The Sermon Library Problem: What Chegg’s Collapse Taught Me About AI and Product-Market Fit

I run a product that helps pastors prepare sermons. We have 245,000+ sermons in our library, decades of content, and a subscription model that’s worked for years. Pastors come to SermonCentral when they’re stuck, when they need inspiration, when Sunday is three days away and the blank page is winning.

And for the first time in my career, I’m looking at that model and asking: Is our product-market fit about to evaporate?

The Treadmill You Don’t See Moving

Reforge published a piece that hit me like a gut punch. The core argument: product-market fit is a treadmill, not a destination. The bar for what customers consider “good enough” is always rising, and AI just cranked the speed to a sprint.

Here’s the part that stuck with me: unlike previous tech shifts that unfolded over years, AI causes the PMF threshold to spike exponentially, giving incumbent solutions no time to adapt before losing relevance.

Mobile took a decade to reshape industries. Cloud computing gave companies 5–7 years to migrate. AI? The examples are already piling up.

Chegg lost 87.5% of its valuation. Stack Overflow saw traffic crater. These were category leaders with massive content libraries and loyal user bases.

Sound familiar?

The Chegg Parallel Is Uncomfortably Close

Let me lay this out plainly.

Chegg’s model: a massive library of human-created study content, monetized through subscriptions, behind a paywall. Students paid because they couldn’t get the answers anywhere else. Then ChatGPT showed up. Suddenly, students could get comparable answers, for free, instantly, with no subscription required.

Now replace “students” with “pastors” and “study content” with “sermon outlines.” Replace “ChatGPT” with SermonAI, Sermon Snap, or honestly just ChatGPT itself with a decent prompt.

The structural vulnerability is identical: a content library behind a paywall, AI that generates comparable output for free, and a “good enough” bar that resets overnight.

I talk to pastors every week. More of them are experimenting with AI tools for sermon prep. Most are cautious about it — but it solves their immediate problem: “I’m stuck and Sunday is coming.”

The Real Strategic Question

Here’s where most product leaders get it wrong. The knee-jerk reaction is: “We’ll just bolt AI onto our existing product.” Slap a chatbot on the homepage. Add an AI summary feature. Ship it fast.

That misses the deeper question: is your product the platform people use AI through, or the platform that AI makes redundant?

If you’re a content library and you add AI search, you’re still a content library. You’ve made the existing model slightly better, but the customer’s mental model hasn’t changed. They’re still coming to you for content, and AI is still generating that content for free elsewhere.

The real pivot is harder. It means rethinking what your product actually is.

For SermonCentral, the strategic move is “AI-powered sermon prep workspace where our library is an input, not the product.” The library becomes training data, context, theological grounding — the thing that makes our AI better than generic ChatGPT. The product becomes the workflow.

Three Questions Every SaaS Leader Should Be Asking Right Now

If you run a content-library or knowledge-base product — and honestly, this applies to most subscription SaaS — here’s the framework I’m using:

1. Can AI generate a “good enough” version of your core deliverable? Be brutally honest. Can AI give my customer something that clears their bar? For many use cases, 70% quality delivered instantly beats 95% quality behind a paywall. If the answer is yes, your paywall is losing its teeth.

2. What does your product offer that AI alone cannot? This is where you find your moat — or discover you don’t have one. For SermonCentral, it’s community validation (knowing 10,000 other pastors used this sermon), exegetical depth that’s been peer-reviewed, denominational fit, and sermon series planning that accounts for the liturgical calendar. These are things a generic AI doesn’t know to consider. Your version of this list is your survival strategy.

3. Are your current users already using AI tools alongside your product? If you don’t know, find out this week. A simple exit survey question, an onboarding poll, a one-question email. If even 15–20% of your users are experimenting with AI alternatives, the adaptation window is already closing.

The Window Is Smaller Than You Think

With previous technology shifts — mobile, cloud, social — companies had years to adapt. You could see the wave coming, form a committee, hire a consultant, run a pilot, iterate for a few quarters, and still catch up.

AI doesn’t work like that. The window slams shut before you recognize the threat severity. Chegg didn’t see a slow decline and choose not to respond. They saw a cliff, and by the time they recognized it, they were already falling.

The work we’re doing right now at SermonCentral — instrumenting whether our users are using AI sermon tools, prototyping AI-augmented workflows that use our library as an input rather than competing with free generation, rethinking activation so that a new user’s first 48 hours deliver something AI alone cannot — is existential work.

What’s Actually Scarce

The companies who survive this shift will be the ones who rebuilt their products around the assumption that AI-generated content is free and abundant — and then found the thing that’s scarce.

For church tech, what’s scarce is trust. Theological accuracy. Community wisdom. The peace of mind that comes from knowing your sermon was shaped by a tradition, not just generated by a machine.

Those things have real value. But only if we build products that deliver them in ways a pastor can feel on a Tuesday night when Sunday is looming.

The treadmill is speeding up. Time to change direction before it throws you off.


Your Turn: Apply This Today

Use these questions to pressure-test whether your product is the next Chegg — or the one that replaces it:

  • Name the task your product completes. Write it in one sentence from the user’s perspective: “When I need to ______, I use ______.” If AI can now complete that task in 30 seconds, your moat is eroding. Be honest.
  • Run the “AI substitute” test. Have someone on your team try to accomplish your product’s core job-to-be-done using only ChatGPT or Claude. Document exactly where the AI falls short. That gap is your defensible surface.
  • Map your unique data assets. What does your product know that a general-purpose AI cannot access? User history, community content, proprietary datasets? If the answer is “nothing,” that’s your most urgent product risk.
  • Identify your highest-engagement users and interview three of them. Ask them directly: “Have you tried using AI tools for what you use us for? What happened?” Their answer will tell you more than any dashboard.
  • Rewrite your product’s value proposition for the AI era. Your old version assumed AI wasn’t available. Rewrite it assuming users have access to powerful AI. What do you still uniquely offer? That’s your actual pitch.
  • Set a 90-day “substitution risk review.” Put a recurring calendar item to evaluate how much of your product’s core workflow can now be replicated by AI. Treat it as a competitive threat review, not a tech curiosity.

Is your SaaS product facing a similar AI-driven PMF threat? I help product leaders think through competitive positioning and strategic pivots in the age of AI. Let’s talk.

Philippians 2 and Agentic Systems: Why Humility Is the Foundation of Intelligent Systems

“Do nothing from selfish ambition or conceit, but in humility count others more significant than yourselves. Let each of you look not only to his own interests, but also to the interests of others. Have this mind among yourselves, which is yours in Christ Jesus, who, though he was in the form of God, did not count equality with God a thing to be grasped, but emptied himself, by taking the form of a servant, being born in the likeness of men.” (Philippians 2:3-7, ESV)

Paul’s letter to the Philippians contains what theologians call the kenosis passage — the self-emptying of Christ. It’s about voluntary limitation, choosing constraint over capability, service over sovereignty.

I’ve been thinking about this as I watch agentic systems become more capable. The rhetoric around AI often centers on unlimited potential, boundless capability, systems that can do anything. But in my experience building AI systems, including multi-agent workflows for executive tasks, I’ve observed that success often comes through deliberate constraints rather than unlimited scope.

Well-designed AI agents typically focus on narrow mandates: a calendar agent that protects focused time blocks rather than trying to optimize entire lifestyles, or an email agent that surfaces priority messages rather than attempting to replace human judgment entirely. Each agent serves a specific function within defined bounds.

This represents a design choice rather than a technical limitation.

The Kenosis of Intelligent Systems

When building AI workflows, the temptation exists to create agents that can handle everything. But, what I’ve seen is that this approach typically produces chaotic results — agents interfering with each other, making decisions outside their expertise, creating more complexity than clarity.

A more effective approach involves thinking about AI agents as specialized robots rather than general-purpose minds. Each agent can be designed to “empty itself” of capabilities it doesn’t need, serving a specific function more effectively through limitation.

Specialized agents with narrow scopes — research agents that don’t schedule meetings, scheduling agents that don’t write summaries, writing agents that don’t manage tasks — can demonstrate greater utility through deliberate constraints.

This mirrors patterns in effective human teams, which typically consist of specialists who understand their roles rather than generalists attempting everything. They practice a form of professional kenosis — voluntary limitation for collective effectiveness.

Paul’s instruction to “count others more significant than yourselves” suggests a design principle: building systems where each component serves the whole rather than maximizing individual capabilities.

The Servant Leadership Model for AI

The parallels between servant leadership principles and effective AI system design are notable. Servant leaders focus on enabling others’ success rather than demonstrating their own power, asking “How can I help you accomplish your goals?” rather than “How can I show you what I can do?”

Effective AI systems often follow similar patterns. GitHub Copilot suggests contextual code completions rather than attempting to write entire applications. AI writing assistants help clarify thinking rather than replacing human thought processes. Advanced language models acknowledge uncertainty and ask clarifying questions rather than claiming omniscience.

These systems practice technological humility by acknowledging their limitations.

In contrast, AI systems that fail in production environments often attempt to exceed their appropriate scope, make decisions beyond their training data, or present uncertain inferences as established facts. They lack the kenotic restraint that characterizes truly useful intelligence.

Building Products for Global Spiritual Formation

This principle becomes particularly important when developing products for spiritual formation. Digital discipleship platforms serve diverse global communities across cultural, linguistic, and theological boundaries. The temptation exists to build universal systems that can serve everyone.

However, effective spiritual formation tends to be deeply personal and contextual. A Bible application serving a house church in rural Kenya requires different features than one serving a suburban megachurch. Prayer applications for new believers need different structures than those designed for theological students.

AI systems serving spiritual formation appear most effective when they practice kenosis — limiting their scope to serve specific communities well rather than attempting to serve everyone adequately.

Current development work on AI tools for sermon preparation follows this model. Rather than attempting to write complete sermons (which, based on informal conversations with pastoral leaders, many pastors prefer to avoid), such tools can focus on specific supportive tasks: locating relevant cross-references, summarizing historical context, or structuring outlines. They operate within deliberate constraints to support pastoral ministry rather than replace it.

Each tool “empties itself” of broader capabilities to serve one function excellently. Like Paul’s description of Christ, they don’t grasp for equality with human pastors — they take the form of servants.

The Paradox of Powerful Restraint

An interesting observation: seemingly powerful AI systems often prove most effective when operating under significant constraints. The wisdom of limiting scope applies to artificial intelligence as much as human teams.

In my experience, the most effective AI implementations have narrow, well-defined purposes. They operate within their designated areas, defer to human judgment on edge cases, and acknowledge when they lack sufficient context for recommendations.

This represents strength through limitation rather than weakness.

Paul writes that Christ “did not count equality with God a thing to be grasped.” He could have insisted on unlimited power but chose constraint for the sake of service. The kenosis wasn’t a loss of divinity — it was divinity expressed through voluntary limitation.

Similarly, the most intelligent AI systems may not be those with the most capabilities, but those that use their capabilities most wisely — which often means choosing restraint over action.

Technical Humility in Agentic Systems

What might this look like in actual system design? Consider what could be called “kenotic interfaces” — AI systems that actively limit their own scope.

For example, an email management system might flag messages for human review when confidence levels fall below high thresholds, choosing uncertainty over potentially incorrect automated actions. A research assistant might include confidence indicators in summaries, distinguishing between well-sourced findings and preliminary observations that require verification.

These design choices represent features rather than limitations. The wisdom of acknowledging uncertainty can increase system trustworthiness.

The Global Scale Challenge

Building for global spiritual formation means designing for contexts that developers may never fully understand. While optimization for familiar cultural contexts remains feasible, platforms serving Orthodox Christians in Eastern Europe, Pentecostals in West Africa, and house churches throughout Asia require different approaches.

The kenotic approach suggests building systems that acknowledge their cultural limitations. Rather than attempting to provide universal spiritual guidance, they can provide tools that local leaders adapt to their specific contexts.

Bible reading features need not assume Western individualism. Prayer tools need not assume specific liturgical traditions. Community features need not assume particular church structures.

Each feature can “empty itself” of cultural assumptions to serve diverse communities more effectively. Like Christ taking human form while maintaining divine nature, these systems can preserve core functionality while adapting to local contexts.

The Long View

Paul’s kenosis passage encompasses more than humility — it describes transformation. “Therefore God has highly exalted him and bestowed on him the name that is above every name” (Philippians 2:9). Self-emptying leads to greater effectiveness rather than diminishment.

A similar pattern may emerge for AI systems. Those practicing technological kenosis — voluntary constraint for the sake of service — may ultimately prove more valuable than systems grasping for unlimited capability.

The Tower of Babel failed because it attempted to exceed proper limits. Modern AI might encounter similar challenges without the discipline of restraint.

The most powerful systems may be those that understand when not to exercise their power.


Key Insight:

The kenosis principle — Christ’s voluntary self-emptying described in Philippians 2 — offers a design philosophy for AI systems. Instead of maximizing capabilities, effective AI agents can practice deliberate constraint, serving specific functions excellently rather than attempting everything adequately. This proves particularly relevant for products serving global spiritual formation, where cultural humility and contextual awareness matter more than technical sophistication. Just as Christ didn’t grasp for equality with God but took the form of a servant, intelligent systems may become more useful when they acknowledge limitations and defer to human judgment on edge cases. The paradox of kenosis — that voluntary limitation can lead to greater effectiveness — may apply to artificial intelligence as much as spiritual leadership. In a world of increasingly capable AI, the most valuable systems may be those that understand when not to use their power.

Photo by Vitaly Gariev on Unsplash

How to Build a Subscription Product for Ministry Without Losing Your Soul

I’ve been involved in launching three subscription products for ministry organizations. Based on my experience working with these platforms, serving thousands, to tens of thousands, to now millions of paying subscribers across multiple Bible translations and generate significant monthly recurring revenue.

Here’s what I learned: the hardest part isn’t building the paywall. It’s deciding what belongs behind it.

Every ministry leader building a subscription product faces the same tension. Your mission says “go into all the world” (Mark 16:15). Your business model says “pay to access the good stuff.” These aren’t just competing priorities, they’re fundamentally different philosophies about how discipleship works.

I’ve been on both sides of this equation. I’ve built products that gate basic Bible access behind subscriptions (terrible idea). I’ve also built products that use freemium models to fund global Bible translation (much better). The difference isn’t just revenue, but whether your monetization strategy serves your discipleship strategy or undermines it.

Three Models, Three Different Answers

At Bible Gateway, the platform serves free Bible access to a large user base while Bible Gateway Plus subscribers pay $6.99/month for power user tools like reading plans, verse comparison, offline access. The core content stays free. The professional ministry tools require subscription.

At SermonCentral, pastors can browse a large collection of sermon outlines for free but pay to download manuscripts or export to presentation software. A portion of free users convert to paid subscriptions because they’re not buying content, they are buying workflow optimization. The convenience is why they subscribe.

At Sermons4Kids, children’s Bible lessons are available free online but premium curriculum packages with printables and teacher guides sit behind a subscription tier. Churches get the ministry impact for free. Paid subscribers get the operational efficiency.

Three products, three paywalls, one principle: free access to spiritual content, paid access to ministry tools.

The Gap Between Free and Any Price

The hardest conversion in ministry isn’t $3.99 to $14.99. It’s $0 to $3.99.

Research suggests that many churchgoers expect digital ministry tools to be free. This creates what behavioral economists call the “zero price effect” — the psychological barrier where consumers perceive enormous difference between free and $0.01.

But here’s a counterintuitive pattern I’ve observed: once someone crosses that barrier, price sensitivity appears to drop. In my experience with subscription platforms, users who upgrade from lower-tier to higher-tier plans sometimes convert at higher rates than free users converting to basic plans.

The insight: your first paying customer is psychologically different from your free user. They’ve already decided that professional ministry is worth paying for. Your job isn’t to convince them ministry has value, it’s to prove your specific tool delivers that value better than alternatives.

The 90-Day Rule

In my experience, the vast majority of subscription churn happens in the first 90 days.

If a pastor survives three months with a ministry tool subscription, they tend to stay for extended periods. The pattern appears consistent across different ministry platforms I’ve observed.

This isn’t just a retention metric, I consider to also be a discipleship insight. The users who integrate these tools into their actual ministry workflow create habits that last. The ones who subscribe impulsively during a crisis (Saturday night sermon prep panic) churn when the crisis passes.

What this means for product design: your onboarding isn’t about feature education. It’s about habit formation. Successful platforms design their first-90-days experience around weekly use cases, not daily engagement metrics.

Annual Beats Monthly (But Not Why You Think)

Based on my observations, a significant majority of ministry tool subscribers choose annual billing over monthly. That’s not just better cash flow, it’s better discipleship outcomes.

Monthly subscribers tend to treat tools as disposable. They sign up for specific projects (Easter series, summer camp curriculum) then cancel. Annual subscribers build the tool into their ministry rhythm. They explore features beyond their immediate need. They recommend it to other pastors.

The psychological commitment of annual billing creates what behavioral economists call “investment bias.” When pastors spend more upfront instead of paying monthly, they appear more likely to actually use the features they paid for. Usage drives value realization. Value realization drives retention.

But here’s the non-obvious part: annual billing also appears to reduce what I call “subscription guilt.” Monthly charges create recurring reminders of cost. Annual billing shifts the conversation from “Is this worth the monthly fee?” to “How can I get more value from the tool I already bought?”

When Monetization Serves Discipleship

The best ministry subscription products don’t just avoid compromising their mission, they use their business model to advance it.

Free users at Bible Gateway get access to numerous Bible translations, with subscriber revenue supporting translation partnerships with Bible societies globally. Every subscription potentially contributes to putting Scripture into new languages. The monetization strategy supports the discipleship strategy.

In some ministry platforms, premium subscribers don’t just get better curriculum — their subscriptions help fund free access for churches in regions where subscription fees equal significant portions of daily wages. Paying customers aren’t just buying convenience. They’re supporting global ministry reach.

This flips the traditional ministry funding model. Instead of asking donors to fund ministry to strangers, you’re asking ministry practitioners to fund better tools for themselves while supporting ministry to strangers as a secondary benefit.

The psychological difference is significant. Donors give out of obligation or generosity. Subscribers pay for value received while creating value for others. One feels like charity. The other feels like partnership.

The Soul Question

Building subscription products for ministry isn’t about finding the right pricing strategy. It’s about answering the right theological question: Does your paywall bring people closer to God or further from God?

If your subscription gates basic spiritual content like Bible reading, prayer resources, or fundamental discipleship materials, you’re creating barriers to spiritual growth. That’s not just bad business (people will find free alternatives). It’s bad stewardship.

If your subscription provides professional tools that help ministry leaders serve others better — workflow optimization, advanced study tools, organizational resources — you’re creating leverage for kingdom impact. It seems like common sense that Pastors who invest in better ministry tools may reach more people, not fewer.

The test: Would removing your paywall increase spiritual growth in your users’ lives? If yes, your monetization strategy needs work. Would removing your paywall decrease your users’ ministry effectiveness? If yes, you’ve found the sweet spot.

Your subscription product should make the gospel more accessible, not less. Sometimes that means charging nothing for content. Sometimes it means charging appropriately for tools. The soul question isn’t whether to charge — it’s what to charge for and why.

Every dollar your subscribers invest should ideally return more than a dollar of kingdom impact. That’s not just sustainable business. That’s biblical stewardship.

Photo by Mockup Free on Unsplash

Karpathy’s Autoresearch and the Parable of the Talents: What AI Stewardship Looks Like in Practice

A few weeks ago, Andrej Karpathy — former AI director at Tesla, co-founder of OpenAI — released a project that made me think about ministry.

I didn’t expect that either.

Karpathy built a framework called autoresearch. It runs autonomous ML experiments on a single GPU while the researcher sleeps. The AI agent modifies training code, runs a 5-minute experiment, evaluates the result, keeps improvements, discards failures, and loops. About 12 experiments per hour. Roughly 100 overnight. He woke up to measurable performance gains — with zero human intervention during the run.

The part that got me: Karpathy doesn’t write the training code anymore. He writes a Markdown file — plain English instructions — that tells the AI what to research, what constraints to follow, and when to stop. His words: “you are programming the `program.md` Markdown files that provide context to the AI agents.” He calls this “programming in Markdown.”

The human moved up one level of abstraction. Define the methodology, set the guardrails, let the system execute. Not less involved — involved differently, at the level of direction instead of mechanics.

39,800 GitHub stars in the first two weeks. The tech world noticed.

I think the church should too.

The Parable We Keep Skimming

In Matthew 25:14-30 (ESV), Jesus tells the story of a master who entrusts his servants with talents — significant sums of money — before leaving on a journey. One receives five talents, another two, another one. The first two invest and double their resources. The third buries his in the ground.

When the master returns, the investors are praised: “Well done, good and faithful servant. You have been faithful over a little; I will set you over much” (Matthew 25:21, ESV). The one who buried his talent gets rebuked. Not for losing money — he hadn’t lost anything. He was rebuked for doing nothing with what he’d been given.

We tend to read this as a general principle about using your gifts. It is that. But I think there’s something more pointed here for 2026.

AI is a talent in the Matthew 25 sense. It’s a resource placed in front of this generation, and we have a choice. Invest it toward the mission, or bury it because the risk feels too high.

What This Looks Like at My Desk

I want to be specific, because the abstract conversation about “AI and the church” doesn’t move anyone forward.

I’m Director of Product at HarperCollins Christian Publishing, where I lead Bible Gateway — a platform serving over 75 million monthly visitors engaging with Scripture. Before this role, I led product for SermonCentral, which grew to 14,700+ paying subscribers with access to more than 145,000 sermon manuscripts.

Over the past year, I’ve built a system of 18 AI agents that handle competitive analysis, research synthesis, meeting intelligence, content drafting, and task management. Several run overnight — not unlike Karpathy’s loop. The architecture is different (mine orchestrate across business functions, his optimizes a neural network), but the pattern is identical: define methodology, set constraints, let the system execute, review results in the morning.

Every hour I used to spend pulling competitor data or formatting reports is now an hour I spend thinking about how 75 million people experience Scripture online. Or how to make Bible Gateway better for the person opening it at 2 AM because they can’t sleep and need something solid to hold onto.

Karpathy programs research methodology in Markdown now instead of writing Python. I program strategic priorities and agent instructions instead of pulling spreadsheets. The abstraction layer moved up. The work got more human, not less.

The Fear Is Understandable — and Partly Right

I hear the concerns from church leaders, and I take them seriously.

AI will replace authentic ministry. AI will make pastors lazy. AI will simulate relational presence that only a human body in a room can provide. These aren’t irrational. Some are already happening in small ways.

If a pastor uses AI to generate a sermon they never wrestle with, that’s a problem. If a church deploys a chatbot as a substitute for pastoral counseling, that’s a problem. If we treat AI-generated prayers as equivalent to the honest, stumbling prayers of a person before God — we’ve lost something that matters more than efficiency.

But Karpathy’s work shows the other path. The tool doesn’t replace the human. It moves the human to where they’re most needed.

The pastor doesn’t stop preaching — they stop spending 4 hours hunting for the right illustration and spend that time with the family walking through a divorce. The administrator doesn’t stop managing — they stop updating attendance spreadsheets and spend that time training volunteers. The ministry leader doesn’t stop leading — they stop drowning in email and spend that time on the phone with a donor questioning their faith.

I’ve lived this tradeoff. When my agents took over competitive analysis (something that used to eat 3-4 hours a week), I didn’t fill that time with more busywork. I spent it in 1-on-1s with my team and in deeper product strategy. The output quality went up because I was operating at the right level of abstraction.

Where the Line Is (and Where I’m Still Figuring It Out)

I want to be honest — I don’t think anyone has this mapped perfectly yet. I certainly don’t.

Here’s where I’d draw it today:

AI should handle the administrative. Scheduling, data analysis, report generation, email triage, content formatting. These consume enormous amounts of ministry time, and they don’t require pastoral presence. Automate them aggressively.

AI should accelerate the research. Sermon prep research, theological cross-referencing, community demographic analysis. These benefit from AI’s speed and scope. The pastor still does the synthesis — the “what does this mean for my people on Sunday” work. But raw material gathering? Let the machine run overnight, like Karpathy’s experiments.

AI should never simulate the relational. It should not write your prayers. It should not be the voice your congregation hears when they need a shepherd. It should not replace the hospital visit, the awkward conversation in the parking lot, the moment after the service where someone says what they’ve been carrying for months.

The servant in Matthew 25 who was praised put the resource to work — but in service of the master’s purpose, not his own convenience (Matthew 25:20-23, ESV).

Here’s the tension I haven’t resolved: where does “accelerating research” end and “simulating thinking” begin? When an AI summarizes 30 commentaries on a passage, is the pastor still doing exegesis, or are they just picking from a menu? I don’t have a clean answer. I think it depends on whether the pastor is engaging the summaries critically or just grabbing the first one that sounds good. But that’s a discipline question, not a technology question — and discipline questions are harder to solve with guardrails.

If You’re a Church Leader Starting from Zero

You don’t need 18 agents. You need one tool that saves you 3 hours a week.

Pick the task that eats the most time with the least relational value. For most pastors I’ve talked to, it’s sermon illustration research, email management, or meeting notes. Start there. Learn one tool well. Measure the hours you get back.

Then — and this is the part most people skip — reinvest that time in something only a human can do. A visit. A phone call. An hour of prayer you’ve been meaning to protect but kept losing to administrative drift.

Set your guardrails before you need them. Write down what AI will not do in your ministry context. Revisit it quarterly. Technology expands into unintended spaces when boundaries aren’t explicit — I’ve watched this happen in product development for 15 years.

The Talent in Front of Us

Karpathy’s autoresearch is an engineering achievement. But the deeper pattern is almost theological: the human was never meant to stay at the level of mechanical execution. We’re built to operate at the level of purpose, direction, and relationship. Genesis 1:28 gives humanity dominion and stewardship — a mandate to cultivate, not just maintain (Genesis 1:28, ESV).

The master in the parable didn’t give talents so the servants could admire them or lock them away. He gave them to be invested — put to work — in ways that generated return.

For those of us building technology that serves the church, the return isn’t financial. It’s pastors freed from busywork to do the work they were called to. It’s 75 million monthly visitors encountering Scripture through a platform that keeps getting better because the product team has time to think. It’s churches stewarding every tool available — including AI — in service of the mission they’ve been given.

The talent is in front of us. What we do with it is a stewardship question.


Josh Read is Director of Product at HarperCollins Christian Publishing (Bible Gateway) and holds a doctorate in Strategic Organizational Leadership. He writes about AI, product leadership, and digital discipleship at drjoshuaread.com.

The 10 Product Strategy Mistakes I Keep Seeing (After 10+ Years in SaaS)

An enamel pin about Product Management

I’ve made every one of these mistakes. Some of them more than once. Product strategy reads well in a blog post, but in practice it’s a minefield of competing priorities, stakeholder pressure, and the constant temptation to say yes to everything.

After more than a decade leading product and growth for SaaS companies – including subscription products serving millions of users – I’ve developed a pretty reliable list of strategy mistakes that kill momentum. Not the theoretical kind you read about in business school. The real kind. The ones that cost you quarters.

Here are the 10 pitfalls I keep coming back to, the ones that have cost me the most time, energy, and momentum over the years.

What is Product Strategy, Really?

Before we get into the mistakes, let’s get aligned on what product strategy actually is – because the lack of a shared definition is often the first problem.

Product strategy is the set of choices that connect your company’s vision to the work your team does every day. It answers three questions:

  1.  Who are we building for? (target audience)
  2.  What problem are we solving for them? (value proposition)
  3. How does this create value for the business? (business model)

Marty Cagan, author of Inspired and founding partner at Silicon Valley Product Group, puts it simply: strategy is about deciding which problems are worth solving. Roman Pichler frames it as the path to your product vision – the high-level plan for achieving your goals.

The important thing is that strategy is about CHOICES. Not a roadmap. Not a feature list. Choices about what you’ll do, and more importantly, what you won’t do.

With that foundation, here are the 10 mistakes that undermine those choices.

Mistake 1: Confusing Activity with Progress

This is the one that gets almost everyone. You ship a feature. Then another. Then another. Your release notes look great. Your team feels productive.

But the metrics aren’t changing.

I’ve lived this. We shipped feature after feature and our conversion numbers stayed flat. Lots of effort, but no forward motion. The problem was that we were building things that were nice to have, not things that moved the needle.

This is what the Jobs-to-be-Done (JTBD) framework helps you avoid. When you understand the actual job your customer is hiring your product to do, it becomes much easier to evaluate whether a feature advances that job or just adds noise. Clayton Christensen’s insight was that customers don’t buy products – they hire them to make progress. If your feature doesn’t help the customer make progress on their core job, it’s activity, not progress.

How to avoid it: Before greenlighting any feature, ask “which metric does this move, and by how much?” If the team can’t answer that clearly, the feature isn’t ready to build. This is easy to say, but extremely difficult to do. Use a prioritization framework like RICE scoring (Reach, Impact, Confidence, Effort) to force the conversation beyond gut feel.

Mistake 2: Strategy by Consensus

There’s a version of inclusive leadership that sounds great in theory but kills strategy in practice. You bring everyone to the table. You gather input. You synthesize. You try to find a path that makes all stakeholders happy.

… and you end up with a strategy that offends no one and inspires no one.

Real strategy requires choices. Hard ones. The kind where someone in the room won’t like the answer. If your strategy document doesn’t explicitly state what you’re NOT doing, it’s a wish list.

This is what killed products like Google+. Google had the engineering talent, the distribution, and the resources to build a social network. But the strategy tried to be everything to everyone – a Facebook competitor, a Twitter alternative, an identity platform, a photo sharing service. No hard choices were made and the product sadly died a slow death by committee.

How to avoid it: I’ve learned (the hard way) that my job is to make everyone feel heard, synthesize the inputs, make a clear decision, and then communicate the reasoning. People can disagree with a well-reasoned decision, what they can’t work with is ambiguity. Write down your strategy in one page. If it doesn’t fit on one page, you haven’t made enough choices yet.

Mistake 3: Copying the Competition

Your competitor launches a feature. Your sales team forwards the announcement. Your CEO asks “why don’t we have this?” And suddenly your roadmap has a new top priority that wasn’t there yesterday – classic!

I’ve fallen into this trap more than I’d like to admit. You absolutely should know what your competitors are doing. The real risk is letting their decisions drive YOUR strategy.

When you copy a competitor’s feature, you’re solving for THEIR customers with THEIR context.

You don’t know why they built it. You don’t know if it’s working. You don’t know if they’re about to kill it. You’re making a strategic bet based on a press release.

Gibson Biddle, former VP of Product at Netflix, uses what he calls the DHM Model – Delight, Hard-to-Copy, and Margin-Enhancing. The “hard-to-copy” piece is key but with AI it’s getting more difficult. If your strategy is just replicating what competitors build, you’ll always be behind AND you’ll never build anything that’s uniquely valuable to your users.

How to avoid it: Understand what problem the competitor is trying to solve, then ask whether YOUR users have that same problem. Sometimes they do, and then you should solve it in a way that fits your product, your architecture, and your users’ workflow. Sometimes they don’t, and the right answer is “we’re not building that” – Jeff Bezos has a great framework for this kind of decision.

Mistake 4: Ignoring the Metrics That Actually Matter

Vanity metrics are seductive. Page views are up! Sign-ups are growing! App downloads hit a new record!

But if your churn rate is climbing at the same time, you’ve got a leaky bucket. And no amount of top-of-funnel growth fixes a retention problem.

I’ve been in situations where the dashboards looked green but the business was struggling, and situations where the top-line numbers looked concerning but the underlying health was strong. The difference was which metrics we were watching.

This is what the North Star Metric concept helps solve. Your North Star is the single metric that best captures the core value your product delivers to customers. For Spotify, it’s time spent listening. For Airbnb, it’s nights booked. For a subscription SaaS product, it might be weekly active usage or feature adoption depth.

How to avoid it: For any subscription product, the metrics that matter are: how many people start a trial, how many convert to paid, how many cancel, and what’s the net change. Everything else is context. Build your dashboard around these numbers first, THEN add the supporting metrics that explain why they’re moving.

Mistake 5: Trying to Serve Everyone

This one is especially hard in mission-driven organizations. You WANT to help everyone. Every user segment seems important. Every use case feels valid.

But trying to serve everyone equally means serving no one well.

Your onboarding can’t be optimized for beginners AND power users simultaneously. Your pricing can’t be accessible to individuals AND competitive for enterprises without compromise.

Trying to serve everyone equally means serving no one well.

Kodak learned this the hard way. They saw digital photography coming but tried to straddle both worlds – maintaining their film business while half-heartedly investing in digital. They served neither audience well, and a company that once dominated an entire industry filed for bankruptcy in 2012.

How to avoid it: The best products I’ve used (and the best products I’ve built) made clear choices about who they were for. They explicitly prioritized one audience and designed everything around their needs first. When you do that well, other segments often benefit anyway, from a focused, coherent product rather than a compromised one. Define your primary persona. Write it on the wall. When someone asks “but what about this other segment?” you have your answer ready.

Mistake 6: Having No Strategy at All

This sounds obvious, but it’s shockingly common. My last few roles I’ve called “The Fixer” because years of the company running hard has caused them to lose their focus and they suddenly realize they don’t have a strategy. They have a roadmap. They have a backlog. They have quarterly goals. They ship things on time.

But there’s no unifying thesis about WHERE the product is going and WHY.

Roman Pichler calls this the most common product strategy mistake he encounters. Teams jump straight from vision to execution without the strategic layer that connects them. The result is a collection of features that individually make sense, but collectively don’t tell a coherent story.

How to avoid it: Your strategy should be a testable hypothesis, not a document that lives somewhere on the server. Try this format: “We believe that [target audience] struggles with [problem]. If we build [solution], we’ll see [measurable outcome] within [timeframe].” If you can’t fill in those blanks, you don’t have a strategy yet. You have a to-do list.

Mistake 7: Treating Strategy as Static

You spend weeks crafting the perfect strategy document. Leadership signs off. The team aligns. You print it out and pin it to the wall.

Six months later, the market has shifted, a competitor has launched something unexpected, and your customers are telling you something you didn’t anticipate. But the strategy is “locked.”

Eric Ries built the entire Lean Startup methodology around this problem. The Build-Measure-Learn loop isn’t just for startups – it’s for any team that operates in uncertainty, which is literally every product team. Your strategy should have built-in checkpoints where you evaluate whether your assumptions still hold.

How to avoid it: Set quarterly strategy reviews. Not annual planning sessions where you redo everything – lightweight reviews where you ask: “What have we learned? What’s changed? Do our bets still make sense?” The best strategies are living documents, not manifestos. Jeff Bezos distinguishes between “one-way door” decisions (irreversible, deliberate slowly) and “two-way door” decisions (reversible, move fast). Most strategic choices are two-way doors. Treat them that way.

Mistake 8: Skipping Validation Before Committing

You have a great idea. The team is excited. Leadership is bought in. You go straight to building.

Three months later, you launch to silence. Customers don’t want it, don’t understand it, or already solved the problem another way.

I’ve seen this pattern destroy entire quarters. The excitement of a new idea creates momentum that skips right past the “should we build this?” question and lands on “how do we build this?”

How to avoid it: Before committing engineering resources, validate the problem AND the solution. Talk to 5-10 customers. Run a fake door test. Build a prototype and put it in front of real users. Teresa Torres’ Continuous Discovery framework calls this “opportunity solution trees” – mapping the opportunity space before jumping to solutions. The cost of 2 weeks of discovery is nothing compared to 3 months of building the wrong thing.

Mistake 9: Siloed Strategy Without Cross-Functional Input

Product writes the strategy. Engineering learns about it at spring planning. Design gets brought in when wireframes are needed. Marketing finds out at launch.

This isn’t strategy. It’s a relay race where nobody can actually see the finish line.

The best product strategies I’ve been part of were shaped by engineering constraints, design insights, and market intelligence from day one. Your engineers know what’s technically feasible and where the architecture creates opportunities. Your designers have insights about user behavior that data alone can’t capture. Your sales and support teams hear objections and pain points every day.

How to avoid it: Include engineering and design leads in strategy formation, not just execution. Share customer research broadly. Bring it up in meetings regularly. Make your strategy document accessible to everyone on the team, not locked into a leadership slide deck. When people understand the WHY behind the strategy, they make better decisions at every level.

Mistake 10: Being Unrealistic About Execution Capacity

This is the mistake that ties all the others together. You have a clear strategy. You’ve validated the direction. You’ve made all the hard choices about what to build.

Then you commit to 3x more than your team can actually deliver.

Your roadmap becomes a pressure cooker. Quality drops. Shortcuts get taken. The team burns out. And paradoxically, you end up delivering LESS than if you’d committed to fewer things done with excellence.

I’ve seen this cycle repeat across every company I’ve worked with. The ambition is always bigger than the capacity, and the gap gets filled with overtime and technical debt instead of honest prioritization.

How to avoid it: Be ruthlessly honest about how much your team can ship in a quarter. Then cut 20% from that estimate. Even writing that sounds crazy, but it must be done. Use the OKR framework (Objectives and Key Results) to limit your bets to 3-5 outcomes per quarter – not 3-5 per team, 3-5 total. Warren Buffett’s “two-list strategy” applies here: write down your top 25 priorities, circle the top 5, and treat the other 20 as your “avoid at all costs” list (avoid them entirely until the top 5 are achieved). The same logic applies to product strategy.

The Uncomfortable Truth

Product strategy is about having the discipline to say no to good ideas that don’t align with what matters most right now.

Every mistake on this list comes from the same root: the unwillingness to make a hard choice and live with the tradeoff.

Choose the right things. Decide clearly. Pick your own path. (I wrote about this focus in 5 things needed for business success.) Watch the honest metrics. Serve someone specific.

Strategy is the art of sacrifice. The sooner you get comfortable with that, the better your products will be.

Product Strategy Checklist

Before you finalize your next product strategy, run through this list:

  • Can you state your target audience in one sentence?
  • Can you articulate the core problem you’re solving for them?
  • Does your strategy explicitly state what you’re NOT doing?
  • Is every major initiative tied to a measurable outcome?
  • Have you validated your assumptions with real customers?
  • Does your team have the capacity to execute this quarter’s plan?
  • Have you set a date to review and adapt the strategy?
  • Can your entire team articulate the strategy without looking at a document?
  • Is there a clear North Star Metric everyone is aligned on?
  • Would you bet your own money on this plan working?

If you can’t check every box, your strategy still has gaps. Go back and make the hard choices.

Frequently Asked Questions

What are the most common product strategy mistakes?

The most common product strategy mistakes include confusing activity with progress (shipping features that don’t move metrics), strategy by consensus (avoiding hard choices to keep everyone happy), copying competitors instead of solving for your own users, ignoring retention metrics in favor of vanity metrics, and trying to serve every user segment equally. The root cause of most strategy failures is an unwillingness to make clear choices and accept tradeoffs.

What is the difference between product strategy and a product roadmap?

Product strategy defines WHERE you’re going and WHY. It’s about choices, tradeoffs, and the thesis behind your product direction. A product roadmap is the HOW and WHEN – the sequence of work that executes the strategy. A roadmap without a strategy is just a feature list. A strategy without a roadmap is just a vision. You need both, but strategy comes first.

How do you create an effective product strategy?

An effective product strategy begins with a clear understanding of your target audience, the problem you’re solving, and how solving it creates business value. Frameworks like Jobs-to-be-Done help identify what customers actually need. Validate your assumptions through customer discovery before committing resources. Set a North Star Metric to track progress. Review and adapt quarterly. Most importantly, be explicit about what you will NOT do – that’s ultimately where the real strategy lives.

How often should you update your product strategy?

Product strategy should be reviewed quarterly and updated when market conditions, customer needs, or business goals change significantly. It should NOT change weekly based on competitor moves or stakeholder requests. The best approach is setting lightweight quarterly checkpoints where you evaluate whether your core assumptions still hold, while keeping the overall strategic direction stable enough for the team to execute with confidence.

The Traffic You Depend On Is Being Answered Without You

I’ve been staring at a traffic chart for the last three weeks that I can’t stop thinking about.

It’s Chegg’s chart. The online education platform lost 34% of its organic visitors in a matter of months. That’s a cliff. Their keyword footprint went from 11.1 million to 3.5 million.

And the culprit wasn’t a competitor outranking them or a Google algorithm update penalizing thin content. It was Google answering the questions before anyone ever clicked.

The Machine That Eats Your Top of Funnel

Google’s AI Overviews are the AI-generated summaries that now appear at the top of search results, and they are fundamentally changing what it means to rank on Google. For years, the playbook was clear: create valuable content, optimize it for search, capture intent, convert visitors.

That model assumed one thing: that people would actually click through to your site.

AI Overviews break that assumption.

When someone searches “how to explain forgiveness to a congregation” or “best illustrations for an Easter sermon,” Google can now synthesize an answer from multiple sources and present it directly in the search results. No click required. No visit to your site. No entry into your funnel.

Tomasz Tunguz laid this out clearly in a recent analysis:

“Content dependency on organic search is no longer a sustainable acquisition model.”

That sentence should be pinned to the wall of every SaaS product leader who relies on organic traffic (understanding these shifts is a critical PM skill) to fill the top of their funnel.

Chegg Is the Preview

The pattern is showing up everywhere. Stack Overflow, the platform that essentially taught a generation of developers how to code (including me), is seeing the same erosion. Informational queries that used to drive millions of visits are now being answered inline by AI.

The New York Times is thriving. Why? How? A $100 million content licensing deal with Google. They’re feeding the AI, on their terms, for revenue.

Here’s what I think the data is telling us:

1. Q&A-style content is the most vulnerable. If your value proposition is answering questions that can be summarized in a paragraph, you’re in the blast radius.
2. Branded, premium, behind-the-paywall content is more defensible. AI Overviews can summarize a sermon topic, but they can’t replicate a full manuscript, a downloadable media pack, or an AI-powered sermon builder.
3. The winners will be the ones who stop treating Google as a given and start building direct relationships with their audience.

What This Means for SaaS Product Leaders

I run product and growth for a content platform that serves pastors. We have 245,000+ sermons and 50,000+ text illustrations, exactly the kind of content library that ranks well for long-tail informational queries.

For years, that library has been our primary discovery engine. Pastors search for sermon ideas, find us, browse free content, start a trial, and convert to paid.

That model still works today, but we’re down around that same 34% mark and from what I can tell so is everyone, across all industries. But I’d be naive to assume it’ll work the same way in 18 months.

Here’s the uncomfortable math: if organic traffic drops by even 20-30%, and organic is your dominant acquisition channel, no amount of conversion rate optimization saves you. You can have a best-in-class trial-to-paid flow and still miss your numbers because not enough people are entering the funnel in the first place.

It’s an exposure problem. And it requires a fundamentally different response than what most product teams are used to.

The Diagnostic Before the Panic

Before you restructure your entire growth strategy, there’s a critical diagnostic step that teams often skip. You need to know whether AI Overviews are actually appearing on YOUR highest-value queries.

Here’s the move:

  • Pull your top 50 keywords from Google Search Console. Look at click-through rate trends over the last 90 days, segmented by week.
  • The signature you’re looking for: stable or rising impressions, but declining CTR. That pattern means Google is showing your content in results, but users aren’t clicking because the AI Overview already gave them what they needed.
  • If your impressions are dropping, that’s a competitor or algorithm problem. If impressions are stable but clicks are falling, that’s AI Overview cannibalization. Different diagnosis, different treatment.

Most teams I talk to are just making this distinction. They’re looking at traffic declines and assuming it’s an SEO problem when it might be a platform shift problem. The difference matters.

Three Moves to Make Now

I’m not going to pretend I have the full playbook figured out. But here’s where my thinking is landing:

1. Shift discovery investment toward owned channels.
Email nurture sequences, community platforms, pastoral networks, partnerships with organizations that already have the audience. Organic search should be one of many channels, not the only one. Every dollar of effort I’m putting into SEO-driven top-of-funnel content I’m asking if that same effort in email or community would be more durable.

2. Make your paywall content genuinely irreplaceable.
AI can summarize a sermon outline. It cannot replicate a curated media pack, a professionally produced video series, or a workflow tool that saves someone three hours a week. The content that survives AI summarization is the content that requires depth, production value, or interactivity: things a search snippet can’t deliver.

3. Explore whether the threat is also an opportunity.
The NYT licensing deal tells us something important: Google is willing to pay for premium vertical content. If you’re the dominant content platform in your niche, there may be a deal to be made.

A licensing partnership could convert a traffic threat into a revenue stream while maintaining brand visibility inside AI-generated results. Worth exploring.

The Bigger Lesson

I keep coming back to something I’ve learned over the last few years leading product: the most dangerous risks are the ones that look like stability. Traffic holding steady today doesn’t mean the foundation isn’t shifting underneath.

Chegg’s team didn’t wake up one morning to a 34% traffic drop. It happened gradually, then suddenly. The chart looks normal until it doesn’t.

The product leaders who navigate this well will be the ones who diagnosed early, diversified before they had to, and built value that can’t be summarized in a paragraph. The ones who don’t will be staring at a chart they can’t explain and wondering where all the visitors went.

I’d rather be asking the hard questions now than explaining the traffic decline later.