- Reasoning may help agents stay on track, but goal definition and judgment remain hard to outsource.

- Anthropic may be product-focused, but hype, opacity, and regulatory self-interest still make trust difficult.

- Open source and local models are increasingly good enough when the surrounding system is well designed.

- Frontier-model economics are awkward: inference can be profitable, while training races make breakthroughs short-lived.

- Robotics may deliver clearer value in infrastructure, mining, manufacturing, and space than in humanoid house helpers.

## Notable Lines

- "I think it's more harness than model."

- "It worked with the old model, but you needed to think about how you actually work."

- "The computer is the computer and it can talk."

- "If you don't have to do any of the upfront training, the margins on the inference is insane."

- "I probably don't need Mythos to continually say what's the git command for..."

- "Space robot seems fine."

Pete and Andy unpack the recent surge in anti-AI sentiment and argue that the anger is often aimed at the wrong target. They trace the backlash through graduate job anxiety, elite AI messaging, economic stagnation, and broader distrust in institutions, then pivot into a more grounded discussion about what AI can and cannot realistically replace. Along the way they talk about cost curves, productivity myths, design, taste, originality, and why the enduring value in human work may sit less in task execution and more in judgment, experience, and problem selection.

## Chapters and Themes

- Opening on the visible rise in AI hostility, from graduation-ceremony boos to a broader sense that AI has become a cultural punching bag.

- Why the backlash feels understandable: people hear wealthy AI leaders talking about job losses while younger workers and graduates already feel economically cornered.

- The difference between hating AI itself and hating the incentives, theft, corruption, and concentrated power associated with the companies leading the current wave.

- Why "human-made" branding may grow, but why avoiding AI entirely will be hard when customers still buy on price, speed, and output.

- AI job-loss narratives versus practical constraints: compute, energy, token costs, and whether replacing real workers is actually economical at scale.

- The gap between claims of 10x or 100x productivity and the lack of obvious real-world evidence in company performance and output.

- Why some work may become cheaper and more automated, while the scarce layer shifts toward framing problems, exercising judgment, and understanding real user needs.

- A closing reflection on design, taste, originality, and how creative work changes once the old technical bottlenecks become commoditized.

## Key Takeaways

- Much of the current anger around AI is really anger about economic insecurity and untrustworthy institutions.

- Anti-AI sentiment may be emotionally understandable without being strategically useful.

- The economics of AI replacement are still far less obvious than the rhetoric suggests.

- Cheap task execution does not remove the need for judgment, taste, and lived experience.

- The enduring moat in creative and technical work may be knowing what matters, not just producing outputs faster.

- Local, controllable, human-directed AI remains a healthier direction than total dependence on centralized providers.

## Notable Lines

- "Go support your local homegrown organic AI project."

- "Everybody else's job is really easy to automate, right? Except mine."

- "Good developers were never about the code."

Pete and Andy are joined by Shawn Yeager to talk about what AI does to professional services firms once the billable hour starts collapsing.

The core argument is that firms in law, accounting, marketing, and similar sectors will need to move away from selling time and toward selling trust, judgment, systems, and outcomes.

As AI compresses document work and routine delivery, the real value shifts to client relationships, productized services, workflow design, and the ability to package expertise in ways that scale.

## Chapters and Themes

- `00:00-05:35` Shawn introduces his thesis: AI is killing the billable hour, forcing professional services firms to rethink what they sell after time-based work gets compressed.

- `05:35-10:29` The discussion moves to outcomes versus hours. Shawn argues the real value in legal and other services has always been trust, judgment, and knowing what to do when things go wrong.

- `10:29-14:14` Pete and Shawn explore how AI changes delivery by making it easier for non-specialists to generate quality work when the right systems, files, and scaffolding exist.

- `14:14-18:07` They discuss the rise of the product-minded generalist. Shawn argues everyone increasingly needs product thinking: customer empathy, technical awareness, and the ability to shape deliverables into something sellable.

- `18:07-20:19` Andy raises the firm-structure question: if AI flattens the old professional services pyramid, what happens to the layers of junior talent and middle management?

- `20:19-22:52` Shawn and Andy dig into the talent-pipeline problem. If firms become flatter and more system-driven, they may struggle to develop the next generation of experienced practitioners.

## Key Takeaways

- AI is compressing the work that used to justify the billable hour.

- Professional services firms will need to sell trust, judgment, and outcomes rather than time.

- Product thinking is becoming a core skill, even in traditional service businesses.

- Smaller, high-agency teams can now do work that previously required much larger firms.

- The long-term challenge may be talent development, especially in heavily regulated professions.

## Notable Lines

- “AI killed the billable hour.”

- “Ultimately what they sell is trust.”

- “What would you do if you had five or ten more employees?”

- “I think everyone’s got to be a product manager now.”

Pete and Andy are joined by Mark from Maple to talk about privacy in AI, why everyday users may finally be starting to care, and what it takes to build private AI products that can still compete on user experience.

The conversation ranges from end-to-end encrypted AI and open source model stacks through to agent security, business control, workflow automation, and why most of today's agent hype still feels more like fake sizzle than finished sausage.

## Chapters and Themes

- `00:00-03:26` Mark introduces Maple as a private AI product, and the conversation opens on why privacy matters online and whether normal users actually care.

- `03:26-07:51` Competing with ChatGPT and Claude means winning on UX first, with privacy as the extra unlock for more personal or sensitive use cases.

- `07:51-14:46` Kids, AI companions, model bias, and the quieter long-term risk of AI shaping how people think rather than just what they know.

- `14:46-22:34` Open source models, open source harnesses, and why visibility into prompts, middleware, and agent behavior matters.

- `22:34-29:50` Maple's roadmap, Wingman's architecture, and the difference between consumer AI products and SME-focused agent orchestration.

- `29:50-38:14` Why privacy is often mispriced by businesses, and why control may be the stronger commercial framing than privacy alone.

- `38:14-49:38` Are AIs actually replacing jobs, or just making small teams more capable and more capital efficient?

- `49:38-58:07` OpenClaw, determinism, pipelines, memory, and the "lethal trifecta" of private data, inbound internet, and outbound internet access.

- `58:07-01:11:16` Segregated scopes, agent permissions, enterprise information boundaries, and whether central AI intelligence is the right architecture at all.

- `01:11:16-01:44:23` Jack Dorsey's intelligence layer, agent gossip, software-defined businesses, and a closing detour into British accents, Siri, and bedtime podcast energy.

## Key Takeaways

- Privacy only matters commercially if the product experience is good enough to compete.

- For many businesses, `control` may be a clearer selling point than privacy on its own.

- Open source models are not enough; the harness and surrounding tooling matter just as much.

- Most agent hype still breaks down when reliability, repeatability, and permissions matter.

- The real opportunity may be software and pipelines generated by AI, not agents acting unchecked.

- Businesses will need scoped agents, clear approvals, and tighter boundaries than today's demos suggest.

## Notable Lines

- "It's similar to ChatGPT... but we do it end-to-end encrypted."

- "I think the agent is the sizzle, not the sausage."

Pete and Andy dig into "unruggable productivity" and what it means to build software that respects the customer instead of trapping them inside a vendor's AI stack.

They connect recent 37signals ideas to their own work on Wingman and Flight Deck, arguing for agent-friendly software, clearer work surfaces, and business systems designed around control, portability, and real workflows.

## Chapters and Themes

- `00:00-05:06` Opening on 37signals, Rework, and whether software should embed its own agent or let users bring their own.

- `05:06-08:54` Why chat is a bad place for structured follow-up, and why forms may be a better primitive for agents gathering information.

- `08:54-14:17` Chats, tasks, and documents as different work surfaces with different jobs inside Flight Deck.

- `14:17-21:06` "Unruggable productivity" as positioning: software that respects you and does not hold your business hostage.

- `21:06-31:16` Venture-backed software incentives, authentic marketing, and finding a values-aligned audience instead of chasing everyone.

- `31:16-37:11` Product design tradeoffs around control, self-hosting, onboarding, and releasing sooner with a narrower target market.

- `37:11-45:03` Whether the highest leverage move is selling the tool or using it to launch workflow-native challenger businesses.

- `45:03-55:03` Examples, reflection loops, and pipeline-based automation as the real path to better AI output.

- `55:03-01:07:20` Why chat cannot be the whole interface, and why future businesses will need many constrained agents instead of one all-knowing assistant.

## Key Takeaways

- The best AI software may be agent-friendly, not agent-controlled.

- Chat is useful for exploration, but not for all forms of work.

- Software should preserve customer control instead of increasing dependency.

- Narrow markets and strong principles may beat broad generic positioning.

- Examples, reflection, and structured pipelines matter more than prompt tricks.

- Businesses will likely need multiple agents with limited context and clear boundaries.

## Notable Lines

- "Software that respects you."

- "I should not have an off switch for your business."

- "You should have the agent work where the work is."