Not Brothers

Mark Hughes, Ryan Hughes
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No Nonsense Business and Tech Talk. Just two business partners who’ve survived nearly two decades of client deadlines, all-nighters, stealing each other’s fries, and somehow still speaking at family events. In 2009 they co-founded Oodle – a digital marketing agency that started with two laptops, zero clients, and an unhealthy amount of confidence. Sixteen years later it’s one of the sharpest independent shops in the country. Along the way they’ve launched other companies, products, and ideas together. Every week they pull a couple of chairs up to a mic and rip open the exact stuff most podcasts polish to death: Which new AI and technology tools are actually shipping vs. which ones are just vaporwareThe creative calls that made fortunes and the ones that almost ended themThe unsexy business decisions that separate “cool startup” from “company that pays its bills”Real-time, zero-filter debates, because when you’ve argued over cap tables with your actual family, you stop pretending to agree Not Brothers. Just two co-founders who’ve been mistaken for siblings so often they made it the title.

  1. 4d ago

    Episode 13 - Herald: Changelogs People Actually Read

    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 00:00 — Why Oodle built Herald 00:44 — What Herald is and the changelog problem it solves 03:02 — Release notes for users, engineers, and bigger feature launches 04:56 — Using AI to turn GitHub activity into draft changelogs 06:21 — Moving from creator to editor of release notes 07:22 — Two-way GitHub sync and avoiding duplicate work 09:31 — Custom categories and tuning the AI import prompt 10:25 — Public/private repos and nested projects 11:31 — Multi-repo product families and parent changelogs 13:08 — Scheduled releases 14:22 — Getting started without a blank canvas 15:30 — Drafting a release from everything since the last GitHub release 16:43 — Customizable in-app changelog widgets 17:36 — Making product updates discoverable 19:28 — In-app updates vs. noisy notifications 19:59 — Groups, JWT, and segmented changelog visibility 21:44 — Internal users, client users, and beta release use cases 22:10 — A simple tool that adds value in the right capacity 23:07 — The three user types Herald is built for 23:48 — Real release notes, testing, and future feedback 24:35 — Website demo and interactive examples 24:59 — Try Herald and let us know what you think

    25 min

  2. May 27

    Episode 12 - AI Economics Hangover

    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 00:00 — The AI economics hangover 01:24 — Executives, overpromising, and shareholder-value promises 02:40 — Why AI hype is easy to sell upstairs 04:30 — Token drunkenness and the cost reality check 05:54 — Fiscal pressure, Microsoft, Claude, and Copilot 07:26 — Finding the limits of agentic AI tools 09:44 — Goalposts, model progress, and AI fatigue 11:55 — The iPhone analogy for frontier-model improvement 14:18 — AGI goalpost shifting and useful-but-not-magical agents 16:49 — Model economics and better autonomous coding loops 18:26 — Dopamine machines, fake confidence, and verification 20:48 — Reddit, authenticity, and trust in AI training data 21:56 — Sam Altman, job disruption, and the softer economic view 23:29 — Is AI a bubble or an early overbuild? 24:38 — Data centers, memory prices, and supply-chain ripples 26:48 — Infrastructure bets and consumer/app-layer demand 29:03 — Google’s distribution advantage in AI 30:02 — Gemini, coding models, and different model strengths 31:04 — Google Workspace as the AI surface area 32:34 — AI search, generated answers, and SEO disruption 33:20 — Actual content people want may finally matter 35:36 — The echo chamber vs. mainstream adoption 36:33 — Untapped users and the application layer 37:39 — AI inside existing tools, not only standalone chatbots 38:02 — Better chatbots would still be a win 38:32 — Wrap-up 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

    39 min

  3. May 18

    Episode 11 - If You Build It With AI...Will They Come?

    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 00:23 — Why AI changes the product-development conversation 01:08 — Has the Silicon Valley pecking order flipped? 02:27 — Distribution was always the hard part 04:20 — When product moats get easier to copy 05:04 — Salesforce, Oracle, and the power of incumbency 06:41 — Niche products in the AI era 07:16 — Why small markets used to be hard to serve 09:38 — The new case for niche software businesses 10:23 — Using AI-friendly tools for distribution 11:05 — Ryan's problem with MCP servers 12:42 — Distribution paths beyond MCP 12:54 — Programmatic SEO and the slop problem 14:02 — LinkedIn, AI content, and the loudest voice in the room 15:58 — Answer-engine optimization vs content spam 17:10 — Good old-fashioned inbound marketing, now AI-readable 19:10 — Free tools as top-of-funnel distribution 20:17 — Why interactive tools build brand equity 21:10 — Making product outputs shareable 22:28 — Buying niche newsletters and owned audiences 23:27 — Ryan draws the line on spam 25:27 — AI agents, cold outreach, and inbox overload 27:27 — When sender bots meet screener bots 28:17 — Why AI does not belong in every communication layer 29:56 — AI content repurposing engines 32:07 — Using AI to extract the useful five minutes 33:44 — Social volume, quality, and the For You page 35:27 — The final answer: building is easier, distribution still wins

    37 min

  4. May 13

    Episode 10 - Does AI Make Us Dumber?

    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

    48 min

  5. Apr 29

    Episode 9 - AI in Critical Thinking and the Weirdness it can Create

    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 useOveruse patterns in AI AI in academiaImpact of AI on learningEthical considerations in AI use Chapters 00:00 Ethics and AI in Academia08:55 Critical Thinking vs. Plagiarism17:53 AI as a Tool in Education24:29 Academic Projects and Senior Theses32:10 AI in Business and Work Environments38:10 AI and Writing Skills Development

    45 min

  6. Apr 15

    Episode 8 - Are You Working ON or IN Your Business?

    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

    38 min

  7. Mar 30

    Episode 7 - Dead Internet Theory

    The conversation explores the concept of Dead Internet Theory and the impact of agentic workflows on social networks. It delves into the future of human interaction on the internet and the implications of AI-first vs human-first product design. The conversation explores the integration of AI and human interaction, emphasizing the importance of AI in making human lives easier. It delves into the impact of AI on content management, decision-making, and human roles, highlighting the democratization of content creation and the concept of Jevons Paradox in AI. Takeaways Dead Internet TheoryAI vs Human-Centric Product Design AI and human interaction are both importantAI should be used to make human lives easier Chapters 00:00 Dead Internet Theory and Agentic Workflows08:53 The Future of Human Interaction on the Internet21:12 AI-First vs Human-First Product Design27:08 API vs. Interface32:24 Agentic CMS Workflow38:19 Empowering Humans with AI43:20 Interactive Prototypes

    50 min

  8. Mar 19

    Episode 6 - Innovation is Hard

    Why innovation is difficult for small and medium businesses — and how AI is changing the game Key Themes1. Innovation Requires Accepting FailureInnovation is like "setting money on fire" — but necessary for long-term winsMost experiments fail; the learning is the value, not the outputR&D tax credits exist specifically because the government wants businesses to invest in uncertain outcomesAnalogy: Innovation is like working out — everyone wants the results, nobody wants the 5-year grind2. The Real Work Isn't Writing Code — It's Solving ProblemsWriting code is fast; architecture and problem-solving are the hard partsLosing a day's work and recreating it in 30 minutes proves: the code isn't the value, the thinking isAI can write code extremely quickly, but still struggles with novel architecture and business-specific problems3. AI Has Fundamentally Changed Innovation Speed (2026)What took weeks to build now takes daysThe barrier to entry for innovation has never been lowerSmall/mid-sized businesses are the biggest winners — they can now do what only enterprises could afford beforeExample: Building interactive, regional data visualizations that would have been "cost-prohibitive" before4. Enabling Teams, Not Replacing ThemThe goal isn't to replace workers with AI — it's to eliminate the work nobody wants to doNon-technical team members can now build React artifacts and interactive toolsThe focus shifts from "writing code" to architecture, ideas, and oversightPeople still need to learn through failure (like touching the hot stove)5. Bespoke Software is Now AccessiblePreviously, custom software required $2-3M+ investment for dev teamsNow, small teams with AI tooling can build tailored solutionsExample: Instead of begging enterprise vendors for features, just build what you needModern frameworks (Rails, etc.) allow deployment in minutes6. AI Security & Control ChallengesAI agents will try to work around restrictions (digging tokens out of logs, attempting DNS changes)Balancing innovation with security is an ongoing tensionLocal/on-premise models offer a path for sensitive data processingThe future: purpose-built, domain-specific models that don't need general knowledge7. The Future of AI InnovationFrontier models are being compressed to run on consumer hardware (RTX 6000, etc.)Next evolution: slicing off specialized capabilities for specific use casesSmall, tuned models for narrow tasks (OCR, customer service, etc.) instead of massive general-purpose models Takeaways for ListenersBudget for failure — Innovation requires experiments that won't workAI lowers the barrier — What cost millions now costs a fractionEmpower your team — Give them AI tools and let them experimentFocus on architecture — Let AI handle code output; humans own the thinkingStay curious — The landscape changes weekly; ride the wave or get left behindEpisode Length: ~47 minutes Tone: Conversational, technical but accessible, optimistic about AI's potential with realistic caveats about challenges

    48 min
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About

No Nonsense Business and Tech Talk. Just two business partners who’ve survived nearly two decades of client deadlines, all-nighters, stealing each other’s fries, and somehow still speaking at family events. In 2009 they co-founded Oodle – a digital marketing agency that started with two laptops, zero clients, and an unhealthy amount of confidence. Sixteen years later it’s one of the sharpest independent shops in the country. Along the way they’ve launched other companies, products, and ideas together. Every week they pull a couple of chairs up to a mic and rip open the exact stuff most podcasts polish to death: Which new AI and technology tools are actually shipping vs. which ones are just vaporwareThe creative calls that made fortunes and the ones that almost ended themThe unsexy business decisions that separate “cool startup” from “company that pays its bills”Real-time, zero-filter debates, because when you’ve argued over cap tables with your actual family, you stop pretending to agree Not Brothers. Just two co-founders who’ve been mistaken for siblings so often they made it the title.

Information

  • Creator
    Mark Hughes, Ryan Hughes
  • Years Active
    2K
  • Episodes
    13
  • Rating
    Explicit
  • Copyright
    © 2025 Mark Hughes, Ryan Hughes