Podcast Summary: Startup Stories – Mixergy

Episode #2284: Why is Morning Brew’s Founder Selling “AI Transformation”?
Host: Andrew Warner
Guests: Alex Lieberman, Arman Hazarkhani (Co-founders of 10x), Jesse (Investor/Entrepreneur)
Date: November 5, 2025

Episode Overview

This episode explores the rise of “AI Transformation” as a new service offered by agencies and consultancies, led by Morning Brew’s co-founder Alex Lieberman and Arman Hazarkhani of 10x. The conversation dives deep into how companies can actually leverage AI for productivity, what “AI transformation” really means beyond the buzz, and the practical realities—business models, product demos, and philosophical implications—of integrating AI into business operations. The discussion is candid and sometimes skeptical, challenging the sustainability and growth prospects of AI-focused service businesses.

Key Discussion Points & Insights

1. What is “AI Transformation,” Really?

[00:13]

• Definition: Moving companies “across the chasm from pre-AI to post-AI”—helping them not just use tools like ChatGPT, but implement AI at multiple levels of their operations.
• Challenge: While individuals can adopt AI quickly, transforming an entire organization is “multiplayer AI”—it requires change management, tool-building, new processes, and training.
  • Arman: “It's literally one step [for a person]. But to do that on a business... that's really, really tough. That takes studying, that takes change management, that takes tool building... people, process and technology.” [01:27]

2. Business Model & Growth Strategy of 10x

[02:09], [05:18]

• Two Focus Areas:
  • “Engineering as a service” (AI-powered dev shop) as the main revenue driver.
  • AI Transformation as the fastest-growing segment.
• Unique Approach: Not charging hourly—clients and engineers are both paid based on output, not time.
  • Alex: “Service businesses are going to move away from hourly work. And we think it's starting with engineering.” [05:18]
• Incentives: Highest-performing engineers can make $1M/year—competing with big tech.
  • Alex: “Our best engineer will make a million bucks in cash next year.” [05:55]
• The “engineering” side provides recurring revenue and serves as a “Trojan horse” for AI transformation initiatives.

3. Application Layer Opportunity & The Big Vision

[06:48], [07:30]

• There’s a market opening to build value at the application layer (apps/tools on top of AI) rather than infrastructure.
• Arman: “For zero cost, you can compete with these hyperscalers. All you need is brilliant people in a room.”
• Alex: “What would it look like if McKinsey or BCG was created today? ... We believe we can be the household name to do [AI transformation] either through services or through product.” [09:06]

4. How AI Changes Software Development

[09:48], [11:21]

• Process: Traditional dev shops are “labor arbitrage”; 10x does “intelligence arbitrage,” using AI agents as “junior developers” managed by human engineers.
  • Arman: “No one on our team actually types letters into an IDE... We're pushing modern AI tools to the limit, scoping work down to the most atomic level.” [09:48-11:21]
• AI Engineer Role: Thinks like an architect, breaking down work to microtasks for multiple AI agents.
  • Arman: “They're really acting as managers of these different agents.” [12:30]
• Output: Faster, cheaper, higher-margin product development.

5. Is AI Transformation Sizzle or Substance?

[13:28], [19:52]

• Skepticism: Engineering contracts are sticky, but AI transformation projects risk being one-off “sizzle” rather than recurring “meat.”
• Lead Generation: Positioning as AI transformation attracts new engineering clients via free AI diagnostics.
  • Alex: “Half of our clients have come through these free AI diagnostics we do... half of those turn into engineering clients.” [20:13]

6. Practical Example – Billboard Company AI Audit

[20:40]-[25:08]

• Process:
  1. Company requests help “using AI.”
  2. 10x starts with org-wide surveys, stakeholder interviews, and usage data analysis.
  3. Pinpoints friction points (e.g., dropoff during creative upload and moderation), then proposes targeted AI tools to improve efficiency and product quality.
  • Arman: “We made these recommendations... For each one, there’s a lift vs reward: how much is this going to cost and how much are you going to get out of it?” [24:11]
• Sustainability Question: AI transformation isn’t easily an ongoing service. Compare to McKinsey: “Does a biz ops person ever run out of work?” [25:25]

7. The Evolution of AI Services

[26:16]-[32:13]

• Blending Boundaries: Tech consulting (engineering) and AI transformation will likely blend; tech providers will continually move up the value chain as AI “problems” keep evolving.
  • Jesse: “Let’s stop using the word AI… The real word is I can build technology for you cheaper and faster than I used to be able to.” [27:18]
• Ongoing “transformation work” will persist as AI and business needs constantly shift.

8. Tools & Demos

[33:17]-[48:29]

• Custom GPT "EOS Facilitator":
  • Alex walks through using a custom GPT trained on Gino Wickman’s Traction for business process coaching—direct uploads of frameworks and prompts, automating management tasks like accountability charts.
    • Alex: “The way I think about custom GPTs is… it’s almost like a persistent ChatGPT instance… If it's a tool that I'm going to want to share with other people… then I think about building a custom GPT.” [33:17]
• Claude CLI Demo:
  • Arman shows off using Anthropic’s Claude and Playwright to automate market research and LinkedIn outreach via command line, running multiple web tasks in parallel using AI agents.
    • Arman: “If you have a free or near free junior employee and you can’t find a way to generate leverage from that, that’s a skill issue.” [47:47]
• Notable Tech Insight:
  • Most exciting progress now is at the application layer (interfaces, fine-tuning, “arms and legs for AI”) rather than core base models.

9. Human vs. AI: Where’s the Boundary?

[51:46]-[55:23]

• Human-in-the-loop is here to stay—for now, “AI is like a high-agency junior employee… you build up trust, and as it proves itself, you let it do more.”
  • Alex: "I view AI today as this incredible junior employee... As intelligence gets better and better, you end up getting ‘employees’ that look more and more like senior employees… The job of an executive really becomes orchestration." [53:47]
• Some jobs might be fully automated in 5-10 years, but the transition is opportunity-rich.

10. Scaling the Services Business

[55:23]-[61:20]

• Margin Management: 10x claims “margin down to the day”—paying and charging by output, heavy focus on repeatable “Lego blocks” of IP to build higher margins.

  • Arman: “We have the margin down to the day... We charge on output and we pay on output and everyone's aligned.” [56:47]

• Risks: Scope creep, unreliable output-based pricing, and need to “never take on a fight you can’t win.”

  • Jesse: “There are a lot of frameworks... it’s important for you guys to build, to crush this, to start to think about some of these questions more directly.” [55:31]

• Big Picture Goal:

  • Use a content/media play (Morning Brew-style) on top of 10x’s services for lead generation and differentiation.
    • Alex: “The goal is to build kind of 10x Media on top of 10x, where there's only one place that executives go to to make AI more actionable.” [60:37]

Notable Quotes & Memorable Moments

• Arman on AI's Power:
  • “No one on our team actually types letters into an IDE... If I walk through a WeWork and I see someone typing letters in an IDE, they're working in like 1990.” [09:48]
• Jesse on Business Models:
  • “Let’s stop using the word AI because the word AI is stupid. The real word is I can build technology for you cheaper and faster than I used to be able to.” [27:18]
• Alex on Consulting:
  • “Consulting firms really just latch onto being a growth and strategic partner... The CEO, when he has cold sweats at night, is not texting his best friend—he's texting his partner at Bain, BCG, or McKinsey.” [28:57]
• On Automation Opportunity:
  • “If you have a free or near free junior employee and you can't… generate leverage from that, that’s a skill issue.” (Arman) [47:47]

Timestamps for Key Segments

| Segment | Timestamp | |-----------------------------------------------------------------------|--------------| | What is AI transformation? | 00:13 | | Business model & output pricing; highest paid engineers | 02:09, 05:55 | | The big vision: app layer, team of geniuses, McKinsey for AI | 06:45-09:19 | | Case study: Snap Exports, atomic scoping/design using AI | 09:48-12:30 | | AI transformation as “sizzle” for lead generation | 13:28-20:13 | | Detailed transformation audit: Billboard company | 20:40-25:08 | | Sustainability, repeat project-based AI consulting vs. recurring work | 25:08-32:13 | | EOS Facilitator custom GPT demo | 33:17-38:53 | | Claude CLI practical coding/automation demo | 38:58-48:29 | | The future pace of AI agents | 46:31-51:46 | | Human-in-the-loop vs. full automation | 51:46-55:23 | | Margin and scaling frameworks for services business | 55:23-61:20 |

Takeaways for Founders & Operators

• AI transformation goes beyond adopting new tools. Success requires rethinking processes, teams, and incentive structures—not just plugging in ChatGPT.
• Output-based pricing & AI engineering is the future for high-performing dev shops—but beware of margin compression from poorly scoped work or scope creep.
• Transformational projects are best used as wedge sales into longer-term, recurring engineering or consulting work.
• Building internal IP (“Lego blocks” of AI automation) drives future margins and repeatability.
• The real winners will control both trust/distribution (via content, brand, executive relationships) and technical excellence.
• AI will increasingly automate not just low-level tasks but architecture and orchestration—plan for a rapidly shifting competitive landscape.

Tone:
Candid, ambitious, sometimes skeptical, but always focused on actionable business realities—not just hype.

For full technical details, step-by-step demos, and further thoughts on the future of work and automation, listen at the noted sections above.