AI was supposed to save time. So why does it feel like it is making us work more?

In Episode 17 of Not Brothers, Mark and Ryan unpack the AI efficiency paradox: the strange pressure to keep agents running around the clock, maximize every subscription, and constantly wonder whether someone else has built a better workflow that is leaving you behind.

Ryan talks about the practical bottleneck created when AI agents move faster than humans can review their work. Code, tasks, and deliverables can stack up in parallel—but somebody still has to verify that the output is correct, useful, and worth keeping. More production does not automatically mean more progress.

They connect AI FOMO to the older workplace obsession with being busy. If the goal is meaningful impact, then free time, strategic thinking, and the ability to step away may be better signs of success than a dashboard full of running agents.

The conversation also gets into token leaderboards, lines-of-code metrics, false productivity, AI-generated rework, and why companies will struggle to measure the economics of AI usage. Mark and Ryan argue that AI works best as an augmentation layer: remove repetitive work, improve the quality of the outcome, and free humans to do the thinking that actually matters.

The bottom line: use AI where it produces real value. Stop using it just because it is available—and do not be afraid to shut the agents down and walk away.

Chapters:

00:00 — The AI efficiency paradox

00:34 — Why faster AI can make us work longer

01:25 — AI FOMO and the pressure to run agents 24/7

02:18 — When agent output outruns human review

03:07 — Letting agents work while you step away

03:49 — AI and the new work-life integration problem

05:23 — Being busy is not the achievement

06:31 — Downtime, tinkering, and technology burnout

07:38 — Why technologists fantasize about going off-grid

09:18 — Token usage is not productivity

10:50 — Broken metrics: lines of code and AI leaderboards

13:08 — The coming economics of token efficiency

13:57 — When using AI takes longer than doing it yourself

14:46 — AI FOMO and fear of being replaced

15:46 — AI should remove busywork, not people

17:12 — A practical AI win: website content migration

18:19 — What clients actually want from agency AI use

20:25 — The cost-cutting trap and losing human expertise

21:54 — Most people are not running 15-agent companies

22:58 — Experimenting without worshipping the workflow

24:20 — Know when to stop the loop and walk away

25:11 — Wrap-up

AI loops are changing how people get real work done with agents.

In Episode 16 of Not Brothers, Mark and Ryan break down what “loops” actually mean in practice — not as vague AI hype, but as a better way to structure work: define the goal, write the success criteria, pair the builder with a reviewer, and let the system iterate until the work meets the bar.

Ryan shares an example of a development task that ran for hours with a builder agent and review agent working together. Instead of babysitting every step, he gave the system clear win conditions and let it loop through implementation, critique, and revision.

They also talk about why this is not really new. Good AI workflows are starting to look a lot like good human workflows: clear requirements, tight scope, explicit assumptions, and a shared definition of done.

The episode moves beyond software development into creative and marketing work — including social content, clips, transcripts, visual QA, and repeatable production workflows. The big idea: loops work best when they are simple, scoped, and reviewed.

Chapters:

00:00 — What are AI loops?

00:27 — Why loops are not exactly new

01:10 — From prompt-by-prompt work back to real requirements

02:58 — OpenClaw, cron jobs, and early agent loops

03:35 — GPT-5.5 and goal-oriented prompting

04:55 — A four-hour builder/reviewer agent loop

06:03 — Chunking work versus letting loops run

06:44 — Good AI workflows look like good human workflows

08:34 — Why nuance and hidden requirements still matter

09:53 — The review agent is the critical piece

10:40 — Win conditions tell the loop when to stop

12:29 — The value shifts from typing to thinking

14:01 — Solving the wrong architecture before code exists

14:54 — Why knowledge work is undervalued

15:59 — The boiler repairman and the value of experience

17:13 — Value-based billing and technology leverage

19:18 — The dopamine trap of instant AI iteration

19:43 — Prototyping first, then turning reactions into requirements

20:11 — AI can help write specs, but you still have to read them

21:50 — Applying loops to creative and social content

23:34 — Getting to 90% before humans review

24:35 — Using transcripts as requirements for content loops

25:28 — Stop staring at the harness and start defining the contract

26:26 — Start with small loops, not a 50-agent circus

27:21 — Simple workflow loops like standups, notes, and PR rebases

27:53 — Wrap-up

In this episode of Not Brothers, Ryan and Mark dig into a major shift inside modern teams: AI now lets non-technical people prototype, build, and express product ideas in ways that used to require developers, designers, and long handoff cycles.

That's powerful. It's also messy.

The upside is real. AI can take someone from a vague idea to an interactive prototype incredibly quickly, compressing wireframing, design, and prototyping into a tighter feedback loop. Designers, strategists, and PMs can create something tangible enough for the team to test and improve.

But a slick UI can create the illusion that something is "done" when there's no real infrastructure, no secure backend, and no maintainable architecture. AI is great at making something that feels real — but often it's a house of cards no responsible team can simply deploy.

The team shares what Oodle has worked through internally: oversized pull requests, skipped requirements, one-off solutions, spaghetti code, missing docs, and unclear handoffs. The big theme — AI doesn't remove the need for product thinking, it makes it more important. Ryan's example: flexible custom fields in Cortex beat hard-coding for one client. And his best metaphor: AI will bore through a concrete wall with a spoon if you ask it to, so planning still matters.

Rather than banning the tools — unrealistic, since "life finds a way" — Oodle builds guardrails: project instructions, agent rules, standards, and an internal assistant, Sheldon, that asks clarifying questions and turns vague bugs into actionable reports.

Handoff quality matters too. If you build something with AI, you still own it: explain the problem, document intent, set success criteria, and make review easy. The workflow is also inverting — technical people now ask non-technical teammates to carry prototypes further before development takes over.

The takeaway is simple: write things down. Clear writing, intent, and documentation are becoming core skills for turning ideas into real software.

Chapters

00:00 — The collision between technical and non-technical teams 
01:11 — AI gives non-technical people a new way to express ideas 
03:12 — Why “done” is harder to define now 
05:34 — When a polished UI creates the illusion of progress 
07:46 — Why AI prototypes often are not deployable 
11:20 — Guardrails, standards, and responsible AI workflows 
15:02 — Existing products make the collision more complicated 
17:22 — Product design versus one-off feature requests 
20:25 — AI will dig through concrete with a spoon 
22:05 — The problem with huge AI-generated pull requests 
24:47 — Why smaller chunks beat massive code drops 
27:57 — Compression, cleanup, and maintainability 
30:28 — Better pull requests need demos, screenshots, and context 
36:01 — What open source is teaching us about AI-generated code 
40:02 — Why banning AI is not the answer 
42:31 — How agents can improve bug reports and feedback loops 
45:17 — Don’t clean up everyone else’s AI mess 
49:09 — The workflow has flipped for idea people 
52:59 — Writing clearly is now a core AI-era skill 
55:10 — Final takeaway: write it down

Short podcast summary
Mark and Ryan dig into Herald, Oodle’s developer-native changelog and release notes platform built for teams that ship through GitHub but hate writing product updates from scratch. Ryan explains the gap he found in existing changelog tools, why release notes usually get skipped, and how Herald uses GitHub history plus AI to turn commits and pull requests into editable release drafts. They also cover GitHub sync, nested projects, scheduled releases, customizable widgets, email notifications, and user segmentation — all aimed at making product updates easier to publish and easier for users to discover.

YouTube description
Most teams ship more than they communicate.

In this episode of Not Brothers, Mark and Ryan talk through Herald — Oodle’s changelog and release notes platform for software teams that live in GitHub but hate writing release notes from scratch.

Ryan explains why changelogs are usually skipped, why existing tools did not quite fit the workflow he wanted, and how Herald turns GitHub activity into draft release notes using AI. Instead of starting with a blank page, teams can connect a repository, pull in commits and pull requests, draft a release, edit the important parts, and publish across Herald, GitHub, email, and an in-app widget.

They also get into two-way GitHub sync, public and private repositories, nested projects for related repos, scheduled releases, customizable changelog widgets, user groups, segmentation, and why discoverability matters just as much as authorship.

Herald is built for developers, product teams, indie founders, and small SaaS teams that want to keep users informed without turning release notes into another full-time job.

Try Herald: https://sendherald.com

Chapters

Description
The AI gold rush is hitting its first real hangover.

In Episode 12 of Not Brothers, Mark and Ryan talk through the gap between what AI companies promised, what executives bought into, and what the tools are actually proving they can do. The conversation starts with cloud-license cancellations, token spend, AI data-center bets, and the realization that “AI will solve everything” is not the same thing as a useful operating plan.

Ryan argues that AI is still an incredible tool — even if it never gets dramatically smarter — but the fantasy of universal automation, effortless AGI, and instant economic transformation is starting to crack. Mark pushes on the business side: why executives accepted the hype, how fiscal pressure may be changing the story, and why the next phase of AI value may come from practical application layers instead of frontier-model moonshots.

They also get into AI dopamine loops, hallucinated research, agentic coding tools, the iPhone analogy for model progress, Sam Altman softening job-replacement claims, data-center and memory-market ripple effects, Google’s AI distribution advantage, Google Workspace integration, and what AI search might do to SEO.

The takeaway: AI is not going away. The useful version is probably less magical, more embedded, more specialized, and much more dependent on human judgment than the hype cycle promised.

Chapters

Pinned comment / hook
AI is still powerful. The fantasy version is what’s getting repriced.

Tags/topics
AI, AI economics, AGI, token costs, AI agents, OpenClaw, OpenAI, Anthropic, Google Gemini, Google Workspace, AI search, SEO, data centers, jobs, automation, future of work, Not Brothers Podcast

AI can make building products faster. It does not make people care. Distribution, trust, and attention are still the real game.

Description
Building software is easier than ever. Getting anyone to care is still the hard part.

In Episode 11 of Not Brothers, Mark and Ryan dig into the modern version of “if you build it, they will come” — and why that idea breaks down fast in an AI-driven product world. Vibe coding, faster prototyping, and smaller teams have made niche software products more realistic than they used to be. But the same tools also make it easier for competitors, clones, and half-baked alternatives to show up overnight.

The real debate: has the power shifted from developers to distributors, or was distribution always the thing that separated products that survived from products that disappeared?

Mark and Ryan talk through AI-era distribution tactics including AI-friendly tools and CLIs, MCP servers, programmatic SEO, answer-engine optimization, free tools, shareable product outputs, niche newsletters, cold email ethics, and content repurposing engines. Along the way, Ryan gets predictably fired up about MCP bloat, AI slop, automated outreach, and bots talking to bots until everyone involved is just burning tokens.

The takeaway: AI can help you build faster, but it does not magically create trust, attention, demand, or distribution. If you build it, they probably will not come — unless you give them a damn good reason to.

Chapters

Does AI make us dumber, or does it just move the line between what humans need to know and what tools can handle?

In Episode 10 of Not Brothers, Mark and Ryan pick up the thread from their previous conversation about AI in education and push it further: if AI can write the paper, build the CLI app, summarize the research, and automate the busy work, what exactly are humans supposed to learn, practice, and protect?

The conversation gets into education, critical thinking, memorization, work, hobbies, purpose, the future of AI adoption, and the difference between delegating execution and outsourcing your brain.

The short answer: yes, AI can make you dumber at the thing you delegate. But that may be fine if you’re using the saved time and leverage to get smarter about the thing that actually matters.

00:00 — Does AI make us dumber?
01:02 — AI in education vs AI at work
02:27 — Delegating your brain
03:44 — What are schools actually measuring?
06:04 — Real-world skills vs academic restrictions
06:54 — Resourcefulness vs intelligence
09:23 — AI, memorization, and what we call “smart”
10:31 — Does learning need to be hard?
12:26 — Are we at another inflection point?
14:23 — Human purpose when work changes
16:23 — Universal high income and building for fun
18:00 — Retiring without a backup plan
20:25 — What do we do with AI-created time?
21:17 — Are AI models plateauing?
24:08 — The IKEA example: AI plus human judgment
25:43 — Acceptable AI use in education
27:48 — When does AI work become “mine”?
29:42 — Building blocks and the 10-year-old problem
31:07 — Handwriting, typing, and obsolete skills
33:51 — Brain development and hard things
35:52 — Why AI adoption feels faster than the internet
37:53 — Can AI or the internet be regulated?
40:15 — So, does AI make us dumber?
40:30 — Dumber at one thing, smarter at another
42:45 — Critical thinking vs subject matter expertise
44:18 — AI is best at patterned execution
46:27 — The final answer: maybe
48:24 — Are papers even the right test?
50:06 — Education needs to figure this out

The conversation delves into the ethical implications of AI use in academia, particularly in the context of overuse patterns and the balance between critical thinking and plagiarism. It also explores the role of AI as a tool in education and the challenges associated with setting boundaries and rules for its use. The conversation delves into the impact of AI on academia, particularly in the context of academic projects, senior theses, and ethical considerations. It explores the use of AI as a tool for persuasion and argumentation, its role in academic integrity, its application in business and work environments, and the maturity and ethical use of AI in education. The discussion also addresses the development of writing skills in the context of AI use.

Takeaways

• Ethical implications of AI use
• Overuse patterns in AI AI in academia
• Impact of AI on learning
• Ethical considerations in AI use

Chapters

• 00:00 Ethics and AI in Academia
• 08:55 Critical Thinking vs. Plagiarism
• 17:53 AI as a Tool in Education
• 24:29 Academic Projects and Senior Theses
• 32:10 AI in Business and Work Environments
• 38:10 AI and Writing Skills Development

In this episode of the Not Brothers Podcast, Mark and Ryan dig into one of the most important questions for entrepreneurs: are you working in your business, or on it?

Using Oodle’s long-running offsite rhythm as the backdrop, they break down how stepping away from daily execution creates space for alignment, strategic thinking, and better decision-making.

They cover how their offsites have evolved over the years, what preparation looks like, how to spot when you’ve become the bottleneck in your own business, and why intentional time away can be one of the best investments you make as a business owner. Along the way, they mix in stories from past offsites, lessons from hard pivots, and the frameworks they use to keep the business moving forward.

Chapters
00:01 Intro, working on vs. in your business
01:09 What offsites are and why they matter
03:14 What Oodle offsites actually look like
06:28 How they prepare and gather leadership input
09:23 Early offsites, tactical work, and the shift to strategy
12:51 Asking, “If we started today, would we build this business the same way?”
14:00 Offsites as alignment and board-meeting time
15:00 How to tell if you’re stuck working in the business
18:35 Why true offsites need zero distractions
20:27 The “seesaw” framework and removing yourself as the bottleneck
22:15 Family, tax write-offs, and why they avoid turning offsites into vacations
26:15 The artifacts and strategic documents that come out of offsites
27:56 Most memorable and most impactful offsite stories
34:14 Planning 3 years out, even when tactics change fast
37:00 Final takeaway, when to change structure and create space to work on the business