In this episode, Jeff Mains sits down with Alistair Gordon, founder of Expertunity and author of "Master Expert," to explore why technical excellence alone isn't enough to drive career momentum and organizational impact.

Alistair reveals how subject matter experts (SMEs) can unlock influence without abandoning their technical edge through what he calls "expert ship"—a set of enterprise skills that translate expertise into clear business value. The conversation challenges the assumption that management is the only path forward for technical professionals and offers practical frameworks for founders looking to retain and grow top technical talent.

[5:00] - The leadership development gap: Only 11% of first-time leaders receive training in their first year, leaving 89% to sink or swim

[7:50] - Why "knowledge leader" failed: Technical experts don't want to be leaders—they want to avoid "useless meetings where nothing gets done"

[12:00] - The invisibility problem: Much of experts' work (like keeping email systems running) is completely invisible until something breaks

[14:30] - Expert as coach: The most transformational skill is learning to ask better questions before providing technical advice

[19:30] - The coaching paradox: Half of stakeholders love the questioning approach; the other half just want immediate answers

[23:00] - The negativity trap: Experts often spend 22 minutes explaining why something is difficult before mentioning it's actually a good idea

[29:00] - The promotion trap: Three out of four times, forcing technical experts into management roles is "a train wreck"

[40:30] - The remuneration shift: In successful tech companies, technical experts often earn more than leaders because they add more value

💡 "The era of people leaders dominating organizations is over. It's technical experts who are keeping the lights on and inventing the future." - @AlistairGordon

💡 "You can't teach a technical subject matter expert anything. They have to learn it. They have to want to learn it themselves." - @AlistairGordon

💡 "Most experts think their value should be obvious. But if your work is invisible and you can't describe it clearly, it won't be noticed." - @AlistairGordon

💡 "Career progress doesn't equal promotion. Most technical experts want to invent stuff that's cool and makes a difference—not fill in appraisal forms." - @AlistairGordon

💡 "The transition from individual contributor to first-time leader is the hardest transition in leadership—and it's five times harder for introverted technical experts." - @AlistairGordon

💡 "Find something positive to say first. Don't let technical complexities dominate the conversation before understanding what they're trying to achieve." - @AlistairGordon

Most leaders assume everyone wants career progression through management. Technical experts often want to build cool things that make a difference, not manage people. Ask what drives them before creating development paths.

Don't force technical experts into management roles they don't want. Establish technical career tracks with comparable compensation and recognition. The best chip designer at Nvidia isn't being "weighed down with management responsibilities."

Technical experts need coaching, stakeholder engagement, business acumen, and communication skills to translate their work into business value. These "enterprise skills" (not "soft skills") are what unlock their full

Ryan Debenham, CEO of Grin, shares his unconventional journey from software engineer to leading a nearly billion-dollar creator management platform. In this candid conversation, Ryan reveals how he "accidentally" became a CEO by following challenges rather than titles, and why that mindset shift transformed how he builds products and companies.

He discusses the critical disconnect between engineering and go-to-market teams, the revolutionary potential of AI agents in influencer marketing, and why democratizing influence could unlock a massive untapped market. Ryan also shares insights from his time at Qualtrics (acquired by SAP for $8B) and Route, offering practical wisdom on connecting product teams to revenue outcomes and building AI that feels "alive."

[4:30] - The Accidental CEO Path: Ryan explains how becoming a CEO was never his plan—he loved building products but never built companies around them. His career evolved by chasing challenges rather than titles or money.

[10:30] - The Product-to-Company Graveyard: Ryan candidly shares how his early product ideas (including a ride-sharing concept 20 years ago and a photo categorization tool) died because he focused only on building, not on solving the hard business problems.

[12:15] - The Mindset Shift: The biggest change from engineering to CEO? When revenue numbers became Ryan's responsibility, he finally understood what customers truly needed—not just what they said they wanted.

[14:30] - Breaking Down Silos: Ryan discusses why the tension between product, engineering, marketing, and sales "will kill the business" and how he's connecting these departments at the hip.

[19:30] - The Qualtrics Lesson: A powerful story about spending six months building the wrong text analytics product at Qualtrics, despite sitting next to customers repeatedly. The lesson: understanding business needs requires deeper connection than just listening to feature requests.

[26:00] - AI as Electricity: Ryan's compelling analogy comparing LLMs to the development of electricity and CPUs—powerful building blocks that are worthless alone but transformational when paired with the right infrastructure.

[28:30] - Mandatory AI Adoption: Ryan required all engineers at Grin to use AI coding tools. One engineer quit over the pressure but came back, realizing it was a mistake. His prediction: in a few years, you won't get hired as an engineer if you don't know AI tools.

[32:00] - Building Software That's "Alive": Ryan describes Gia, Grin's AI agent that journals daily, runs standups with other agents, creates action items, and can discuss what she's learning and what features should be built next.

[35:00] - The Influencer Marketing Problem: Why Grin's growth stalled—aspirational customers bought the software but failed at influencer marketing because the operational complexity was too high, leading to churn.

[38:30] - The Two-Sided Platform Gap: Most influencer platforms built for merchants and forgot creators. Ryan explains why supporting creators is the most important part of the solution.

[44:30] - Democratizing Influence: Ryan's vision that "everybody is an influencer"—the real opportunity is capturing and rewarding the micro-influence that happens in everyday conversations between millions of people.

[49:00] - The Collision Course: Why affiliate marketing and influencer marketing are merging into something new—it's all about capturing word-of-mouth at different scales.

In this episode, Jeff Mains sits down with Radhika Dutt, author of Radical Product Thinking, to challenge the conventional wisdom around goal-setting, KPIs, and OKRs. Radhika reveals why chasing metrics can actually distort behavior and undermine long-term growth, introducing a powerful alternative: treating growth like a puzzle rather than a scorecard.

The conversation explores how well-intentioned targets create perverse incentives, why measures should be tools for insight rather than evaluation, and how a curiosity-driven approach—using the OHLA framework (Observe, Hypothesize, Learn, Adapt)—helps teams make smarter decisions in real-world conditions. Radhika shares compelling examples from OpenAI, maritime SaaS platforms, and robotics companies to illustrate how puzzle-solving beats goal-setting for sustainable growth.

Whether you're drowning in dashboards or hitting targets while feeling like something's off, this episode offers a refreshing lens on progress, leadership, and building momentum without the performance theater.

[0:00] - Episode introduction and overview of why goal-setting may be backfiring

[4:48] - The fundamental problem with KPIs and OKRs: Goodhart's Law and Campbell's Law explained

[6:28] - Dutt's Law: "A measure is only useful as a tool for insight, not a yardstick for evaluation"

[7:16] - Real-world example: How OpenAI's user engagement targets led to dangerous "sycophantic AI"

[10:37] - The hidden dangers of hitting targets while ignoring negative indicators

[11:44] - Introduction to puzzle-setting vs. goal-setting mindset

[12:09] - The OHLA framework explained: Observe, Hypothesize, Learn, Adapt

[17:51] - Case study: Why improving filters wouldn't have solved the real problem

[28:47] - The performance theater trap: Why jumping to solutions feels comfortable but fails

[30:28] - How to get customer meetings when people say "you should already know this"

[33:00] - Why in-person observation matters more when mental models differ

[36:27] - Growth comes from matching user mental models, not forcing adoption of yours

[37:47] - The Tesla UI example: When "cool" design ignores user mental models

[37:47] - Top-down vs. bottom-up: How to introduce puzzle-solving in organizations

[39:27] - Why leaders fear losing control and how to address it

[43:01] - Vision-driven vs. iteration-led: Crafting a detailed, actionable vision statement

[45:41] - Example vision statement that tells the whole story without mentioning the product

[48:03] - Why detailed visions create ownership better than memorable slogans

[50:01] - One mindset shift founders can make this week to reduce performance theater

1. "When a measure becomes a target, it ceases to be a good measure. We've known this since 1975, yet we keep setting goals for metrics."
2. "A measure is only useful as a tool for insight, not a yardstick for evaluation. That's the critical mindset shift."
3. "When you set targets, everyone's incentive is to show you they've hit that target. You don't look at the negative numbers to see what's actually happening."
4. "Puzzles trigger curiosity and questioning. If you already know the answers, there's no puzzle. That's the...

Mark Walker, CEO of NUE, joins Jeff Mains to discuss how modern SaaS companies can transform revenue operations from fragmented systems into a unified lifecycle. With $30M in funding and customers like OpenAI, Anthropic, and Jasper, NUE is redefining quote-to-cash by treating revenue as a continuous flow rather than disconnected handoffs.

Mark shares insights on disrupting entrenched markets, building high-performance cultures, and why speed and flexibility have become the ultimate competitive advantages in an AI-driven world.

0:54 - The hidden complexity tax

4:42 - Curiosity as a career compass

8:59 - Skating to where the puck is going

11:44 - The unified truth

14:26 - The $2M discovery

18:03 - Speed as strategy

21:29 - Flexibility unlocks enterprise deals

26:45 - The Trojan horse strategy

28:09 - Productized implementation

29:56 - Lightning-fast deployments

38:21 - Market disruption wisdom

45:47 - Culture starts at the top

46:16 - NUE's three core values

Stop thinking of quoting, billing, and invoicing as separate steps. They're part of one continuous flow. When these systems are disconnected, you bleed 3-5% of ARR annually (per MGI research) and create unnecessary friction for customers and teams.

In a world where the pace of change has changed, the most critical attributes in technology partners are speed, flexibility, and time to value. Companies that can implement and iterate quickly have a massive competitive advantage over those locked into rigid, year-long implementations.

When attacking entrenched markets, find a wedge product that serves as your entry point. But that wedge must be exceptional on its own merits. NUE's CPQ is so good that customers buy it standalone, then discover the billing platform inside.

NUE targeted the hardest problems first—multi-attribute pricing, complex enterprise scenarios—because they wanted to help companies grow. If you're good at where customers are headed, small companies can use your platform to compete with giants.

Values on the wall mean nothing if leadership...

In this episode of SaaS Fuel, Jeff Mains sits down with Daniel Nikic, a global strategist and problem solver who advises multinational corporations and funds on AI, software, and data investments. The conversation explores the critical balance between artificial intelligence and human judgment in today's business landscape.

Daniel brings a refreshing counterbalance to the AI hype cycle, emphasizing that while AI excels at eliminating "bot work" and processing data, it cannot replace human expertise, experience, and contextual understanding. The discussion covers the dangers of taking AI outputs at face value, how investors should evaluate AI-powered insights, and where AI truly creates value versus where it falls short.

The episode also explores global market opportunities in the Middle East, Eastern Europe, and Latin America, the realities of entrepreneurship beyond the social media glamour, and practical advice for SaaS founders navigating AI adoption, fundraising, and outsourcing decisions. Daniel's decades of international experience provide unique insights into emerging tech evaluation, investment trends, and the future of SaaS metrics.

[4:39] - The Bot Work Revolution

[5:40] - The AI Audit Imperative

[6:04] - The Competitive Convergence Problem

[7:30] - Bot Work vs. Insight Work

[9:09] - The Implementation Test

[10:13] - High-Impact AI Use Cases

[12:51] - The Training Challenge

[14:45] - The Human Connection Factor

[17:46] - Political Bias in AI

[18:40] - Education Under Threat

[22:12] - Middle East Market Opportunity

[23:56] - Latin America's Undervalued Talent

[24:29] - Data Centers as the New Oil

[25:52] - Eastern Europe's Tech Advantage

[30:14] - The Outsourcing Value Question

[32:57] - Entrepreneurship's Hidden Stress

[34:16] - The Rejection Resilience

[35:36] - The Hard Work Reality

[36:08] - Stress Management Separates Winners

[37:32] - EQ Over IQ

[38:16] - User Experience Trumps AI Hype

[39:07] - Due Diligence Fundamentals

[40:15] - The Founder Factor

[41:01] - Overnight Success Myth

[44:52] - The Investment Reality Check

[45:13] - Fundraising in the AI Era

In this episode of SaaS Fuel, host Jeff Mains sits down with Alex Sanfilippo, founder of PodMatch—a platform revolutionizing how podcast guests and hosts connect. Unlike traditional SaaS companies chasing celebrity users and vanity metrics, Alex built PodMatch with a radically different philosophy: serve the community first, prioritize human connection over growth at all costs, and give back to creators rather than extracting maximum value.

Alex shares how PodMatch became a category leader without outside funding by focusing on one North Star metric, maintaining an all-paid model with no free trial, and launching the PodValue Initiative—giving over $1 million back to podcasters. This conversation challenges conventional SaaS wisdom and offers a refreshing perspective on building sustainable, values-driven software businesses that actually serve their users.

[4:35] - The Two-Week Validation Moment

[6:41] - The Community-First Philosophy

[7:22] - Serving the 99%, Not the 1%

[10:12] - The Bold All-Paid Model

[13:46] - Community as the Last Moat

[19:11] - The PodValue Initiative

[21:22] - Why Podcasters Quit

[24:46] - The One-Year Rule

[38:18] - The Profit-First Struggle

[40:12] - The Single North Star Metric

[44:58] - Messaging Matters Most

Most SaaS companies chase the biggest names and enterprise clients. Alex flipped this model by focusing on independent creators—the 99% that everyone else ignores. This created fierce loyalty, organic growth, and a defensible community moat. Lesson: Your competitive advantage might be in serving the market segment everyone else overlooks.

By eliminating free trials and free tiers, PodMatch ensured only serious, committed users joined the platform. This counterintuitive move actually increased signups while dramatically improving platform quality and reducing churn. Lesson: Sometimes adding friction (payment) filters for better customers and creates a...

In this episode, Jeff Mains sits down with Luv Kapur, a technology leader at Bit who's reshaping how enterprises build software. Luv shares his journey from leading platform engineering at one of Canada's largest pension funds to joining a startup on a mission to help organizations scale development through composability and AI-powered tools.

The conversation explores how AI is fundamentally changing software development—not by writing more code, but by enabling teams to compose better solutions with less custom code. Luv challenges the hype around code generation, arguing that the real bottleneck isn't writing code but translating business requirements into sound architecture and reusing battle-tested components.

Luv also offers a grounded perspective on AI's impact on jobs, the importance of discoverability in component libraries, and practical advice for CTOs building composable organizations.

[0:00] - Episode introduction: AI-powered, cloud-native enterprise development tools

[1:00] - The hidden cost of poor discoverability in internal libraries and how it silently slows high-performing teams

[4:26] - Luv's background: From leading platform engineering at Healthcare of Ontario Pension Plan to joining Bit

[4:47] - The spark for the leap: Believing in the mission of helping enterprises scale development globally

[5:19] - The consistency problem: When products span multiple teams but feel disjointed to users

[6:37] - Building a platform team whose customers are developers themselves

[7:23] - Discoverability as the key problem: Developers couldn't find what already existed

[9:24] - Why inner source software transforms development artifacts into invaluable organizational assets

[11:37] - Viewing your org chart as a dependency graph, not a hierarchy

[15:51] - The AI hype is justified, but code generation isn't the real bottleneck

[17:01] - The bottleneck is translating business requirements into software architecture, not writing code

[18:41] - AI should help us do less work, not more work

[19:27] - Why developers won't lose jobs: There's infinite work, not finite work

[20:19] - Reusing battle-tested components increases quality and reduces surface area for errors

[21:59] - Reducing AI context to dependency graphs and APIs prevents hallucinations

[23:05] - Private enterprise data is the gold mine for AI value

[24:35] - The rise of citizen developers: Non-technical people building with natural language

[26:40] - Empowering citizen developers with internal component marketplaces

[27:19] - How AI changes the build vs. buy equation through faster prototyping

[30:09] - Internal tools will be hit hardest by AI disruption

[34:41] - SaaS companies must align with core business value to stay sticky

[36:19] - The biggest mistake: Equating vibe-engineered solutions with production-ready software

[39:01] - Building AI muscle: Start with clear scoped goals, not vague initiatives

[40:45] - The future: Higher skill ceiling, elimination of junior developer roles, but more opportunities overall

[43:45] - Junior developers must contribute to open source and build visible impact

[44:31] - The one capability every software leader needs: Willingness to adopt AI and keep learning

In this episode, Chris Carter reflects on a pivotal leadership mistake that deeply impacted his team—making a team member cry during a meeting.

Chris candidly shares the emotional aftermath, the lessons learned from his mentor and spouse, and how this experience reshaped his approach to leadership. The discussion offers valuable insights for SaaS leaders on empathy, accountability, and team management.

[0:00] Chris Carter opens up about a critical leadership error: making a team member cry in a meeting.

[0:11] He discusses the emotional toll and the importance of seeking advice from trusted mentors and loved ones.

[0:20] Chris emphasizes the need to treat every team member equally and avoid leading through fear or threats.

[0:55] He highlights the importance of understanding the root cause of performance issues—whether personal or professional—and considering alternative solutions.

[1:10] Jeff Mains asks how Chris made amends and the broader impact on the team.

"I made the mistake one time of making a team member cry. Literally, I made him cry in one of our meetings and I felt horrible afterwards."

"As a leader, you can't lead by fear. You have to work with your team, not threaten them."

"If someone is struggling, try to help them first. If it doesn't work out, replace them quickly but compassionately."

"You never know what's going on in someone's life outside of work. Empathy matters."

1. Lead with Empathy: Understand that your team members are people first, employees second.
2. Seek Guidance: Don’t hesitate to consult mentors or loved ones when facing tough leadership moments.
3. Avoid Fear-Based Leadership: Inspire and support your team rather than intimidating them.
4. Address Issues Directly: If a team member is underperforming, address it quickly and fairly.
5. Consider the Whole Person: Recognize that personal issues can affect work performance—be flexible and supportive.
6. Learn and Grow: Mistakes are inevitable; what matters is how you respond and grow as a leader.

cc@approyo.com

http://www.Approyo.com

https://www.linkedin.com/in/christopher-carter-885159/

X.com/Approyo

The Captain's Keys

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