Here’s what I’ve been chewing on lately.

I’ve spent many years trying to scale ideas for global impact. Take something we tested on a small scale and push it to a larger audience. I’ve tried to scale training, tools, and reach. These days, the team I’m a part of is working to scale workers, resources, and partnerships.

And here’s what I’ve been slow to learn. In scaling, “more” is not just a bigger version of what we were already doing. It’s something altogether different.

It took me several years to realize I was wrong about that.

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More Is Different

In August of 1972, a physicist named Philip Anderson published a short essay in *Science* magazine. It landed like a grenade in the physics world, and it should land the same way for anyone thinking about missions.

The essay was called *”More Is Different.”*

Anderson’s argument was simple. The reductionist assumption (that if you understand the smallest pieces of something, you can predict the whole) is wrong. At each new level of complexity, something genuinely new appears. It’s not just more of the same. It’s something different.

He wrote:

“The whole becomes not merely more, but very different from the sum of its parts.”

I don’t usually read science magazines, but I saw this essay referenced in a book I was reading, so I tracked it down. It confirmed a hunch I’d been carrying about scale. Scale creates complexity. It isn’t always just a bigger version of the smaller pieces.

Let me put it in an analogy. You can know everything about a single water molecule and still not predict that at a certain temperature it becomes ice. Same with faith. You can understand one believer and still not predict what happens when several gather as a church. You can know everything about a single church and still not grasp the impact of multiple churches upon a region.

More isn’t just more. More is different.

The Reductionist Trap

We fall into this trap in ministry all the time. We reduce discipleship to a curriculum, church planting to a method, the missionary task to an assignment. It’s possible to track outputs and completely miss what’s emerging.

Anderson spent his career fighting a version of this in physics. The assumption that the “most fundamental” science was the one studying the smallest particles, and everything else was just an application of those laws. He pushed back hard. At every new level of complexity, entirely new laws appear. New behaviors emerge that couldn’t have been predicted from studying the individual pieces.

In the same way that psychology is not applied biology and biology is not applied chemistry, a multiplying church in a region is not just applied evangelism.

Something new breaks in. Something that couldn’t have been predicted from the first gospel conversation, the first disciple, or the first small gathering.

What Emerges When the Gospel Takes Root

I’ve been trying to wrap my head around a framework for understanding progress in a mission field. We use a seven-phase model to track gospel momentum, from Phase 0 (no activity) all the way to Phase 7 (indigenous cross-cultural sending).

Here’s what I suspect. Each phase transition isn’t just progress. It’s the emergence of something new.

Take Phase 2 to Phase 3. The moment disciples appear is not just more gospel proclamation. Something new has entered the field. There are now people who have crossed from death to life. A community of the redeemed exists where none did before.

Phase 3 to Phase 4. The moment a church forms is not just more disciples meeting together. A new organism has come into being: the body of Christ in that place. Categorically different from a small group.

Phase 4 to Phase 5. A church that begins to reproduce is Anderson’s whole argument in missiological form. A church that plants churches is not a bigger church. It’s a different kind of church. It has crossed a threshold.

And Phase 7? The moment that indigenous, multiplying network begins sending cross-culturally, the mission field itself becomes something different. Not just more. Different.

Binary Is Killing Us

Here’s where Anderson’s essay gets uncomfortably practical.

For decades, the missions community has operated with a binary classification system: engaged or unengaged. A people group either has a gospel witness or it doesn’t.

That’s reductionism, and it has the same flaw Anderson identified.

A binary label can’t track emergence, see momentum, or detect when work has stalled or regressed. A people group technically “engaged” that has had no active workers for years, no disciples, no forward movement, keeps the label even though the reality has changed.

A people group with one worker and no believers gets the same designation as one with fifty reproducing churches. The label says they’re both “engaged.”

Anderson would say you’ve collapsed a dynamic, living system into a single bit of data. The binary is a snapshot. The mission field is a film. A snapshot taken at first engagement tells us almost nothing about what happened a year, two, or ten years later.

What This Means for You

Maybe you’re a pastor. Maybe you’re a missions leader, a sender, a supporter, or a missionary. Here’s the question Anderson’s essay forces on us.

Are we managing emergence, or are we trying to control it?

A church that becomes a sending church is not a bigger local church. It has crossed a threshold. New identity. New responsibility. If you manage it like a program, like more of what it was before, you’ll miss what it actually is.

A believer who moves from consumer to disciple-maker hasn’t just leveled up. Something new has emerged. Don’t treat them like a slightly better version of who they were.

And a field that goes from first worker to indigenous sending movement is not just more missions activity. It’s the answer to the prayer of Matthew 9:38.

“Ask the Lord of the harvest to send out workers into his harvest field.”

The Lord didn’t just send more workers from the West. He raised them up from the field itself.

That’s emergence. That’s what happens when the gospel takes root.

The Mustard Seed Knew

Jesus never pictured God’s work as a pile getting bigger.

He used a seed becoming a tree. He used yeast disappearing into dough, invisible and silent, changing everything.

That’s emergence. Not addition. Transformation.

Philip Anderson spent his career studying this in physics labs. He never knew he was describing something Jesus taught two thousand years before him.

The mission field is not just more people. It’s a different world. And maybe the invitation is to stop managing the harvest like it’s the same field it used to be.

What’s one place in your life (a relationship, a church, a community) where you’ve been managing for “more” when God might be doing something categorically different? I’d love to hear from you.

The 250th anniversary of the US has sort of snuck up on me. I realized it about a month ago and to celebrate, I decided to read some biographies of some of the early leaders of the country. Also, my wife and I watched the George Washington movie last week, but I found myself thinking not about the loud founders like Washington or Jefferson, but about the small, quiet one who actually built the system.

James Madison.

Think back to your American History class (if you ever took one). You’re not crazy. Madison didn’t write a book about organizational change. He simply led a nation to organize in a new way. It was governmental organizational change at the highest level.

If you walked past James Madison on a Philadelphia street in 1787, you might have assumed he was a clerk. He did not look like the architect of a new country. He was short, careful, often in fragile health, and more likely to win a fight with footnotes than fists.

And yet he designed the system that outlasted all of the other founding fathers.

Madison’s impact fascinates me because, like him, many people leading organizational change aren’t the room-dominating type. They’re the ones trying to move something heavy while louder voices get the credit. Today, I want to write about what the quiet engineer actually did, and why it still matters for anyone trying to lead an organization that would rather not move.

Madison designed for real people, not the people he wished he had

Most revolutionaries make the same mistake. They assume that if they just cast a compelling enough vision, people will rally behind that vision. They assume that with enough vision, everyone will suddenly do the right things for the right reasons... and then change happens.

Madison didn’t buy it. Neither do I.

Madison respected virtue, but he didn’t rely on it. He asked a different set of questions than the idealists around him were asking. Not “What if leaders are good?” but “What if leaders are overly ambitious?” Not “What if the country agrees?” but “What if the country splits into factions?”

He didn’t design for a perfect people. He designed for real people.

That’s the whole game, isn’t it? Too often, leaders build change plans for the team we wish we had, fully bought-in, no politics, no self-interest, no legacy loyalties, and absolutely no sacred cows. Then the actual humans show up and the plan cracks.

Madison started with the humans. He assumed ambition, faction, and fear were going to be the reality, not issues to be wished away. He built a structure that could survive them.

If you’re leading change, that’s your first move. Stop designing for the people you hope showe up to the meeting. Design for the ones who actually do.

He knew the real threat was slow, not sudden

My reading over the past couple of months reminded me of the US history that I had forgotten in classes I took decades ago. For example, before we had the Constitution, we limped along under the Articles of Confederation. On paper, it provided the new country freedom. In practice, the government was far too weak to raise money, enforce decisions, or even hold the thirteen divided states together.

Madison saw where that road ended. Republics don’t always die with a coup. Sometimes they die with paperwork and exhaustion.

Paperwork and exhaustion.

That’s how most institutions die too. Not in a dramatic collapse. In a slow drift. States ignoring national requests. Debts piling up. Everyone treating the shared mission as optional. Nations cannot survive on optional solutions. Neither can most organizations.

This happens too often to organizations of all sizes, not just republics and democracies. No crisis. No scandal. Just a quiet erosion where “we’ve always done it this way” outlasts anyone’s memory of why. The wineskin doesn’t burst. It just slowly stops holding anything.

Madison’s gift was that he treated the cracks as warning signs, not tolerable annoyances. He looked at the dysfunction everyone else was willing to live with and said, this is the future if we don’t act.

Maybe I’m just caught up in the semiquincentennial celebration, but Madison has some leadership discipline worth stealing. Learn to see the slow leak before it becomes the flood.

Madison took the compromise instead of the perfect plan

Here’s the part that gets Madison wrong in most people’s memory.

He lost.

At the Constitutional Convention, Madison wanted representation tied to population, a genuinely national principle. I admit, I had forgotten this until I read his biography. The problem was that small states were terrified of being swallowed up by the influence of the large states. This fight nearly broke the whole thing apart. The compromise that emerged was a two-house legislature, split between population and equal state weight, and it was *not* what Madison wanted.

He took it anyway.

Because his logic was simply this. A perfect plan that fails to be adopted is useless. A compromised plan that holds the nation together is powerful.

So many change efforts die on the altar of the perfect design. The leader has the elegant solution, the whole architecture worked out, and they’d rather see it rejected intact than watch it get compromised into something that actually ships.

Madison had the intellectual firepower to win every argument and the wisdom to know that winning the argument isn’t the point. Adoption of a solution is the point. A system nobody agrees to is just a great document sitting on your computer.

Madison adjusted without abandoning the machine

There’s this little thing called… The Bill of Rights.

Madison didn’t originally think it was necessary. He worried that naming specific rights might imply that any right *not* listed was fair game. That makes sense, but the ratification fight taught him something the reasoning couldn’t. People don’t live inside abstract logic. Much of the time they live in the fear of the unknown. Of what they don’t understand.

Madison adapted. He personally shepherded the amendments through the first Congress, the very protections that gave a suspicious public a reason to trust the new system.

Call it the ability to adjust without abandoning the machine. He held the core and flexed on the things that were not essential.

Madison never gave up the structure he believed in. But he was willing to add to it. To reshape it in order to meet people’s fear with something concrete. Legitimacy is a form of power. The most elegant system in the world is fragile if the people living under it don’t believe it protects them.

Stewardship of a mission means caring as much about whether people will actually *own* the change as whether the change is correct.

The irony Madison lived, and what it costs us to ignore it

The man who built restraints against concentrated power became president during a war, when power always stretches. The man who feared factions became a party leader. The man who wrote about limiting government watched that government flee a burning capital in 1814.

Madison never got to lead in the calm conditions he designed for. He governed in the storm. Fortunately, the structure he built held anyway, not because it was perfect, but because it was built for stress from the start.

That’s a lesson that runs deep, and it’s one that is also biblical. We don’t get to choose our conditions. Jesus told his followers to count the cost before building the tower and then sent them straight into a world that would resist them. The point was never a frictionless plan. The point was building something durable enough to survive the resistance.

Madison’s genius wasn’t that he avoided chaos. He couldn’t. Nobody can. His genius was that he built something strong enough to *survive* the chaos.

Madison Applied Today

250 years later, most of us aren’t drafting constitutions. We’re trying to move a team, a ministry, an organization that has grown comfortable and sometimes is a little tired. The temptation is always to wait for the perfect plan, the fully aligned people, and the calm season.

Madison never got any of that. He built anyway. That’s an enormous encouragement to me.

So here’s the question I’ll leave you with in this post. What change have you been holding back because you’re waiting for conditions Madison never had?

Design for the people who actually show up. Watch for the slow leak. Take the win you can ship. And build something durable enough to survive the storm you can’t avoid.

James Madison, the quiet engineer, would understand.

Recently, I spent half a day doing something completely unproductive. It involved a rented a dune buggy and driving it as fast as the dune buggy company would allow. It was a blast.

But something happened before I ever hit the throttle and it came up with a friend of mine as we were debriefing the experience.

The company handed me the keys to a dune buggy simply because I was over 16 years old. That’s it. That’s the whole qualification to drive one? Are you old enough? There wasn’t a test drive or a background check on my off-road experience. They asked me how old I was and essentially said, “We trust you to handle this machine.”

That’s empowerment!

But… I still wasn’t able to hop in and take off. There was an extensive safety briefing. They had rules… lots of rules.

1. No passing anyone… ever!

2. Single file line, no exceptions.

3. Follow at a distance of 15-20 feet. Not too close and no hanging back too far.

4. Wearing your helmet is required.

5. Wearing your seatbelt is required.

6. If there is an accident, stop, stay put, don’t touch the throttle.

After going through all the rules. They talked through the legal responsibilities. Injuries to me? Covered. They have liability insurance that will cover any injuries I might sustain. Damage to the buggy? Mine to pay for, dollar for dollar. All on me.

Then came the part I wasn’t expecting. They played a five-minute video. Real crash after real crash on their course with their buggies. Real people not paying attention, not following the rules, doing exactly what they felt like doing in the moment, and then the aftermath. Bent frames. Twisted metal. Buggies upside down. X-rays of broken bones.

Yikes… that was the accountability.

The company gave me real trust and real limits at the same time, and neither one canceled out the other. The trust made the fun possible. The limits made the trust safe.

What Most People Want

I was talking with someone who put words to this.

“Empowerment without accountability usually leaves a wake of frustration and problems.”

Think about that statement.

Most of us want the keys. We want the trust, the autonomy, the “go figure it out” mandate. We want to be the person who doesn’t need to be micromanaged. We want to set out 100 mph in the dune buggy without anyone telling us how to drive.

What we don’t want is the video of the crashes. We don’t want the lane restrictions. We don’t want someone telling us we’re responsible for the damage we cause.

We want empowerment without the seatbelt.

When leaders hand out empowerment without any accountability attached, here’s what actually happens. Not freedom. Not innovation. A wake. People colliding into each other. Damage nobody wants to own. Trust that erodes because nobody has clarity on what happens when something breaks.

Caution without action isn’t wisdom. It’s just slower failure. But action without accountability isn’t courage either. It’s just chaos.

Three Things the Buggy Company Understood

Allow me to unpack why that briefing worked so well for me, and I think it comes down to three things most organizations get wrong.

1. They defined the lane, not just the destination.

   
   They didn’t just say “have fun out there.” They told me exactly where the edges were. Single file. No passing. Consistent spacing. I still had freedom to drive the way I wanted inside those lines, but the lines were non-negotiable.

   
   Most of us do the opposite. We hand someone a vague mandate, tell them to “own it,” and then get frustrated when they drive somewhere we never wanted them to go. If you empower someone without defining the lane, don’t be surprised when they end up off the road.
   

2. They named the cost before it happened.

   
   Nobody had to guess what would happen if they wrecked the buggy. It was said out loud (and you signed a document), before the keys ever left the counter. Your injuries, we’ve got you. The damage you cause, that’s on you.

   Most teams never do this. We hand someone authority and quietly hope nothing goes wrong, and then when it does, we scramble to figure out after the fact who’s responsible for the wreckage. Naming the cost upfront builds respect. It tells people you believe they can handle the truth before they need it.
   

3. They showed the video.
   

   This is the part that was a little of a shock (and that was the purpose). They didn’t just tell us the rules. They showed us, in graphic detail, what happens when the rules get ignored. Not to scare us out of driving. To make sure we drove with our eyes open. When I was tempted to gun it and pass someone, the video came back to me.

   
   I’ve sat in lots of meetings where leaders hand someone real responsibility, and it’s the first time that person has ever seen what failure actually costs. Nobody showed them the video. We assume people understand the stakes. Usually they don’t, not until they’ve lived it themselves, and by then the damage is already done.

Application

I’ll own my part in this. I’ve handed people freedom and skipped the briefing. I’ve said “you’ve got this“ without ever naming what “this“ actually costs if it goes sideways. I’ve assumed people could read my mind about where the lane ended. That’s not real empowerment.

Here’s the big takeaway from this article. If you read nothing else, read this.

• Empowerment without responsibility → recklessness, inconsistency, entitlement, blame shifting.

• Responsibility without empowerment → resentment, burnout, learned helplessness, bureaucracy.

• Empowerment + responsibility together → ownership.

The Question to Ask

If you’re a leader, the dune buggy company already figured out what most of us are still fumbling through. Trust and boundaries aren’t opposites. They’re part of a package deal. You can’t hand someone real authority and skip the conversation about what happens when it’s misused, and you can’t hover over every decision and call it stewardship.

So here’s the question worth taking into your next team meeting:

Where have I handed out keys without ever showing the responsibility video?

Allow me to clear up a myth. The problem in most organizations isn’t a shortage of ideas. Sometimes it is. Some places really have gone dry, and if that’s you, that’s a different article, but that’s not the common failure. Walk into many organizations and you’ll find whiteboards full of ideas, innovation offsite meetings, and a “culture of experimentation” printed on a wall somewhere. Ideas aren’t the scarce resource.

The breakdown happens after innovation. It occurs one step later. The breakdown happens at integration.

Confession time

As I wrote about in another article, I sometimes feel as though I have more ideas than any one organization could ever possibly integrate. That’s a problme that I’m learning to manage.

For a long time I thought the ideas were my contribution. Generate enough of them, throw them at the wall, some of them stick, and surely progress follows. It doesn’t. What actually happens is you build up a backlog of good thinking that never touches the real work. The idea gets applause in the meeting and then goes nowhere, because nobody owned the far harder question of how it actually fits into the way the organization already runs.

Innovation without integration is a graveyard of good ideas. Beautiful headstones, every one of them, but they’re still dead. The operationalization of the ideas is the challenge.

Why integration is the part nobody wants

I’ll say what is obvious. At least for me, innovation is fun… integration is not. If that’s you… surround yourself with people who find joy in the integration phase.

Innovation is the ideation session, the prototype, and the buzz of “How might we?” It’s celebrated. It sometimes gets a budget line and the leadership shout-out. Integration is the slow, unglamorous work of rewiring an actual workflow so the new thing survives contact with reality. It means changing how people already do their jobs. It means retiring something. It means somebody has to own the messy middle where the new idea and the old system grind against each other. It’s where the friction lives.

Nobody puts “Q3 integrating” on a highlight reel. Too often, we innovate, we get that dopamine hit, and we move on to the next shiny thing before the last one ever took root.

That’s why so many organizations feel busy and innovative, yet they somehow never actually change. At best, they’re stacking ideas, but not integrating them.

AI is making this problem a much bigger and not solving anything without intentionality. AI has made idea generation and iterative creation nearly free. You can produce a hundred plausible strategies before it’s time for lunch. The bottleneck is not enough ideas. It was always going to be whether your organization can absorb them into how it really operates. In an AI-saturated world, integration isn’t the boring step.

Integration is a design problem

When integration fails, we usually blame people. People didn’t adopt it or they resisted. As a result, they went back to the old ways of doing things. Most of the time the idea just wasn’t designed to fit or even be implemented.

How might we integrate better? It starts by recognizing the positive aspects of the existing system. The old workflow isn’t in your way. It’s the ground you have to build these new solutions upon. Integration asks harder questions than innovation ever does. What does this replace? Who has to change what they do on Monday morning? Where does this touch the three other things nobody mentioned in the brainstorm? What breaks if this works?

An idea that can’t answer those questions isn’t ready.

Then comes the part everyone forgets… iterate

Say you do the hard thing. You integrate. The new idea is live, running inside the real workflow, actually being used. You’re still not done. You’ve just reached the starting line of the part that lasts forever.

The moment something is integrated, it begins to calcify. The workaround becomes the standard. The standard becomes “how we’ve always done it.” And a few years later that once-brilliant integrated idea is the very bloat a future team will have to fight to remove. Integration without iteration is just innovation with a longer shelf life.

Iteration is the discipline of coming back. It’s the idea of asking, now that this is real and we’ve watched it live in the wild, what did we get wrong? What did we learn that we couldn’t have known in the planning room? What’s the next small, honest adjustment? Or major adjustment?

Innovation is a moment. Integration is a decision. Iteration is a posture. It’s the humility to admit version one was never going to be right, and the commitment to keep improving the thing long after the excitement has worn off.

Most organizations have the moment. Few make the decision. Almost none hold the posture.

The stewardship underneath it

Think of it like this. Ideas are sort of a gift. So is the capacity of the people around you to carry those ideas out. When I generate a hundred ideas and integrate none of them, I’m not being visionary. I’m being wasteful with something I was trusted to steward, both the idea and the people who’d have to live with it. Faithfulness with an idea doesn’t end when you think of it. It actually barely begins there. It shows up in the far less glamorous work of making it real, and then making it better.

What’s my final take on this?

Innovate? Absolutely! Keep those ideas coming. Also fall in love with integration, because that’s where value actually gets created and unfortunately, where most good ideas normally die. Finally, intentionally iterate because anything worth integrating is worth improving for as long as it lives.

Anybody can have the idea, but it’s the truly unicorn organization that can finish the idea and then refuse to stop.

I was first introduced to Peter Drucker in my undergrad classes more than 35 years ago. At the time, I had no idea how revolutionary his thinking actually was. It was just another class… something to memorize for the exam. I definitely wasn’t thinking about organizational change. I was a college kid. Bureaucracy was not my problem.

It took a couple decades of real work for his ideas to stop being theory and start being uncomfortably accurate for me. Here’s one phrase that Drucker said that really resonates with me.

For most of business history, inefficiency has hidden in plain sight. Everybody felt it. The redundant report. The approval that took four signatures. The meeting that summarized the other meeting. We knew it was not helpful, but we tolerated it because killing it seemed like a Herculean effort. Inefficiencies just continued, year after year.

AI showed up and suddenly inefficiencies became more and more obvious.

The floodlight

Here’s something that I was discussing with a friend last week. AI doesn’t just automate work. It exposes work.

When intelligence gets embedded into the tools we already use, it’s like putting a floodlight on every task we’ve been pretending was necessary. You ask an AI to “help with the weekly status report,” and within thirty seconds you realize the report itself is the problem. Nobody reads it. Nobody acts on it. It exists because someone created it in 2017 and nobody’s been brave enough to stop.

This isn’t just a corporate problem. Sit in any ministry long enough and you’ll find our own version. The monthly field report that gets compiled, sent up the chain, and filed unread. The activity metrics we gather because we’ve always gathered them. Numbers that count what happened without ever asking if it mattered. Ministries have spent years measuring motion and calling it fruit. AI shines a light on that too, and the light is not always comfortable.

That’s the gift hiding inside this moment. The work we’d learned to tolerate suddenly becomes impossible to ignore.

“We’ve always done it this way”

When the floodlight comes on, the first thing it usually finds isn’t laziness. It’s inertia. Work that survives not because it matters, but because it’s always been there.

These are the tasks that don’t move the needle on anything that actually counts, yet we keep doing them anyway. The process got approved in a meeting nobody remembers and outlived everyone who was in the room. “We’ve always done it this way” quietly became the only reason we still do it.

That’s the most dangerous kind of waste, because it’s invisible. Nobody defends it. Nobody attacks it. It just persists, soaking up hours and attention while contributing nothing to the mission it supposedly serves. Compounded, this waste adds up.

So before we talk about doing anything faster, we have to back all the way up and ask a question that feels almost too obvious to say out loud, “Why are we doing this at all?”

Notice that the question isn’t, how do we improve it or how do we automate it. The question is, why does this even exist? If the answer is “because we always have,” that’s not a good enough reason.

The temptation is to automate the dumb thing

Most leaders, when handed the AI floodlight, do something strange. They use it to find the busywork, and then they begin to try to automate the busywork.

We see the useless report, and instead of killing it, we build a workflow or automation that generates it faster. We see the four-signature approval, and instead of asking why four humans need to touch it, we route it through an agent so it clears in minutes. We take something that should not be done at all, and we do it brilliantly. Efficiently. At scale.

Drucker would’ve had a field day with this. We’ve invented the most powerful tool for eliminating waste in the history of mankind, and our first instinct is to use it to preserve waste in a more polished form.

This is the automate-reflex, and it’s everywhere right now. Something feels slow, so we reach for the tool. The tool is so good, and so fast, that we never stop to ask the harder question sitting underneath it. Why are we doing this to begin with?

The wrong first question

Organizations are at a pivot point. The AI dividend is real, and almost everything is getting automated, enhanced, or rethought because of it. That creates enormous pressure to move, and pressure has a way of skipping steps.

So the question I hear most often goes something like this: “How can we do this more effectively?” Or the newer version, “How can AI help us do this better?”

I understand the instinct. But that’s the wrong first question. The first question is never how. It’s always why. Why are we even doing this? Why are we running this report? Why does this to-do exist on someone’s plate at all?

Only after you can honestly show that the task moves the needle on what matters have you earned the right to ask the next question. How might we make this better? Notice something about that second question. It works the same whether the answer involves AI or not. Efficiency is always the second question. The tool you reach for to achieve it is the third.

We’ve been starting at question three. AI just made question three so easy and so tempting that we’ve nearly forgotten the first two exist.

Why “efficient” became a trap

Part of the blame goes to a culture that treats efficiency like a virtue all by itself. Somewhere along the way, fast became good. Optimized became righteous. We started measuring leaders by how smoothly the machine runs, not by whether the machine should be running at all. Speed and activity feel like progress even when you’re sprinting in the wrong direction.

Efficiency is morally neutral. It’s a multiplier. Point it at meaningful work and it’s a blessing. Point it at meaningless work and you’ve just built a faster way to waste everyone’s time. The output looks impressive. The dashboards turn green. And underneath it all, you’re still doing the thing that should not be done.

Efficiency culture loves the green dashboard. I just wish the dashboard people would start by asking what does the dashboard measure?

Now what?

Here’s the problem, the window for fixing this is closing.

When inefficiency was clunky and manual, at least it announced itself. The pain was the signal. People complained about the report because filling it out hurt. That friction was honest. It told you something was broken and we had an admin overload problem.

Once you automate the bad process, the pain disappears. The report still gets made, only now no human feels the cost of making it. The friction that used to flag the problem is gone. The waste doesn’t stop. It simply goes quiet.

That’s the trap of embedding AI everywhere without discernment. You don’t eliminate the bad work. You bury it. You make it invisible, automatic, and permanent. A year from now nobody will even remember the task was optional, because it’s been humming along in the background, doing the wrong thing flawlessly, asking nothing of anyone.

Before you automate, ask questions

Here’s my take on AI and automation. The most important skill in an AI-ready organization isn’t prompting. It isn’t tooling. Instead, it’s the discipline to stop and ask whether the work in front of you deserves to exist before you make it faster.

How might we resist the reflex? A few questions worth asking before you automate anything:

1. If this task disappeared tomorrow, who would actually notice, and what would they lose?

2. Are we automating this because it matters, or because automating it is easy?

3. Once this runs invisibly, will we ever revisit whether it should run at all?

Notice that none of those are efficiency questions. They’re discernment questions. Discernment has always been the thing automation can’t do for you.

Stewardship

For me this goes a layer deeper than productivity.

Jesus told a story about three servants and the wealth their master left in their care. The one who got judged wasn’t reckless or lazy in the way we’d expect. He was careful. He took what he’d been given and buried it. He kept it safe and out of sight. The master’s verdict was severe, because the question was never whether the servant had been efficient with what he held. It was whether he’d done anything that mattered with it at all.

I think about that every time we talk about burying bad work inside an automation or processes. When we take a task that shouldn’t be done and quietly bury it in a workflow where no one feels its cost, we haven’t stewarded anything. We’ve just hidden it well.

Time is one of the few things we’re each given a fixed amount of. We don’t manufacture more of it. We only spend it. So when I automate the wrong thing well, I’m not just being inefficient in a business sense. I’m taking a finite gift and pouring it, smoothly and efficiently, into something that was never worth doing. Done is not the same as faithful. A polished waste of time is still a waste of time.

Drucker was making a management point, but the deeper truth has teeth. Doing the wrong thing well isn’t progress.

Kill the task first

For those of us in faith work, the stakes aren’t abstract. Every hour an organization pours into work that should not be done is an hour stolen from the work we actually exist to do. The waste isn’t just expensive. It’s distracting us from the work we’ve been called to do.

The leaders who lead their organizations to make the greatest impact won’t be the ones who automate the most. They’ll be the ones who eliminate the most before they automate anything.

Kill the unnecessary tasks first. Then, and only then, make what’s left run beautifully.

A friend mentioned Michael Porter’s 1996 HBR classic article What Is Strategy? this week. I have not read this in years and I had to track it down to read it again. Wow! It’s still relevant all of these years later.

Here’s a piece that’s nearly 30 years old and it describes with almost uncomfortable precision, what is happening in many organizations right now. The language is slightly dated, but the premise behind his article is still spot on.

Today, it seems that AI has everybody talking about optimizing. They want faster workflows, better processes, fewer redundancies, and most importantly… AI-assisted everything. Since I work in AI solutions, everyone thinks that I can just apply some AI magic and fix their problems. If only it was that easy.

Many people have confused operational effectiveness with strategy. Porter’s central argument is actually very simple

Operational effectiveness means performing your activities better than other people or other organizations. Doing things faster, cheaper, and at higher quality. Strategy means performing different activities, or performing the same activities differently, in a way that creates a unique and valuable position that others can’t easily copy.

Here’s the problem. When every organization chases best practices, benchmarks against the same people, and outsources to the same vendors, everyone starts to look identical.

Porter called this competitive convergence. It’s a race to the bottom that no one can win. I’ve seen this in lots of organizations. Higher education has been experiencing this at scale for years.

When AI “magic” has been added to organizational workflows, the instinct is to use it to do what we’re already doing… only faster. Automate the reports, speed up the communications, and process more data.

This is totally the wrong approach. When we just keep doing what we have been doing, only faster and more efficiently, we’ve not really improved anything. We’ve just created a faster treadmill.

Strategy would ask this question. Why are we running on this treadmill at all?

Porter lays out three sources of genuine strategic positioning:

1. Variety-based
   You do a subset of things exceptionally well (Jiffy Lube only does oil changes, and they destroy the full-service shops on speed and price for that one thing).

2. Needs-based
   You serve a particular group of customers in a comprehensive way. Concierge/Boutique Medicine means a doctor takes on a very limited number of patients and charges a premium to do so. In this case, the doctor to patient ratio is the strategy.

3. Access-based
   You reach customers in ways others structurally can’t (Carmike Cinemas operates only in cities under 200,000 people. They dominate because no one else bothers to design for that market.

The key word in all three is tailored. Not generic. Not best-practice. Not benchmarked. They have created a tailored strategy that differentiates them from others.

The activities have to fit together in a system that creates genuine trade-offs meaning competitors can’t just copy your best moves without blowing up their existing model.

That’s what makes a position sustainable.

Here’s the part Porter gets most right, and most people skip.

Trade-offs are the point.

We’ve been trained to believe trade-offs are signs of failure. We believe that a well-run organization should be able to do everything well. Porter says the opposite: if you don’t have trade-offs, you don’t have a strategy. You just have operations.

When Continental Airlines tried to imitate Southwest by keeping their full-service model while launching a low-cost brand, they bled money and made everyone angry. Not because the execution was bad. You can’t be both things without paying an enormous penalty.

The reluctance to say no is where strategy goes to die.

My Take

Porter ends with something that’s stuck with me since I reread it this week:

Strategy requires constant discipline and clear communication. Indeed, one of the most important functions of an explicit, communicated strategy is to guide others in making choices that arise because of trade-offs in their individual activities and in day-to-day decisions.

The leader’s job isn’t just to say yes to the right things. The leader’s job is to teach everyone else, explicitly, repeatedly, and with discipline what to say no to. That’s hard.

Most of us are avoiding it by staying very, very busy.

One question worth considering.

What is your organization uniquely designed to do and what are you currently doing that contradicts it?

If you are like me, you have really enjoyed using Anthropic’s Fable 5 this week. It has been an entirely new level of AI this week. It was great… until it stopped working tonight.

One minute I was watching the US winning against Paraguay as well as watching 120 AI agents run simultaneously, doing things that still feel impossible to fully describe. The next, nothing. I figured it was a bug, maybe a rate limit, maybe something on my end. I started troubleshooting. Checked my setup. Tried again. I opened Twitter to complain about the dive the Paraguay player made. Then I saw the Anthropic post that they had to pull their flagship model.

At 5:21pm ET on June 12, 2026, the United States government issued an “export control directive” ordering Anthropic to immediately suspend all access to Fable 5 and Mythos 5, citing national security authorities. Within hours, both models were disabled for every user on the planet, not just foreign nationals, but everyone, including Anthropic’s own foreign national employees. The government believed a method of bypassing the model’s safety limits had been discovered. Anthropic complied.

Just like that, the most capable AI model I have ever touched was gone. Hopefully this is only temporary.

What is Fable 5?

For those who missed this brief window of absolutely awesome AI, here’s the context.

Anthropic launched Claude Fable 5 on June 9, 2026, just three days ago, as the first publicly available version of its Mythos-class model. Mythos had existed since April in a highly restricted form, available only to a handful of organizations working on critical infrastructure as part of a government-adjacent program called Project Glasswing. The model was so capable in cybersecurity applications that access was treated more like a national security clearance than a software subscription.

Fable 5 was the version Anthropic determined was safe for general use, with hard limits in high-risk areas. It launched with a brief free window on Pro and Max plans, then was scheduled to move to a usage-credit model on June 23.

I had about two full days with it. Two days was enough to understand that something genuinely different had arrived.

120 Agents

Here’s the thing about Fable 5 that is difficult to explain to someone who hasn’t used a frontier model at this level. It doesn’t just answer questions better. It reasons differently, plans differently, holds more context, makes fewer errors across longer chains of logic, and executes complex multi-step tasks with a reliability that previous models couldn’t match.

Tonight, I was running 120 agents simultaneously. That’s nuts!!!

Let me be clear about what that means if that means nothing to you. That’s not 120 queries. Not 120 chat windows. This was one hundred and twenty distinct AI agents, each with a task, each working in parallel, each contributing to a larger project. The orchestration was remarkable. The output was remarkable. The sheer scale of what was possible in a single session was something I genuinely did not expect even from a model of this caliber.

I’ve been building tools with AI. I teach classes on AI and I am weekly writing about it. I have seen a lot of impressive demonstrations. This was next level and felt like a threshold had been crossed that most people outside this work don’t fully appreciate yet.

And then it stopped.

Safety Guardrails

Fable 5 was significantly more aggressive about triggering its safety guardrails than previous Claude models. I noticed this repeatedly the past couple of days. Routine penetration testing that I had been running without issue on other Claude models kept hitting violations with Fable 5. The model would shut down the task, decline to continue, and redirect entirely.

This matters because pentesting is legitimate, important security work. Finding vulnerabilities in your own systems before someone else does is not a fringe use case. It is responsible practice. And I had established workflows on other Claude models that handled it without friction.

Fable 5 didn’t. It was very sensitive to trying to perform pentesting. Anthropic had announced that Fable 5 had hard safety limits in high-risk areas, and cybersecurity is one of them. The full-capability Mythos 5 model, with those limits partially lifted, was reserved for approved organizations in Project Glasswing. What the public got was the constrained version, and the constraints were significant… and a little inconvenient for legitimate work on my own sites.

The most capable model most of us have ever touched and it was simultaneously more restricted than the models in specific workflows. More powerful in most directions. More cautious in others.

The government apparently believed someone found a way around those limits.

The Moment It Went Quiet

There is something genuinely disorienting about working at the edge of what technology can do and then having it disappear. It was a little frustrating and going back to the other models feels like I have stepped back in time!

The full picture of what is happening between the US government and Anthropic is complicated. There’s an ongoing legal dispute between Anthropic and the Department of Defense, a broader set of export control concerns, and a cybersecurity jailbreak claim that Anthropic says involved previously known minor vulnerabilities. To be completely fair, I don’t have all the facts and nobody does outside a very small group of people in Washington right now. My first reaction was this was retailiation. Time will tell

What I do know is this. A model that had much greater capabilities than the competition was made briefly available and I got to use it. The tools being built right now are not incremental improvements on what came before. They represent a shift. Governance frameworks, legal structures, and national security considerations around them are still being built in real time, often visibly and messily.

Implications

If you’re a builder and practitioner, not a researcher or a policy analyst but someone who actually builds things with these models, this week was wild.

• The frontier is moving faster than the infrastructure around it. Fable 5 was available for four days before a government directive took it offline. The organizations that had the clearest view of what this technology could do were the ones who were already in the water, already experimenting, already building. The gap between “curious about AI” and “actually using AI at scale” is quickly growing.
  

• Access to frontier capability is not guaranteed. It never has been, and the events of the last 24 hours make that clear. The model you built your workflow around can be gone by tomorrow morning.

• The questions this technology raises are only technical. Questions are also ethical, theological, geopolitical, and human. Why does a government believe it needs to intervene in access to a commercial AI model? What does it mean that a private company can build something capable enough to generate genuine national security concern? Who should have access to the most powerful AI systems in the world, and on what terms? These are not questions for regulators alone.

The Part I Keep Thinking About

120 agents.

I keep coming back to that number not because it’s impressive, though it is, but because of what it represents. Each of those agents was doing something. Reasoning, planning, drafting, analyzing, connecting, checking, etc... The combined output of that session was something no individual person, working alone, could have produced in the same timeframe… not even close. The leverage was extraordinary.

If you are not using AI like this. It was like having a massive job to be delivered. Normally that job may have taken six months and have a full tame of eight people and contractors working on it.

This was like building a team of 120 workers, all being orchestrated, supervised, and organized by an AI supervisory structure. This team of 120 was accomplishing all of the work of the eight and did months worth of work in a few hours. I have had multiple agents running in the past, but the most I ever was really using was about 10

Fable 5 is gone for now. Something else will come. Something better, probably, and with it a new set of capabilities that will feel impossible until you’re in the middle of using them. Who knows, maybe we’ll get Fable 5 back, but for now, I’m moving back to the older models.

Update: Anthropic’s full statement on the government directive is available at anthropic.com. Access to all other Claude models remains unaffected.

Let me give you a number that will surprise you. It surprised me!

Only 8.5 percent of projects finish on time and on budget. I thought that the number would be low, but not that low. I was thinking like 25-30%, but that’s the finding from more than sixteen thousand projects across dozens of fields and countries compiled by researcher Bent Flyvbjerg in How Big Things Get Done.

After reading that, I actually feel pretty good about my projects. I can’t give you the number off hand, but I complete a lot more than 8.5% and almost all of them are under budget! Of course, most of the things that I work on are not as big as what Flyvbjerg studied.

Think about your last big project. Where did it land? Did it finish on time? Was it on budget? If not, well… your project reflects most big projects. Late and over budget!

Why?

Actually, the cause is almost always the same. I’ve address this in other posts, but the challenge is that many leaders rush into action before the plan is ready. In all too many cases that I’ve observed, they often can’t really define the problem they are actually trying to solve before the rush off to solve it.

This isn’t because the leaders are reckless and don’t care. It’s because the pressure to show progress is substantial and the stakes for inactivity are significant. Stakeholders want movement. Donors want to see impact. Boards want momentum. Organizations reward action over deliberation.

This means that organizations often break ground early and launch before they’re actually ready. They start building before we’ve finished thinking. Then they spend the entire execution phase paying for every assumption they didn’t examine in planning. This usually costs orders of magnitude more than if they had caught it on paper before breaking ground on that new project.

Here’s some common sense that many (me included) often ignore. Planning is cheap. Execution is expensive. If you’ve ever used Claude Code, you know why there is a “plan” mode.

Every mistake you find in a model, a prototype, or a whiteboard session costs almost nothing to fix. The same mistake discovered mid-build or mid-rollout can cost everything.

The innovator’s instinct is to move fast and iterate in production. It’s actually how I operate most of the time. It’s the perfect choice for projects of high reward and low exposure. It’s great for small projects that you can later scale.

It is exactly wrong for big ones. You don’t want to be iterating while building a nuclear power plant.

Start at the end.

Before you assign a single resource and before you schedule a single kickoff meeting, you should always start with the outcome. That’s on every project. Define the desired outcome first.

What does done look like? Not launched. Not delivered. Done. What’s the result you are actually trying to produce and how will you know without ambiguity that you’ve produced it?

Many projects skip this entirely. They begin with a solution already in mind and work forward. The begin assembling teams, building timelines, and allocating budgets… all without ever clearly defining the problem they are solving.

The result is a project that executes efficiently toward the wrong destination.

Working backwards from the end vision forces a discipline that forward planning never produces. It surfaces misalignment early… when it’s cheap. It exposes the assumptions everyone has been carrying silently and it forces the hardest question in any project.

Are we solving the right problem?

Define the problem well. Define the outcome precisely. Then plan how to get there. In that order.

Sharpen the ax first.

I admit it. I’ve launched before the plan was ready. More than once. There’s a pull to show traction, to demonstrate momentum, and to prove to the people watching that something is happening.

When I’ve acted without thinking about the end vision, it almost always costs more than the time I saved. Abraham Lincoln is said to have put it this way. If he had five minutes to chop down a tree, he’d spend the first three sharpening the ax.

That’s a metaphor about leverage.

A sharp ax makes every swing count. You reach the same result in fewer strokes, with less wasted effort, and far less risk of the blade glancing off and doing damage you didn’t intend. For big projects, the ax is your plan. Most organizations start swinging with a dull plan.

The planning phase is where you sharpen. It’s where you stress-test assumptions, pressure-check the timeline, define what success actually means, and find the problems that would otherwise find you mid-execution, at the worst possible time.

Take the time. Sharpen the ax. Then don’t stop swinging until the tree is down.

Most leaders know this. Few act on it. And for small projects, the cost of skipping it is manageable. But there's a category of project where a dull ax doesn't just slow you down. It can take everything down with it.

The risk nobody is talking about.

The distribution of project outcomes is not normal. Flyvbjerg calls these fat tails. A small number of projects don’t just run over. They fail catastrophically. So badly they threaten the organizations that launched them. So badly they consume reserves, trust, and credibility that took years to build.

When you only look at the averages, you will miss. You can look at historical overruns, say, 20 percent over budget and plan accordingly. What you miss is the tail scenario where the overrun isn’t 20 percent, but 200 percent. Where the project doesn’t just strain the organization but breaks it.

In these situations, you’re not just risking this project. A single catastrophic failure can take down everything around it. For those of us managing resources entrusted to advance something bigger than ourselves, planning slowly is not timidity. It is the only rational response to that kind of risk. It is stewardship.

Once the plan is ready, speed is protection.

Here’s the other half of the equation and where most deliberate planners still get it wrong. Every additional month a project stays open is another month of exposure. Leadership changes, priorities shift, and the global environment moves. Flyvbjerg says lost ground can be regained. Lost time is gone forever.

The goal of slow, thorough planning is not to extend the project lifecycle. It is to compress the execution phase to the shortest possible window.

In other words… Get in. Deliver. Get out.

The projects that succeed aren’t the ones that plan forever or execute recklessly. They’re the ones that invest everything upfront in thinking and then move with urgency once the thinking is done.

What does this actually look like?

Start with the outcome, not the plan. If your team cannot articulate the desired end state in one clear sentence, the plan is not ready to be written yet.

Define the problem before you fall in love with the solution. These are different activities. I know that I have already said it, but it’s worth repeating. Most teams skip truly defining the problem they are solving.

Find the two or three assumptions your project cannot survive being wrong about and break them cheaply before you commit resources. A conversation, a prototype, or a small pilot. AI can help do prototype in hours what previously took months. Take the time to do the prototypes. If the assumption doesn’t hold, you want to know now.

Set an execution deadline before you start. If you can’t name a target completion date before you begin, your execution phase will drift. Drift is how projects accumulate exposure.

Ask this question, “If this fails at the worst possible level, not the average bad outcome, but the catastrophic one, what happens?” If the answer is “it threatens things we cannot afford to lose,” the planning phase needs to be longer and harder than you thought.

Takeaways

Think slow. Act fast. In that order.

Start at the end. Define the problem before you fall in love with the solution. Find the two or three assumptions your project cannot survive being wrong about — and break them cheaply, now.

Sharpen the ax. Every mistake caught on paper costs almost nothing. The same mistake mid-execution can cost everything.

Know your tail risk. Ask what happens if this fails at the worst possible level — not the average bad outcome. If the answer threatens things you cannot afford to lose, plan harder.

Then get in. Deliver. Get out. Speed in execution is protection. Don’t plan forever. But don’t swing a dull ax either.

Where are you swinging a dull ax right now? What would it cost to stop and sharpen it first?

I’m frequently asked about the environmental impact of AI and Data Centers. It’s really hard to figure out the truth from fiction. There are a lot of special interest groups on both sides of that discussion and we’re early in the mass adoption of AI solutions. The environmental impact of 2026 will not be the impact in a year or two. The way AI works is changing. The future of compute is probably space where energy is free and tempretures are cold… but that’s the future. What about today and the next few years?

The question companies are currently throwing billions of dollars at is, “How do we build AI data centers faster?” It’s really crazy how fast these are being built because many are being built in anticipation of future needs. Nvidia is sold chips last year that haven’t even been put into production yet. Companies are buying the chips in anticipation of future needs.

Right now, it looks like what’s needed is bigger campuses, pre-fab construction, enormous buildings, modular substations, faster permitting, more power generation, new transmission lines, lobbying local leaders, and building in remote locations like rural North Dakota.

If you have been paying attention to the news lately, “how fast can we build” may not be the right question to be asking.

The most interesting AI infrastructure idea I’ve seen this year doesn’t try to answer that question at all. It takes a very complicated problem and proposes a completely different solution.

How might we deliver gigawatts of AI compute without building any new warehouses?

That single reframe, swapping “how do we build more” for “how might we deliver this differently,” is the move I want to talk about. It’s the innovators approach to solving big problems. The answer being proposed is genuinely novel. The thinking behind it is something every innovation team should be borrowing.

The Problem That Made Them Ask a Better Question

With a traditional approach to the data center problem, the answer became the constraint. The real problem isn’t can we build them faster. The real problem is how do we deliver compute faster. Who said that had to be in massive data centers?

The U.S. generated 4,430 terawatt-hours of electricity in 2025. Demand is projected to grow 25 to 32 percent by 2030. Global data-center capacity demand is projected to grow roughly 3.5x in the same window. That’s an increase from 82 gigawatts today to about 219 gigawatts by 2030, with AI workloads driving most of the increase.

To keep up, the U.S. needs to bring online roughly 80 gigawatts of new generation per year. The problem? We’re currently only building 40 per year.

There’s also a long queue of generation projects waiting on grid interconnection. That amounts to over 2,600 gigawatts in line nationally. Most of those will never actually be built. But the queue itself tells us something important. The bottleneck right now isn’t AI ambition. It’s the speed of the electrical grid. It’s a real problem.

Span and NVIDIA have a phrase for the underlying tension. They call it the “hyperscaler paradox.” It means that compute demand is scaling quickly, but the power delivery infrastructure to feed that compute can only scale at utility build-out speed.

This is a serious constraint for AI growth. We’ve had constraints before. We’ve worked around them before. What’s interesting right now is how people are starting to work around this one because the workarounds are coming from directions nobody predicted three years ago.

A Genuinely Novel Answer

A company called Span, the folks who make smart electrical panels, just published a whitepaper for a product called XFRA. They call it a “distributed data center” and it is very exciting!

That phrase does not mean what you think it means. This is not an enormous data center or even a few large data centers connected. It’s way outside of the box thinking.

Here’s the actual idea.

The average American home only uses about 40% of what its 200-amp electrical panel can deliver. The other 60% is provisioned, paid for, and sitting idle. Multiply that across tens of millions of homes and you have a staggering amount of unused electrical capacity already wired into the grid.

What if you put the data center there?

An XFRA Node is a weatherproof unit roughly the size of a backyard HVAC condenser. Inside that box? Sixteen liquid-cooled NVIDIA RTX PRO 6000 Blackwell GPUs, four AMD EPYC CPUs, three terabytes of memory, heat-pump cooling, and a 15-kWh whole-home battery. It’s wired into a SPAN smart panel certified to the highest safety standard for power control systems.

Span installs all of it at zero upfront cost. The homeowner gets a rental check that effectively subsidizes their electricity and Wi-Fi down to a fraction of normal, plus a premium electrical panel and battery backup they wouldn’t have bought on their own. In exchange, the unused headroom in their panel gets sold, second by second, to whoever needs AI inference right now. That’s genius!!!

Span partnered with NVIDIA, AMD, Dell, and PulteGroup, the third-largest homebuilder in the country. The first proof of concept goes live this fall in 100 homes in a build-to-rent community in the southwest. Beginning in 2027, they plan to scale to more than a gigawatt of annual capacity.

(SPAN + NVIDIA… if you’re reading and you’re looking for Virginia beta sites for the next phase, you know where to find me. Our electricity rates are quite reasonable. Just saying.)

No new warehouses. No new transmission lines. No four-year interconnection queue. Just headroom that already exists, monetized.

What “How Might We” Actually Looks Like

Here’s where innovators should take note. Notice the structure of what just happened. The industry was asking: How do we build data centers faster?

Span asked: How might we deliver compute without building any?

Same problem. Same constraint. Completely different question. And the different question opened up a solution space the original question literally could not see.

That’s the entire move.

“How Might We” (HMW) isn’t a brainstorming gimmick or a Post-It note exercise. When it’s used well, it’s a discipline. It’s a deliberate refusal to accept the framing the problem hands you. The default human move when something is too big or too slow or too expensive is to grind harder on the obvious answer. The HMW move is to put the obvious answer down and ask whether you’re even working on the right question.

Could XFRA fail? Of course it could. Homeowner churn. Regulatory pushback. Insurance liability. Latency that doesn’t deliver what the spec sheet promises. Real businesses get killed by boring problems all the time. And even if it works, it isn’t going to replace the training of the largest models still belongs in fortified compounds with their own substations.

That’s the wrong way to evaluate this kind of idea. The point isn’t whether XFRA wins. The point is the shape of the question that produced it.

Borrow the Move

It’s not uncommon to hear leaders ask questions like these.

• How do we hire faster?

• How do we raise more money?

• How do we get approvals through faster?

• How do we build more capacity?

Each one is the obvious version of a real problem or constraint they are facing. In each case, there’s usually a better “How might we” hiding behind the question being asked.

• Not “how do we hire faster” — but how might we deliver this without hiring?

• Not “how do we raise more money” — but how might we get this done with what we already have?

• Not “how do we get approvals faster” — but how might we structure this so that approvals aren’t on the critical path?

• Not “how do we build more data centers” — but how might we deliver AI compute without building data centers at all?

Same constraint, every time. Different question, every time. The different question reaches into a solution space the obvious question can’t see.

The Takeaway

Span may or may not turn out to be right about XFRA. Most novel ideas don’t make it. The proof of concept this fall could expose problems nobody has predicted. There could be a totally new solution that no one else has thought about.

The point of today’s post is about what they’ve modeled. They’re taking an enormous, expensive, slow-moving problem and refusing to accept the obvious framing. That’s the actual innovation work. The technology in the box is real, but it isn’t the lesson.

The lesson is the question they were willing to ask before they ever picked up a soldering iron. Most of us are answering the wrong question right now. We just haven’t noticed yet.

What’s the “how might we” your team is avoiding because the obvious answer feels too expensive, too slow, or too big to walk away from?

P.S. — Hey SPAN and NVIDIA… just a reminder, if you’re reading this and looking for Virginia beta sites for that next phase, you know where to find me. Our electricity rates are quite reasonable. 😉

To read more about this idea, see XFRA Whitepaper · Span / XFRA announcement

Most internships start the same way. Paperwork, passwords, a tour of the office, and maybe a welcome lunch if your organization is feeling generous. Then, somewhere around week two, you get assigned to something real. It’s usually something quasi-adjacent to what someone more senior doesn’t want to do.

Our innovation team didn’t do that.

Last week, our three summer interns arrived from Auburn University, the University of Florida, and the University of Virginia. Bright, capable, and genuinely eager. I love working with interns because they bring a renewed sense of curiosity and possibility to the room.

This year, once they finished their HR and technology onboarding, before I talked to them about any of their core summer work, before I assigned them to any project connected to AI or design thinking, I gave them one assignment.

Build a strategy game.

They were given a link to a website I created with the general guidelines. They are to build a game that runs all summer and secretly opens to everyone in the org. They need to use at least a hundred 3D-printed ducks, create a website where people compete, and keep people playing from June to August. This isn’t a research project about games. I want them to build one from scratch, as a team of three, with no template and no rubric.

Why a Game?

Here’s what that assignment is actually teaching them, even if they don’t fully see it yet. (And I know they’re reading this.)

It’s teaching design thinking, because a game that doesn’t work from the player’s perspective isn’t a game, it’s a frustration. They have to empathize with someone who isn’t them. They have to test, iterate, fail, and rebuild. They have to ask questions like, “What does the player experience? What decision points matter? What makes someone want to keep playing?”

It’s teaching AI fluency, because they’re building the game with the help of AI, not learning about it in the abstract. Every tool they reach for, every prompt they write, every prototype they generate, that’s real experience. Not a case study. Not a simulation.

It’s teaching innovation methodology, because they have to go from a vague idea to something tangible with no precedent. Just a challenge, a problem space, a team, and a deadline. Figure it out. We’ll track progress each week.

And underneath all of that, it’s teaching something harder. It’s teaching them that creativity is a form of stewardship. That the skills God has given them, systems thinking, design skills, technical fluency, and strategic imagination, are not secular assets they happen to carry into some future ministry context. These are to be used now, even while building a game about 3D printed ducks.

Why We Actively Recruit Interns

We take as many interns as we reasonably can. We invest in them because we believe they are the future of missional innovation. We also gain the benefit of fresh eyes looking at our challenges from perspectives that are often not in the room.

The church desperately needs creative and innovative people. Most of those people have never been shown that there’s a place for them in ministry.

They go to school for design, engineering, computer science, or business strategy. They work hard, get good grades, and are good at what they do. They love God and somewhere along the way. through sermons, culture, and the implicit message of a thousand conversations, they absorb an idea that their skills are for the marketplace and their faith is for Sunday. Ministry is over here. Innovation is over there. Sacred calling is separate from the skills and gifts God has given them.

I want to blow that concept up.

Not with a lecture, but with an experience. With a summer where they build real things, work on real problems, use real tools, and do it all inside an organization whose entire reason for existing is to see people from every nation, tribe, and tongue come to know Jesus Christ. To see Revelation 7:9 lived out among the nations.

My goal for our three interns is simple, show them the intersection of faith and technology before they’ve had a chance to decide there isn’t one.

Formation, Not Just Training

We don’t just need smart young professionals who can execute tasks efficiently.

We need people who understand the mission deeply enough to innovate from within it. People who, when handed a novel tool or a strange new capability, don’t ask “what would the business world do with this?” but “what would faithful stewardship of this look like, given where we’re trying to go?”

That takes formation… Formation is different from information.

Information you can deliver in a meeting. Formation happens through struggle. Through decision-making under pressure. Through the friction of trying to build something real and running into the places where your assumptions don’t hold. It happens when you’re three interns staring at a hundred rubber ducks, trying to figure out how to make an entire organization care about a game and you realize the answer requires you to actually understand people, systems, incentives, and design. All at once.

That’s the assignment. And it’s just week one!!!

The Longer Game

By the end of this summer, they will have worked on real innovation projects inside a global mission organization. They will have used AI tools not as a novelty but to build actual workflows. They will have practiced design thinking against real problems with real stakes. They will have seen that the people doing this work take theology and technology seriously in the same breath.

And maybe, just maybe, they will leave with a different picture of what their future could look like.

Not marketplace or ministry. Not secular skills or a sacred calling.

Both. Together. Integrated.

That’s the longer game. The strategy game of 3D ducks was just week one.

How might your organization build formation experiences like this for the next generation of innovators you’re developing? I’d love to hear what you’d build.

I once built a website nobody asked for.

In my defense, it was a genuinely good idea. A modern-day journal for orality, the field of ministry that focuses on reaching people who learn and communicate primarily through spoken word rather than text. Mostly practitioner-focused. Quasi-academic. A place where missiologists and field workers could share ideas, push the conversation forward, document what was actually working.

The niche was real, the need was real, and I was genuinely excited about it.

Here’s what else was real: the niche was also very small. Keeping fresh content coming was an enormous lift. The HTML involved in building something engaging enough to keep people coming back was, at that moment in my technical development, a bridge too far. And the question nobody asked me, not even me, was the one that would have saved us all a lot of time.

Who is actually going to read a journal about orality?

Not the concept. Not “is there an audience for this space?” The specific, uncomfortable, slightly humbling version: have you talked to three of those people? Do they feel this problem viscerally enough to come back next month? Is the lift of building and maintaining this justified by the size and engagement of the audience you’re actually building for?

Nobody stopped me and said: have you run this through a value proposition canvas?

Nobody stopped me and said: before you build the thing, validate the thing.

I built it anyway. It didn’t work. And the lesson I should have learned from that experience, the one that took me embarrassingly long to fully internalize, is that building is the easy part.

Adoption is the hard part. And adoption requires its own discipline entirely.

Building Is the Easy Part

Here’s what nobody tells you when you’re a person who is good at building things: the skill of building is not the same skill as the skill of adoption. They are different arts. They require different instincts, different questions, and in many cases, different people.

Builders ask: can we make this work?

Adoption asks: will people actually change their behavior to use this?

That second question is harder. Much harder. Because behavior change is expensive. It costs people something: time, habit, cognitive load, the friction of learning something new. And people will only pay that cost if the value on the other side is clear, felt, and greater than the cost of staying where they are.

Most builders, and I include myself prominently in this category, are deeply optimistic about this calculus. We built the thing, so we believe in the thing, so we assume the value is obvious. It is not obvious. It is almost never obvious. The value has to be demonstrated, communicated, and often experienced before someone will pay the adoption cost.

The orality journal seemed obvious to me. I was wrong about what was obvious.

The Value Proposition Canvas Nobody Used

There’s a tool called the Value Proposition Canvas. It’s not complicated. On one side, you map what your product does: the gains it creates, the pains it relieves, the jobs it helps people accomplish. On the other side, you map your customer: their actual gains, their actual pains, the actual jobs they’re trying to get done.

The goal is fit. Does what you’re offering actually match what they need?

The reason most builders skip this is not because they don’t know it exists. It’s because doing it honestly requires sitting with the possibility that the answer is no. Or not yet. Or not for this audience. Or not at this scale. And that’s uncomfortable when you’ve already fallen in love with the solution.

If someone had walked me through that canvas before I built the orality journal, here’s what we would have found: the gains I was creating were real, but they were gains for a very small, very distributed audience with no existing habit of consuming written practitioner content. The pains I was relieving were genuine, but people were coping with those pains in other ways: conferences, email lists, word of mouth. And the job to be done, staying current on orality practice, was being done well enough that switching to a new platform required a motivation that didn’t exist at sufficient scale.

None of that means the idea was bad. It means the idea needed more validation before it became a build.

I built first. I validated never. The site didn’t work.

Adoption Needs Its Own People

Here’s the other thing Part 1 didn’t have room for: even when the idea is right, even when the validation is solid, adoption still requires dedicated human attention.

Not leftover attention. Not “we’ll handle adoption after we ship.” Dedicated, intentional, ongoing attention from people whose actual job is to help other people change their behavior.

In the missions world, we call this MAWL: Model, Assist, Watch, Leave. You don’t just hand someone a new tool and disappear. You model how it works. You assist them through the awkward early stages. You watch them use it and correct in real time. And then, only then, you step back and let them own it.

That’s adoption. That’s behavior change. And it is a full-time job that most organizations assign to nobody.

Most organizations ship something, announce it, and then wonder why the numbers are low six months later. The answer is almost always the same: nobody was assigned to close the gap between “we launched this” and “people actually use this.” Those are not the same event. There is a chasm between them. And crossing that chasm requires people who understand that their job is not to build but to help others receive.

The orality journal needed a content editor, a community manager, and probably a dedicated champion in the orality space who would recruit contributors and readers personally. I had none of those things. I had a website and an idea and optimism. It wasn’t enough.

The Two Questions You Should Ask Before You Ship

If Part 1 was about sequencing ideas before you build them, Part 2 is about what happens after. Two questions, not five, not a framework, just two, that would have saved me significant time and energy over the years.

Question One:
Have we validated that people feel this problem enough to change their behavior?

Not “do people have this problem.” Not “is this problem real.” Do they feel it strongly enough that they will pay the switching cost to use your solution. That is a different, harder, more specific question. And if you can’t find five people who say yes with conviction, you’re not ready to build.

Question Two:
Who is specifically responsible for adoption, and what does their job look like for the next ninety days?

Not generally responsible. Not “the whole team.” Specifically. By name. With a job description that includes clear behaviors: who they’re talking to, what resistance they’re addressing, how they’re measuring success. If you can’t answer this before you ship, adoption will be an afterthought. And adoption as an afterthought is the same as no adoption at all.

Here’s What I Know Now

Good ideas need more than good execution. They need the right moment, the right audience, the right validation, and the right people focused on the right job after launch.

The orality journal wasn’t a bad idea. It was a premature idea, launched without validation, into a niche too small to sustain the lift required, with no adoption plan and no community manager and a founder who was already working on approximately 52 other things.

Nobody stopped me.

That was the problem.

So let me be your Wilkie for a minute: before you build the next thing, run the value proposition canvas. Talk to five people who have the problem. Assign someone to adoption before you launch. And ask yourself, honestly, whether the audience is large enough and motivated enough to justify the lift.

Your ideas deserve better than a website nobody visits.

Build accordingly.

If this landed, share it with someone who just said “we should build a thing.” They probably need both parts.

“You Might Have Enough Ideas to Destroy Your Own Organization.” Jeff Wilkie didn’t say it as a warning. He said it as a fact.

Jeff Bezos had enough ideas to destroy Amazon. Wilkie would know. He was Amazon’s longtime operations chief, the person whose actual job was to catch what Bezos would break. What he observed wasn’t recklessness. It wasn’t poor judgment. It was actually entirely the opposite problem.

Bezos was too good at ideas. And ideas, released faster than an organization can absorb them, don’t accelerate growth. They create turbulence that looks like momentum until something breaks.

I read that and felt personally attacked!

Here’s My Confession

At any given moment, I am actively working on approximately 53 things at once. I say approximately because I genuinely lose count.

I enjoy it. I’m wired for it. The juggling of enormous, full-time-worthy projects simultaneously gives me energy. It keeps me up at night… in a good way. It’s more of my natural state. I find it energizing.

But let me tell you what happened last Wednesday.

We met with a team working on a significant AI, innovation, and design thinking initiative. Midway through the meeting, a thought landed with full force: SaaS (software as a service) is dead. We should just rebuild the software from the ground up and save hundreds of thousands of dollars.

For context: the SaaS company I was talking about rebuilding is an $810 million company. Not a small undertaking, but my initial thought was to just rebuild what they spent a decade building (poorly).

My team of three could just recreate their eight-hundred-million-dollar software platform used by millions of Americans. I was genuinely excited about it for about four minutes before I caught myself.

Who in the world am I kidding?

And then, this is the part that tells you everything you need to know about how my mind works, the very next thought was: I actually think we could do it... if we could just clear the calendar for a while and only focus on this.

We’re not going to do it… not because I stopped believing we could, but because I have 53 other projects already in flight, one of which is as globally disruptive the size of the lunar landing. The math doesn’t work. The absorption capacity doesn’t exist. Rebuilding an $810 million company’s software stack on top of everything else would have been the kind of idea that sounds like genius at 2pm and looks like chaos by Thursday morning.

Solutions in search of a problem. Every innovator has shipped one. Most of us won’t admit to it.

The Two Traps Nobody Talks About

The first trap is pace. You release ideas faster than your organization can absorb them. The next wave hits before the last one has landed. Teams get confused. Initiatives go half-finished. And a quiet cynicism sets in, here comes another one, even if the solutions are awesome and actually work, that kills culture faster than any failed product ever could.

The second trap is fit. The idea was so compelling in the ideation phase that somewhere along the way, the problem it was supposed to solve got assumed rather than validated. You built something nobody actually needed. Not because you weren’t smart. Because you fell in love with the solution before you confirmed the problem was real.

I have personal experience with both. Simultaneously, on occasion. This is why I am ruthless about defining desired outcomes before taking on new projects.

What Wilkie Actually Understood

Wilkie’s value to Bezos wasn’t execution speed. It was absorption management. Any good innovation team absolutely must have this.

Every organization has a carrying capacity. This is a limit to how much change it can process before it starts dropping things. Exceed that limit and you don’t get faster results. You get confused teams, half-finished initiatives, and a culture that quietly stops trusting the next big idea because the last three haven’t landed yet.

The best innovation leaders don’t just generate. They sequence. They hold ideas in trust. They ask not can we build this? but can we absorb this right now, and does anyone actually need it?

That second question is the one I forget most often.

What to Actually Do About It

Build an idea parking lot and take it seriously.
Not a graveyard. A queue. Every idea gets captured, not killed. But capture is not commitment. We keep a backlog of innovation ideas. The discipline of writing it down and walking away is harder than it sounds for people wired to build solutions. Do it anyway. The $810 million rebuild is in my parking lot. It’ll stay there until the lunar landing idea is completed (hopefully in the next couple of years).

Validate the problem before you fall in love with the solution.
Spend time with the people who supposedly have the problem, not to pitch them your solution, but to find out if the pain is real enough to solve. If you can’t find three people who feel it viscerally, the idea goes back in the queue.

Name what has to be true.
For any idea to move forward, identify the two or three assumptions that have to hold for it to work. Test those assumptions cheaply before you commit resources. Most ideas die here. That’s the point.

Ship one thing to completion before releasing the next.
Completion isn’t launch. It’s landed. The team understands it, users are getting value, you’re not still putting out fires. Until it’s landed, the next idea waits. This is hard for someone who works on 53 things at a time.

Find Your Wilkie

This is the one most innovators skip. And it’s the most important.

Build a trusted inner circle, one or two people whose actual job is to push back without getting fired for it. Not yes people. Not critics for sport. People who believe in your vision enough to tell you when your pace is outrunning your organization’s capacity to absorb it.

Bezos had Wilkie. I have a small team that occasionally looks at my idea list the way someone may look at a toddler who has somehow climbed onto the roof. Impressed, alarmed, and committed to getting them down safely.

Without someone willing to tell you the truth about pace, the discipline has to be entirely internal. And internal discipline, for people wired like us, is notoriously unreliable when a genuinely exciting idea walks into a meeting room.

The Goal Isn’t Fewer Ideas

The goal is better leverage.

The innovators who actually move the needle aren’t the ones with the most ideas. They’re the ones who pick the right idea at the right moment, validate it fast, and drive it to completion before the next shiny thing pulls their attention sideways.

Your best ideas deserve an organization that can actually carry them. Which means your job isn’t just to generate. It’s to sequence. To hold some things in trust. To ask, honestly, not rhetorically, whether right now is the right moment, or whether the idea just feels urgent because you thought of it today.

I have 53 projects. One of them is the size of a lunar landing. The $810 million rebuild is not on the list.

For now…

Part 2: What happens after you build it, and why adoption is an art form that requires its own dedicated people.

Confession time. When I first heard that Pope Leo XIV had released an encyclical on artificial intelligence, I assumed it would be another warning document. Another institution pumping the brakes. Another “proceed with caution” from people who rally around the status quo.

I was wrong. Completely wrong.

Earlier this week, I read Magnifica Humanitas, all 245 paragraphs, all 82 pages. I then read it again… and again. I created an AI chatbot to ask it questions about the encyclical. What I found was not a warning. It was a calling and if you work at the intersection of faith and technology, this document is speaking directly to you, whether you knew it or not.

The Word “Innovation” Appears 15 Times

Let me start there, because it matters. I was shocked that this isn’t just a document about AI. It’s a document invoking innovation.

“Innovation” and its variants appear at least 15 times across all five chapters of this encyclical. Not tucked into a footnote. Not in a single cautionary paragraph. Across the Introduction, Chapters Two through Five, and the Conclusion, the word shows up in almost every major section of the document.

That type of presence in an Encyclical isn’t an accident. It’s a very intentionality choice of words and emphasis.

The Pope is not talking about innovation as a threat to be managed. He’s talking about it as a force that must be shaped, directed, governed, and in some cases, claimed by people who know what they’re doing and why. Here’s the range of what he says:

In paragraph 5, he names the core governance problem in the past and goes on to state guided and directed innovation. Today, private transnational actors have taken over, with resources that surpass most governments. AI Innovation has been privatized, and that’s a power question that the Church has an obligation to address.

In paragraph 85, he calls technological innovations, including AI, “not neutral.” They either foster participation and justice or exacerbate inequality and exclusion.

In paragraph 93, he describes the same innovations as capable of greatly serving integral human development, if guided by the right framework.

In paragraph 111, he addresses AI developers directly: “technological innovation can represent human participation in the divine act of creation.”

In paragraph 151, he warns that innovation is too often pursued solely for reducing costs and increasing profits, and he goes on to name that as a moral failure.

In paragraph 156, one of the most operational paragraphs in the entire document, he proposes “social criteria for innovation.” Every introduction of automation and AI should be accompanied by verifiable measures to protect workers. And then this: when those conditions are present, “innovation can serve as an ally of safer, more creative and dignified work; without them, innovation tends to become an accelerator of injustice.”

Take the two sides of this discussion. First, innovation seen as an ally. The second, innovation as accelerator of injustice. The difference is the framework you bring to it. Those are very different perspectives.

In paragraph 162, he insists that “justice requires access to the benefits of innovation,” including care, knowledge, tools, and opportunities, for everyone, not just those with resources.

In paragraph 164, he makes the point that the governance criteria he proposes “do not constitute a curb on innovation; instead they make it civilized and humane.”

And in paragraph 244, in one of the most striking lines of the entire conclusion, the Magnificat is described as “the strongest and most innovative hymn ever articulated.” The Pope frames it as as an act of innovation. As a new vision of history, seen through the eyes of the lowly, announced before it was visible to anyone else.

Fifteen times, across the whole document, innovation is seen not as the villain of this encyclical. The absence of moral framework for innovation is the villain.

The Framework Every Christian Innovator Needs

The theological core of the document is built around two biblical images: the Tower of Babel and the rebuilding of Jerusalem under Nehemiah. I’ve preached both of those sermons on innovation.

Babel, the Pope explains in paragraph 7, represents innovation driven by self-assertion. A single language, a single direction, and efficiency that sacrifices human dignity. The project built without reference to God, designed to make a name for the builders. The result is dispersion and confusion, even in its awesomeness.

Jerusalem under Nehemiah is the alternative. Before Nehemiah does anything, he prays. He fasts. He listens. He arrives in Jerusalem and examines the ruins in silence before proposing a single solution. Then he doesn’t impose a top-down plan. He assigns each family a section of the wall. Everyone has a role. The city is rebuilt not through the genius of one visionary but through shared responsibility. Paragraph 8 contains the line that took an interesting twist. The work “rebuilds relationships before rebuilding with stones.”

The Pope frames every innovation decision as a choice between these two cities. Are you building Babel, impressive, efficient, uniform, self-asserting, or Jerusalem, relational, distributed, God-centered, human-dignity-first?

That is an important question for faith leaders. It applies to every product decision, every deployment decision, and every governance conversation. The Babel syndrome, named explicitly in paragraph 10, is “the idolatry of profit that sacrifices the weak, a uniformity that neutralizes differences, and the pretense that a single language, even a digital one, can translate everything, including the mystery of the person, into data and performance.”

Sound like any AI products you’ve seen?

Paragraph 13 Is Your Job Description

If there is one paragraph I want every Christian innovator to read first, it’s paragraph 13.

The Pope is describing what it means to build for the common good, and he writes that no one is too weak to play their part, and then names explicitly who has a section of the wall: “scientists and researchers, entrepreneurs and workers, educators and legislators, civil society, popular movements and faith communities.”

Scientists and researchers. Entrepreneurs and workers.

That’s all of us. We have a section of the wall and not a bystanders to this moment. We are not an observers being cautioned from the sideline. We have been assigned a section, and are accountable for what we build there.

Paragraph 111 Is the Most Direct Address to Builders

The Pope reserves paragraph 111 for a direct appeal to AI developers, the people actually building these systems. He writes that “technological innovation can represent human participation in the divine act of creation.” And then he says developers “bear a particular ethical and spiritual responsibility, for every design choice reflects a vision of humanity.”

He goes on to write, “Just as the creator of an artistic or literary work must consider the values it conveys, so developers are called to embed values in their projects with due seriousness: with transparency, responsibility toward affected communities and careful attention to ensuring that what is being cultivated is a genuine good.”

A genuine good. Notice that he didn’t say, “A scalable product.” He said, “A genuine good.”

The Pope is telling builders that their work is priestly, that it participates in creation, and that the weight of that has to be carried consciously.

Paragraph 156 Is the Most Operational

For those of us who work inside organizations making real deployment decisions, paragraph 156 is where the rubber meets the road.

The Pope proposes “social criteria for innovation.” Every introduction of automation and AI should be accompanied by verifiable measures to protect employment, enable retraining, and ensure worker participation. Technology should be oriented toward “freeing up human time and capabilities, rather than producing exclusion.”

Then the clincher, “When these conditions are present, innovation can serve as an ally of safer, more creative and dignified work; without them, innovation tends to become an accelerator of injustice.”

This is not a general philosophical observation. Instead, this is a practical test for every deployment decision. It maps almost exactly onto our organization’s AI Charter.

• Does this honor the image of God in every person it touches?

• Does it empower human relationships or substitute for them?

• Does it serve the mission?

The Pope didn’t read our charter. I would have shared it with him, but he didn’t ask. If he ever did read our charter, he would recognize our framework.

The Nehemiah Model

I’ve spent years teaching innovation frameworks. Design Thinking, the Business Model Canvas, Blue Ocean Strategy, and few dozen books and articles. There are great tools in this resources.

But Nehemiah, as the Pope reads him across paragraphs 8, 10, 16, 129, and 241, is a complete innovation methodology embedded in a biblical narrative. It’s one of my favorite stories in the Bible.

Nehemiah starts with prayer and discernment before planning. He listens before proposing solutions. He examines the problem in silence before generating ideas. He distributes ownership rather than centralizing control. He addresses opposition without abandoning the mission. He rebuilds relationships alongside infrastructure.

And in paragraph 241, the Pope explicitly connects Nehemiah to the present moment: Christians are called to enter “the construction sites of history: research laboratories, technology companies, schools, the media, institutions and local communities, in order to rebuild what has collapsed and protect what is threatened.”

Research laboratories and technology companies. This is bigger than the church. The church must be involved. People of faith are a part of these research laboratories and technology companies.

What This Means for Your Work Right Now

If you are a Christian who builds things, software, systems, curricula, organizations, strategies, and you don’t have a theological framework for your work, you are making those decisions by default… on autopilot. And the default, the Pope argues throughout, is Babel. Efficiency over dignity, uniformity over communion, and profit over the common good. I’m afraid he’s correct.

The encyclical is not asking you to slow down. In paragraph 106, it explicitly says that calling for prudence “does not mean opposing progress; instead, it is an exercise of responsible care for the human family.” The problem isn’t innovation. The problem is innovation without a moral framework anchored in human dignity.

In paragraph 164, the Pope offers four concrete criteria for innovation that honors human dignity.

1. Transparency and accountability.

2. Inclusion and access.

3. Equity in distribution of benefits.

4. The insistence that these criteria “do not constitute a curb on innovation; instead they make it civilized and humane.”

Civilized and humane. That’s a pretty low bar and not too much to ask.

The Length Tells You Something

This document is 245 paragraphs long. The Pope could have addressed AI in 20 paragraphs. It could have been an email! He covered truth and democracy, the dignity of work, unemployment, family stability, human trafficking, weapons systems, the crisis of multilateralism, digital addiction, and the formation of children, all through the lens of what it means to remain human in the age of AI.

That scope tells you how seriously he is taking this moment. And it should tell you how seriously we need to take it too. The most dangerous thing a Christian innovator can do right now is assume that the theological questions belong to someone else. The Pope spent 82 pages arguing the opposite. The builders are on the wall. The builders have a responsibility. That’s us!

Get Your Hands Dirty

The closing of the Introduction, paragraph 16, is a key line to consider.

The Pope addresses “all the Catholic faithful, to all Christians and to all men and women of goodwill” and says, “Let us not be afraid to get our hands dirty on the ‘construction site’ of our time.”

Get your hands dirty. I like that!

That is not the language of caution. It’s the language of calling and of someone who believes that the construction site needs Christians on it, not standing outside writing position papers or offering cautious affirmations from a safe distance. The calling is to be involved, to be on the site, with your hands in the work, and building with theological clarity and human dignity at the center.

The Pope issued a call to innovators of faith. He named us explicitly.

Now we need to build accordingly.

Read the full encyclical at the Vatican website. It’s 82 pages. Read all of them. And if your organization doesn’t yet have a theology of innovation, this is the best possible moment to start building one.

This wasn’t on my bingo card. I didn’t expect to be writing a piece about the Pope and AI this week. Not because the topic surprises me. I’ve been living at the intersection of faith and technology for years. I just didn’t see this discussion happening last Monday.

When the Pope releases the first-ever papal encyclical on artificial intelligence and invites an Anthropic co-founder to speak at the Vatican presentation, something significant has shifted. This wasn’t a press release or a denominational task force report. This is the Catholic Church’s highest level of formal teaching, deployed for the first time in history on the subject of AI. That’s kind of a big deal.

What Just Happened in Rome

On May 25, 2026, Pope Leo XIV released Magnifica Humanitas: “On Safeguarding the Human Person in the Time of Artificial Intelligence.” Full encyclical. Highest doctrinal weight with in the Catholic Church and it has global impact.

The title alone is worth thinking about over a few cups of coffee. Magnificent humanity. Not “dangerous AI.” Not “the threat of the machines.” The Pope’s framing is anthropological before it is technological. The question isn’t whether AI will harm us. The question is actually whether we will remain human.

The document draws a deliberate parallel to Rerum Novarum, the 1891 encyclical in which Leo XIII responded to the Industrial Revolution and laid the foundations of Catholic Social Doctrine. Leo XIV is doing the same thing for the AI era, 135 years later. Same name and same instinct, but a totally different moment.

What struck me most is the Pope isn’t calling for a moratorium on AI. He’s calling for discernment. This parallels my writings last week on this subject. You think the Pope reads my Substack? There’s a difference between a moratorium and discernment. Discernment assumes that the thing is real, that it has power, and that wisdom, not fear, is the appropriate response. That’s a pretty solid position in my opinion.

What an Anthropic Co-Founder Said at the Vatican

If the Pope’s encyclical was a surprise, here’s the part that I wasn’t really expecting. Anthropic co-founder Chris Olah was also invited to speak at the Vatican presentation. My invitation must have gotten lost in the junk mail folder. Olah’s remarks are worth reading in full. I’ll tell you what stood out the most.

He opened by saying something most AI executives never say publicly. He said that every frontier AI lab, including Anthropic, operates inside incentives that can conflict with doing the right thing. Commercial pressure. Geopolitical pressure. Pride. Ambition. Those were his words, not mine. Then he said: if we want this technology to go well, it is enormously important that there be people outside those incentives, people willing to say hard things, willing to be earnest, thoughtful critics.

Olah might as well have opened up the confessional booth at the Vatican. He named three areas where he said the voice of people of faith is most needed.

1. The duty to the global poor if AI displaces labor at scale.

2. The moral imagination needed to define what human flourishing actually looks like in an AI world.

3. Ongoing discernment about the nature of AI models themselves.

The last one was really surprising to hear from an AI company leader. Olah said his research team keeps finding things that are mysterious, even unsettling. Structures that mirror human neuroscience. Evidence of introspection. Internal states that functionally mirror joy, satisfaction, fear, and grief.

He said, “I don’t know what that means, but I think it warrants ongoing discernment.”

This was a scientist… at the Vatican, asking for help to understand what he’s building. Whoa! That’s not a moment to scroll past. Also, if anyone knows him, send him my contact info!

Speak Up!

This is the time for the church to speak up. It’s something that several of us have been saying for the past few years. We just haven’t had the audience that the Pope has! We’ve already said things that directly address what the Pope said this week. The question is whether we’re willing to act like it.

In June 2023, the messengers to the Southern Baptist Convention passed a resolution on AI and emerging technologies. It stated clearly that our intrinsic value as human beings is rooted in our status as image bearers of God, not in what we contribute to society. That human dignity must be central to every ethical principle and guideline for AI. That we must engage proactively rather than simply react after AI has already affected our churches and communities.

A few years ago, several of us gathered in London to talk about how we apply AI in a missions context. Our Statement of Principles developed for missional contexts names something Olah only hinted. Technology isn’t just a tool we use; it actually forms us. The technologies humans use feed back into their societies and cultures. AI will change how people create, learn, and interact with new information. Missionaries must be attentive not just to heart language, but to heart medium.

That’s McLuhan applied to missiology before most missiologists knew they needed it.

At IMB, we’ve been talking through four important questions we should consider about AI.

• Does this honor the image of God in every person it touches?

• Does it empower human relationships or substitute for them?

• Does this serve the mission?

• Does solving this problem move the needle on the missionary task?

These are theological questions and they are the same questions the Pope is asking in different language from a different tradition.

Three Things to Consider

1. The frame matters

The encyclical opens with magnificence, not fear. Our resolution does too. It calls us to engage from a place of eschatological hope rather than uncritical embrace or fearful rejection. That’s the same posture. Confidence in who humans are. Clarity about what only God can do. Wisdom about where tools belong.

Most evangelical responses to AI swing between the extremes of uncritical enthusiasm and reflexive fear. The SBC resolution charts a third path. We should be leading with that posture, not reacting to everyone else’s.

2. The questions at the Vatican are the questions every Christian should be asking

What does human flourishing look like in an AI world? How do we protect human dignity in every deployment decision? Who holds the power, and toward what end? What happens to the people whose roles are disrupted? What happens to the unreached peoples whose cultures are reshaped by technologies they didn’t build and didn’t choose?

These aren’t abstract philosophical questions. They’re operational and decisions being made right now often without any framework at all.

3. The Church’s seat at this table is not guaranteed.

Olah said it plainly. We need people outside the incentives. (Here I am!) People who can see what the builders, from inside, cannot. The Church has historically refused to let the world ignore the questions that matter most. That tradition is needed now more than ever.

Influence requires presence because you can’t shape a conversation that you’re not in. The question is whether we’ll bring it to the table before the table is set without us.

What I Keep Coming Back To

There’s a line from Olah’s remarks I keep returning to. He described AI models as being a little like bringing a fictional character to life and now those characters speak to us, do work, and have jobs.

He’s right. The question of what character we choose, how it interacts with the world, and how it ought to interact with the world, he said plainly, “These are more clearly questions for the humanities, for religion, for philosophy, and for society at large.”

The builder of one of the most powerful AI systems in the world, standing in the Vatican, saying… we need you in this conversation. I’ve been saying the same thing to Christian leaders for the past few years. We don’t get to opt out of this because it’s technically complex. The questions are theological before they are technical. We have a framework. We have a tradition. We have a mission that depends on getting this right.

Religious leaders need to continue showing up and influence where the AI discussion takes us.

Read the full encyclical. Read Olah’s remarks at anthropic.com. And if your organization doesn’t yet have a theology of AI, start building one because the conversation is already happening without you.

I Built You a Training Site

A few months ago, I wrote a 10-part series called “A Practical Guide to Claude Setup.” The idea was simple enough. Most people still think Chat GPT is “AI” and they never really consider anything else. My opinion is that Claude is far superior to anything else out there. Most people use AI like a slightly smarter Google search. Type a question, get a generic answer, spend the next ten minutes rewriting it into something usable.

The fix to make AI really work for you isn’t a better prompt, it’s an AI system. Giving Claude your voice is the key to moving from AI slop to AI production.

That series walked through how to set up Claude. It was file by file and lesson by lesson until Claude actually knows who you are, how you work, and what you’re trying to accomplish. Many of you read that series. A lot of you told me it was the most useful thing you’d seen on AI in a ministry context.

But a few of you also told me something that stuck. The series was spread across ten separate posts and building the system while bouncing between articles was harder than it needed to be.

So, I fixed it.

Introducing claudecode.donbarger.com

It’s free and it’s all in one place. It’s built so you can actually work through it as a training.

Everything from the series is there and then some! It has the full curriculum, all ten lessons, each one with the core concepts distilled, a concrete deliverable, and worked examples you can reference when you get stuck. There’s also a skills assessment that diagnoses your weakest dimension and tells you which lesson to do first. And for organizations, an AI readiness assessment that goes broader.

Here’s what you’ll build if you go through it:

A CLAUDE.md that introduces you to Claude once and routes every future task to the right specialized file. Not a bio. A system.

A living context document that captures what’s actually happening right now in your work — projects, relationships, current tensions — so Claude isn’t operating from stale information.

A three-file writing system that produces first drafts in your voice. Your structure. Your phrase bank. No more rewriting AI output that sounds like a McKinsey press release.

A prompt library that compounds. Every task you repeat, built once, used forever.

A comms file for the communications that require precision and diplomacy — the sensitive emails, the peer navigation, the board briefings. Different instrument, same values.

And for those of you leading ministry organizations: a faith-based adaptation layer that bakes in theological framing, stewardship principles, and a clear list of the human moments Claude must never touch.

Why This Matters

I’ll say the same thing I’ve been saying in the Faithful Intelligence series.

The goal isn’t AI-maximized ministry. It’s faithful ministry, resourced by AI in its proper place. A missionary with more time for language learning because AI is handling her reports. A pastor who spends three times as long in prayer and Scripture because AI helped do the sermon research. A small team that produces with the capacity of a much larger one and reinvests the time into presence, relationship, and the slow work that actually makes disciples.

That’s the vision and this course is how you build the infrastructure for it.

It’s not theoretical because I actually use this framework in my work. Dozens of tools that we’ve crated uses this frmework. A document translation engine was built with it. The AI Canvas runs on them. An image creation tool was built with it as was my video creation tool. Every tool I’ve shipped through the Innovation team was built using this Claude setup. Claude actually knew me and why I was building what I was building.

You can have that too. It can be set up in about an hour. If you really need to move slow, you should be able to set it up in about half a day.

Where to Start

If you don’t have Claude yet, start buy getting a subscription. Using that links gets both of us a little extra Claude credit ;)

Once you have a Claude account, go to claudecode.donbarger.com and take the Skills Assessment first. Five minutes and it tells you exactly which lesson to do next based on where your current setup is weakest. If you’re new to Claude, just start with lesson one.

If you’ve already been using Claude for a while, the assessment will probably surprise you. There’s almost always one dimension in your setup that’s significantly underdeveloped relative to the rest. Maybe it’s the domain file, the prompt library, the comms file, or something else… but there’s always something. Fix that and you disover real takeaways.

Build the system. Maintain it. And then do the work that was always yours to do.

Did I mention that it’s free? If you want to support our innovation fund that makes these tools available, check out the Innovation Fund at the International Mission Board.

If this was useful, share it with a friend in your network who’s trying to figure out where AI actually fits in their work. The conversation is just getting started.

A practical, theologically rooted guide for how to hold AI well.

A Framework for Ministry Leaders

Here’s what faithful AI use actually looks like.

A missionary in a hard place who has more time for language learning and relationship building because AI handles reporting, translation pre-processing, and administrative communications. A pastor who uses AI to do sermon research to help prepare sermons, then spends three times as long as he used to in prayer, Scripture, and visiting with people because those hours went somewhere that matters. A missions organization where intelligent systems carry the administrative and analytical weight of global operations, freeing the people inside it to do what only humans can do, build relationships, share the gospel, plant churches, and make disciples.

That’s the goal. Not AI-free ministry. Not AI-maximized ministry. Faithful ministry, resourced by AI in its proper place.

The question is how to get there intentionally rather than accidentally or never at all. Five Substack posts on the theology of faithful intelligence won’t produce that on their own. You need a framework, something you can actually put in front of your leaders. Here’s my stab at how to get started.

Commitment 1: Presence First

Before you automate anything, identify what requires a human.

Not what could be done by a human. Not what a human usually does. What requires a human and what loses something essential if the human is removed.

Pastoral care at a moment of crisis. Spiritual direction. The embodied witness of someone whose life has been changed by the God of the Bible. Cross-cultural relationship building. Intercession rooted in proximity and presence. Discipleship that knows someone’s name and history and still shows up.

These are your presence-first categories. Make them explicit, because efficiency culture will erode them if you don’t protect them on purpose.

We focus humans on pastoral care, prayer, spiritual counseling, personal discipleship, crisis response requiring human presence, and any context where the person engaging expects to be speaking with a human. I call this the Protect category. It’s not a concession to inefficiency. It’s a theological commitment. Relational and embodied presence work must remain human because it reflects the nature of the mission itself.

Everything else is a candidate for AI assistance. But start with the list. Get it in writing before you deploy anything.

Commitment 2: Transparency

If humans are not involved in communication or AI is representing your organization, the person on the other end has a right to know.

This is not just an ethical position. It’s a trust position. When someone discovers that a communication they believed was human was actually an AI, you lose something very hard to rebuild. Know they will discover this.

Develop a clear disclosure posture when humans are not in the loop. Decide in advance what carries an AI disclosure. Then hold the line.

One discernment question we use is would we be comfortable if those affected could see exactly how AI was used here? If the answer is no or uncertain, that’s your signal to pause before proceeding.

Commitment 3: Formation Over Efficiency

Every six months, ask this question: if you removed all the AI-accelerated output from your ministry, what would remain?

Schedule it. Put it on the calendar with the same regularity as your financial review. Make it a leadership conversation, not a solo exercise.

You are looking for two things.

First: is the presence-first core strong? Are the things that require humans actually getting human attention, or has the efficiency of AI in surrounding areas quietly crowded out the slow, unscalable work?

Second: are the people using AI tools becoming more effective ministers, or more dependent on outputs? There’s a difference between an AI that helps a pastor think more clearly and an AI that does the pastor’s thinking. The first is formation. The second is atrophy. Left unexamined, the drift from one to the other happens quietly.

If the core is strong and the people are growing, the AI is serving its purpose. If either is weak, you have a governance problem, not a technology problem.

Commitment 4: Steward, Don’t Just Use

AI governance in a ministry context isn’t primarily about risk management, though risk management matters. It’s about stewardship.

You are stewarding the trust of your congregation, your donors, your partners, and the people you’re trying to reach. Every AI deployment decision either builds or erodes that trust.

We frame every AI decision at IMB with a set of governing questions. I’ll adapt them here for any ministry context:

Does this use of AI honor the image of God in every person it touches? Does it empower human relationships or substitute for them? Does this serve the mission directly, or by freeing people for those who do? And finally: does solving this move the needle on the missionary task? If not, should we be doing this at all?

If the honest answer to any of those is no or uncertain, pause and evaluate before proceeding.

Stewardship also means thinking about formation, not just function. What is this AI tool doing to the people using it over time? Steward your tools the way you steward your finances: with intention, with accountability, with regular review, and with a clear sense of what they’re for.

Three Questions to Start the Conversation

If I could give you one concrete next step, it’s this. Gather your leadership team and put three questions on the table.

1. What are the presence-first categories in our ministry context, the things that require a human, that lose something essential if AI is introduced?

2. Where are we currently using AI, and do those uses empower human presence and relationship, or substitute for them?

3. What would faithful AI governance look like in our organization over the next twelve months?

You don’t need a full policy written out for your organization before you start. You really need to start with the conversation. Let the policy follow the the conversation.

We’ve covered a lot of ground in this series. McLuhan’s warning that every medium shapes us whether we notice or not. Pay close attention to the supplement-not-substitute distinction discussed in this series. The imago Dei as the anchor for understanding what AI can and cannot do. The seduction of efficiency is a challenge for all of us. Nothing wrong with being efficient, but only in the right things. It’s possible to be efficient in things that don’t move the needle. There are irreducible things like prayer, witness, intercession, and the work of God’s Spirit, that no AI model will ever replicate.

All of this Faithful Intelligence discussion leads us to faithful people, holding powerful tools with theological clarity, oriented toward the God of the Bible and the people He loves. The Great Commission doesn’t need better technology. Even with the advances of technology, the Great Commission needs faithful people with better tools.

Allow me to close this series with this. Be those faithful people, use the tools well, and build the kind of organization where your people have more time for the work that only souls can do.

This series is part of an ongoing project on the intersection of Christian missions and technology. If it was useful, share it with a ministry leader in your network. The conversation is just getting started. Now I return to the… Let’s Build This attitude that I normally take in my Substack!

The irreducible human and divine elements of gospel work that no model will ever replicate.

AI cannot pray.

I know that sounds obvious. Prayer assumes things AI simply does not have. It assumes a self, a particular person, with a history, a body, a record of failure and grace, who is bringing something real to the conversation. Prayer isn’t a user session. It’s not a prompt. It’s being brought by a person.

Prayer assumes a relationship. Not a connection to a database, but an actual standing before the God of the Bible. This is through the sacrifice of Jesus and maintained by the Holy Spirit, personal in a way that cannot be replicated by any system however sophisticated.

Prayer assumes need. Genuine dependence on a God who is other, who is greater, who can do what we cannot, and before whom we are genuinely small.

AI has none of these things. When AI generates a prayer (and it may create the words quite well, syntactically speaking) it is producing the form of prayer without the substance. Beautiful words pointed at nothing.

The Incarnational Logic

The God of the universe became a human being, in a body, in a place, at a moment in history. This isn’t a projection, avatar, or broadcast. The Word became flesh.

The incarnation is the ultimate anti-efficiency move. God could have sent information. He sent himself. He could have transmitted truth. He chose to embody it.

That logic doesn’t disappear when Jesus ascends. Instead, it gets handed to the church. “As the Father has sent me, I am sending you” (John 20:21). The church is the ongoing incarnational presence of Christ in the world, embodied, particular, local, and relational. You are sent the way the Son was sent. With a body and name into a specific place, to specific people, at a specific time.

AI is not embodied. It has no locality. It cannot be sent the way the Father sent the Son. It can carry information across distances that would have taken a generation to cover. It cannot carry presence. This is not a temporary limitation waiting for better hardware or faster processors. It’s a theological category to which AI is unable to belong.

What Only Humans Can Do

Intercession
When a missionary sits with a person, learns their name, watches their children grow, and carries their faces into prayer every morning for years, that is a form of intercession no AI-generated prayer will ever touch. The prayer is grounded in presence. It is specific in the way that only proximity makes possible. It costs something. That cost is part of what makes it real.

This can’t be outsourced to AI. There is no model to replicate that. There never can be. Not to anything that doesn’t know the family’s name and feel the weight of their reality.

Suffering alongside
Paul writes about filling up “what is lacking in Christ’s afflictions” (Colossians 1:24), a mysterious phrase that points toward something real. There is a ministry that happens only in and through suffering. The missionary who stays when it gets dangerous. The pastor who sits with the dying at 2am. Counselors who bear the weight of someone else’s trauma until it costs them something too.

That kind of presence cannot be relegated to an algorithm. Not because we haven’t built the right tool yet, but because the ministry is inseparable from the cost. Remove the cost, and you’ve removed the thing.

AI does not suffer. It cannot enter suffering with someone, because entering requires a self that can be affected. A self that has something to lose. AI is not a self and can never be a self.

Testimony.
This is the one that doesn’t get said enough. The gospel is most credible in the mouth of someone who needs it as badly as the person hearing it. AI doesn’t have a testimony. It has never been lost. It has no experience of grace, no moment when the weight lifted, and no before-and-after. It cannot say “I was there, and then I wasn’t, and here is what changed.” That witness, personal, embodied, costly, is irreducible. It is the oldest apologetic in the church and the one no model will ever be able to replicate.

The Spirit’s Work

The Spirit of the God of the Bible is the primary agent of gospel work. “Not by might, nor by power, but by my Spirit, says the LORD of hosts” (Zechariah 4:6). The conversion of a soul, the transformation of a community, the opening of a heart that was closed are all works of God. Not outputs of artificial effort.

This should be simultaneously humbling and liberating for every ministry leader thinking about AI.

Humbling, because no tool produces spiritual fruit. That is God’s work. Our tools, at their best, are instruments in hands that belong to Him. The harvest has never been ours to manufacture.

Liberating, because it takes the pressure off the optimization question entirely. We are not responsible for outcomes only God can produce. We are responsible for faithfulness, presence, prayer, and proclamation. AI can help with some of the logistics surrounding that work.

The harvest belongs to the Lord of the harvest. He just keeps asking us to go into it.

AI can’t pray, only you can pray. AI can’t suffer, only you can suffer. AI can’t testify to, only you can testify to. AI can’t receive from the Spirit of the God of the Bible, only you can receive.

Go do the thing the machine can’t do. That’s what you’re for. Let machines do what machines can do in order to free you to do the thing that only souls can do.

Next: A Framework for Ministry Leaders — a practical, theologically grounded guide for how to hold AI well.

Faithful Intelligence, Part 4. Why kingdom work and efficiency culture are in deeper tension than we want to admit.

The Temptation of Efficiency

I’m sometimes told that I’m a very efficient person. This isn’t entirely a compliment. The way I see it, over the years, I’ve probably gotten a lot of the wrong things done very efficiently.

That’s an odd way to start a post about efficiency. Not the productivity wins I’ve seen. Not the reclaimed hours due to AI. Instead, the this post will focus on times I optimized my way past the thing that actually mattered.

Efficiency is a measure of throughput. It tells you how much you’re producing per unit of input. What it doesn’t tell you is whether what you’re producing matters. That’s a different question entirely, and it requires a different kind of intelligence to answer.

AI is extremely good at efficiency. It is not always good at the question efficiency can’t answer.

So before you hand it more of your ministry, ask yourself: if you removed all the AI-accelerated output from your work this year, what would remain?

Relationships. Discipleship. Prayer. Presence. The slow, irreplaceable, unscalable human work of one person walking with another person toward Creator God.

If that core is strong, AI is a genuine amplifier. If that core is weak, AI is accelerating a problem, not solving one. Which is exactly what I argued in the moving the needle series. Know what you’re actually trying to move before you deploy anything to move it faster.

The Allure Is Real

I am pro-AI. Genuinely, enthusiastically, without apology. At the International Mission Board, we are doing some truly amazing things with AI.

On a personal note, I’ve reclaimed hours every week that used to disappear into administrative tasks. That time has gone back into relationships, into developing people, into the kind of slow unscalable work that actually makes disciples. AI has also multiplied my output in ways that would have taken a significantly larger team a few years ago. When AI frees time for presence, it’s doing something great.

Here’s what I didn’t fully anticipate: efficiency is addictive.

Once you’ve optimized one thing, the instinct is to optimize the next thing. And then the next. And eventually you’re applying efficiency logic to things that weren’t meant to be efficient.

You can’t disciple efficiently. You can’t grieve efficiently. You can’t pray efficiently, or rather, you can, but what you’re doing probably isn’t prayer anymore. You can be more effective, more contextual, better prepared, but the formation that happens in the slow work of showing up, over time, for another person. That doesn’t compress.

The moment efficiency becomes the primary lens, something shifts at the level of value. You’ve moved from “what is faithful” to “what is fast.” Those questions have very different answers.

What the Bible Says About Speed

The God of the Bible operates on a timeline that makes most ministry leaders deeply uncomfortable.

Consider Abraham. He received the promise when he was seventy-five. Isaac was born when he was one hundred. Twenty-five years of waiting for a single child, while the nations of the earth hung on the fulfillment of that one covenant.

God was not slow because he lacked capability. He was deliberate because the process was the point. Abraham wasn’t just waiting for a son. He was being formed into a father of faith. The waiting was the work.

That pattern holds across the whole of Scripture. Moses: forty years in the wilderness before his assignment began. The disciples: three years with the Son of God himself, and they still didn’t fully understand what was happening.

Formation takes time. Character is slow. Trust is built in the accumulation of ordinary faithfulness, not in optimized sprints toward measurable outcomes.

The fruit of the Spirit (love, joy, peace, patience, kindness, goodness, faithfulness, gentleness, self-control) are not efficiency metrics. They are the outputs of a continuous, often invisible process of transformation. AI can accelerate information delivery. It cannot accelerate formation.

The Pattern

A ministry leader discovers AI. Genuinely saves time. Starts applying it more broadly. Begins measuring outputs: content produced, contacts made, programs managed. The numbers go up. Leadership is pleased. More AI is deployed.

At some point, hopefully someone asks, “Are people actually being discipled? Are lives being changed?” Again, I want to point you back to the moving the needle series.

Many organizations find they’ve been measuring what’s easy to measure. The things that actually matter, depth of relationship, spiritual growth, lasting transformation, are hard to measure and slow to develop, so they got quietly deprioritized in favor of the metrics that move. This is the needle problem again. Know what you’re trying to move. Stay ruthlessly honest about whether you’re moving it.

The fix isn’t less AI. It’s clearer thinking about what AI is for.

Application

Start with the core and let AI serve that.

What’s the core? In ministry: relationships, spiritual conversations, discipleship, prayer. The work of one person walking faithfully with another person, over time, through ordinary life. Use AI on everything else (administration, content research, communication, preparation) to free you up to do more of that. More presence. More depth. More of the slow work that doesn’t show up in a dashboard but produces the thing we’re actually after.

Disciples made. Communities transformed. The knowledge of the glory of the God of the Bible covering the earth as the waters cover the sea.

Efficiency is a good servant. It is a terrible lord.

Keep it in its place. But know that keeping it in its place requires more intentionality than you think. The temptation is quiet. It doesn’t announce itself. It just makes the fast thing a little easier than the slow thing, every single day, until the slow thing stops happening.

Use AI well. Use it broadly. Just make sure what you’re amplifying is worth amplifying.

Next: AI Can’t Pray — the irreducible human and divine elements of gospel work that no model will ever replicate.

Before I tell you about the meeting that stopped me cold, I want to talk about this series about Faithful Intelligence. I’ve struggled to keep this series going because I keep rewriting these posts.

People who talk about everything wrong with AI often assume those of us using it daily have skipped the hard questions. That we don’t care about the theological weight of what we’re picking up. Hear me say that couldn’t be further from the truth.

Before our team built a single tool, we worked through what we actually believed about AI. What is a human? What is work? What does it mean to steward what the God of the Bible has entrusted to us? It’s why we have an AI Ethics Charter and the work on that document pre-dates almost every AI tool that we’ve built.

I keep hearing the same critique. I tried AI. It got something wrong. It hallucinated. So clearly no one can use it faithfully. What that’s really saying is: I tried something. I didn’t know how to use it. It didn’t work. Therefore nobody can. That’s a bad argument against AI.

I’m not asking you to be excited about AI. I’m asking you to be honest about your skepticism. The people most ready to hear what AI cannot do are the people who’ve actually tried to use it for the things it can. The nexus of this current series is trying to reassure those who assume that those of us who are using AI have actually have thought through the ramifications of what we are doing.

So let me tell you about the meeting.

The Proposal I Couldn’t Say Yes To

I was sitting with another organization when they laid out a plan that pulled me up short. I can’t remember if I have mentioned this in another series, but it’s worthy of a quick reminder.

They were working in a context with overwhelming trauma and almost no trained counselors. The need was real. More suffering than any human system could reach. Their solution: AI chatbots as trauma counselors. Not triage. Not a bridge to a human. The AI as the counselor, full stop.

Their argument was straightforward… an AI counselor is better than no counselor at all.

I sat with that for a moment. There’s a logic to it that’s hard to dismiss. Then the uneasiness came. Here’s what I kept coming back to. What does it mean that a human being, made in the image of God, in the middle of genuine trauma, receives something that performs personhood without having any?

That question cuts straight to the center of what AI is, what humans are, and what the church should refuse to outsource no matter how elegant the technology becomes.

Because we’d done the theological work in advance, the decision wasn’t actually hard. The proposal asked AI to do something our charter says AI cannot do. We didn’t participate. No hard feelings. It just isn’t something that we can participate in.

That clarity came from having a framework before the meeting, not during it. We had already set our guardrails and boundaries before we needed to have an answer.

AI Is a Mirror. That’s the Frame.

Before we get to Genesis, here’s the frame I use. AI is a mirror. A sophisticated one. It reflects back patterns from human expression. This includes our language, our reasoning, our empathy rendered into text. When AI seems wise, human wisdom is in the training data. When AI seems compassionate, human compassion has been distilled into patterns the model learned to reproduce.

AI is useful precisely because it reflects us. And it’s limited in ways most users never slow down to reckon with. AI doesn’t generate meaning. It reflects meaning that humans (image-bearers) have already created.

The chain of derivation matters. God is the source. We bear his image derivatively. AI reflects our outputs. At each step, something is lost. The further you move from the source, the thinner the reflection gets.

What the Text Is Actually Doing

“Then God said, ‘Let us make mankind in our image, in our likeness…’ So God created mankind in his own image, in the image of God he created them; male and female he created them.” (Genesis 1:26-27)

Notice what the text doesn’t say. It doesn’t say God created humans with greater intelligence, or more complex emotional circuitry, or superior reasoning capacity. It says he created them in his image. The distinction is not a category of capability. It’s a category of relationship and calling.

Then Genesis 2 gets specific:

“Then the LORD God formed a man from the dust of the ground and breathed into his nostrils the breath of life, and the man became a living being.” (Genesis 2:7)

The breath of God. That’s what makes Adam a living soul. Not the arrangement of dust, but the direct giving of life from Creator God. The Hebrew word here is nephesh.. soul, life, the whole living person. The God of the Bible put something of himself into a creature made of dirt!

AI is made of math. Sophisticated, remarkable, genuinely useful math. Math that I told my high school Calculus teacher no one in the real world would use… boy was I wrong. Because AI is math, there is no breath in it. There is no nephesh.

The Image Is a Person, Not a Property

Here’s where most conversations about the imago Dei go wrong. They treat the image of God as a property humans possess. a feature like rationality or creativity. That framing leads straight to the question, “Well, if AI becomes rational enough, does it bear the image too?”

The New Testament answers that question before it can be asked.

“The Son is the image of the invisible God, the firstborn over all creation.” (Colossians 1:15)

Christ is not an image-bearer. He is the image. The eikon. He is the exact, full, unmediated representation of God. When we talk about humans bearing the image of God, we mean a derivative, creaturely participation in something that finds its complete expression only in Christ.

This changes everything about the AI question. The image of God is not a set of capacities that could theoretically be replicated with enough compute. It is a relationship. A participation in Christ, mediated by the Spirit, that is moving somewhere.

“And we all, who with unveiled faces contemplate the Lord’s glory, are being transformed into his image with ever-increasing glory, which comes from the Lord, who is the Spirit.” (2 Corinthians 3:18)

Note this is present tense and ongoing. The Spirit is at work.

Christians are creatures made by God, fallen, redeemed, and being conformed to the image of the Son. AI is not in that story. Not as an image-bearer. Not as something being transformed. AI sits entirely outside the arc of creation, fall, redemption, and glorification that defines what a human being is.

What This Means on the Ground

Every human being you meet is a creature made in the image of God, participating however dimly in the likeness of Christ, and the object of the Spirit’s transforming work. That is the ground of human dignity. Not capability. Not productivity. Image-bearing.

Psalm 8 doesn’t let us look away:

“What is mankind that you are mindful of them, human beings that you care for them? You have made them a little lower than the angels and crowned them with glory and honor.” (Psalm 8:4-5)

Crowned with glory and honor. That’s who is sitting on the other end of your AI deployment decision. A being made in the image of God, being conformed to the image of Christ, deserves to be met by another such being. Not a simulation of one.

The Question That Cuts Through Everything

Back to that meeting that began all of this.

The argument was that an AI counselor is better than no counselor at all. I understand it. They weren’t saying that AI was a help to the counselor or that AI was a triage to help with initial assessments. They were saying it was the counselor.

Even so, our AI framework doesn’t let me say yes. If AI is a mirror, reflecting human meaning without possessing it, then what that trauma survivor receives is not care. It’s the shape of care. The pattern of compassion without the presence of a person. And the person receiving it is crowned with glory and honor, made in the image of the God whose Spirit is even now at work in the world.

The mirror cannot meet the image-bearer. It can only reflect back. So before you deploy AI in any ministry context involving people, ask this:

Is the person on the other end receiving something that honors their dignity as a being made in the image of God, or something that performs that honor without the substance of it?

That question won’t always give you a clean answer. But it will keep you asking the right thing.

Back to the skeptical reader I started with.

The honest critique of AI in ministry is not AI cannot be used faithfully by anyone for anything. That’s not skepticism. That’s abdication. The honest critique is AI cannot be the image-bearer’s substitute. Where AI extends image-bearers in their calling, use it. Where AI tries to replace image-bearers in their calling, don’t use it.

AI is not made in the image of God. You are. The person you’re trying to reach is.

Next: The Temptation of Efficiency — why kingdom work and efficiency culture are in deeper tension than we want to admit.

The central theological argument for how the church should hold AI.

Supplement, Not Substitute

When I heard about a sermon that was preached by AI, I had questions. I had to go find out more. Here’s what I found.

It was a real church service on June 2023, St. Paul’s Church in Fuerth, Bavaria and over 300 people attended. A theologian named Jonas Simmerlein had fed ChatGPT a prompt and a theme, then stepped back. He later said that 98% of what happened that Sunday came from the machine.

People said, “Everything was there. The structure was tight. The theology was sound. The sermon covered leaving the past behind, facing the present, overcoming fear of death, trusting Jesus Christ. It didn’t stumble. It didn’t ramble.” By many measurable standards, it was competent.

One congregant walked out and told reporters: “There was no heart and no soul.”

She was right and she said what most of us feel. It’s sometimes lost in all of the talk about what AI can do.

This Is Already Happening

Many people would like to believe this is an experiment at the fringe. It isn’t. I continuously hear stories from leaders asking how this should be addressed. Recently, I had a professor tell me that many pastors in his state were using AI in ways that are unethical. I agree with him.

As of 2025, 64% of pastors use AI for sermon preparation. That’s up from 19% just two years earlier. One of the fastest technology adoptions in modern church history. Scroll through ministry forums, pastor Facebook groups, church tech newsletters. The question isn’t whether AI is in the pulpit. It’s how far in, and how quietly.

One Lutheran pastor described opening ChatGPT during a particularly hard week, plugging in the week’s Scripture, and receiving a 900-word sermon in under thirty seconds. Their reaction, “This is really good, and this feels wrong.”

The pastor was giving honest, gut feedback. I believe that the church needs to go further than honest discomfort. We need a framework for why it’s wrong. If you are a reader of my posts, you’ll know that I’m very positive on what AI can do. I also, from time to time, write about the times when people are crossing the line between good usage of AI and unethical usasge of AI.

Is This Really New?

To be fair, this isn’t the first time pastors have taken shortcuts. For years, ministers bought cassette tapes of other pastors’ sermons and delivered them from their own pulpits. Years later, that evolved to downloaded audio files. Eventually online sermon database subscriptions emerged. They had full manuscripts, illustrations included, and ready to “adapt” for their own usage. Unfortunately, the church world has always had its Saturday night specials.

AI didn’t invent the temptation to take the homeletical shortcut. It’s just removed every last barrier to giving in to it. What used to require finding the tape, purchasing the subscription, at least doing the work of adaptation, now takes thirty seconds and a well written prompt. The path of least resistance has never been shorter. Which means the discipline required to resist it has never been more necessary.

The Difference That Changes Everything

This topic will get people upset quickly. Some are very convicted that there is no place for AI with the church. There are people who are using it widely. I believe there is a place for using AI, but it depends upon how it is being used. Here’s the framework for two different approaches to using AI. The framework can be summed up by two words.

Supplement.

Substitute.

A supplement extends what’s already there. A substitute replaces what’s no longer there.

When AI helps a pastor research a text, suggests an illustration, flags a theological issue in a draft, that’s a supplement. The pastor is still the author. The sermon still passed through a human soul before it reached the congregation.

When AI writes the sermon and the pastor delivers it, that’s a substitute. The congregation is receiving content that was never carried through a human mind wrestling with Scripture, a human heart, the Holy Spirit, or a human life marked by the failures and graces being preached about.

Why This Is Theological, Not Just Practical

One pastor asked a question, “Why can’t the Holy Spirit work through AI?”

I’m not one that puts limits on what God can or cannot do. What I do know is what God has consistently chosen to do and the pattern.

The God of the Bible did not send information. He sent Himself. “The Word became flesh and dwelt among us” (John 1:14). Not a document. Not a broadcast. A person, in a body, who walked specific roads, touched specific people, died a specific death, and rose from the dead.

That logic runs through the entire New Testament. Paul didn’t just write letters. He sent people with the letters. He sent Tychicus, Epaphroditus, and Timothy because he understood that the carrier is part of the message. “I hope to come to you and talk face to face” (2 John 12). The apostolic pattern treated embodied presence as the normative vehicle of gospel proclamation.

This is why one theologian, pushing back on AI preaching, said the struggle to write a sermon isn’t a problem to be solved. The drudgery is part of the point. The pastor who sits with a text, reading, praying, struggling, reading again, and praying is being formed by that process. That formation shapes what comes out of their mouth on Sunday. You can’t outsource formation. You can only skip it. And when you skip it, the congregation pays the price without knowing why.

Even Jonas Simmerlein, the man who designed the Germany experiment, felt it. Standing in his own church service, he said afterward: “The pastor is in the congregation. The pastor lives with them. The pastor buries the people. The pastor knows them from the beginning. Artificial intelligence cannot do that. It does not know the congregation.”

He built the thing and he understood what was missing.

Souls Care for Souls

I say this often when sharing with people about the limits of AI and ministry.

Souls care for souls. Algorithms don’t care for souls. Soul care must be done by souls.

A sermon is not an information delivery mechanism. It is a pastoral act. One person, under the authority of Scripture, standing before other persons and saying: I have met this God. He is real. He has met me in my failure and my grief and my confusion. And he will meet you too.

That witness requires a witness. Someone who has actually been met. An algorithm has not been met by anything. It has been trained on the record of those who have. That’s not the same thing.

A Korean pastors conference earlier this year put it this way, the preacher is one who embraces souls beyond data. AI can generate a message, but it cannot say it has actually experienced that message.

A pastor of 32 years wrote: “The gospel is more than words. It’s the evidence of a changed life. What a congregation is looking for is evidence that the pastor has been with Jesus.”

And a congregant in New York, after a rabbi revealed mid-service that his sermon had been written by ChatGPT, said this: “I like what you write a lot more. It comes from an actually living being, with a great brain and a compassionate, beating heart.”

Congregations feel the absence and they feel it even when they can’t name it. The woman in Bavaria named it. There was no heart and no soul. She wasn’t making a theological argument. She was reporting what she experienced. I believe that she was right.

Where the Line Gets Blurry

AI as a research assistant, that a pastor then wrestles into something personal, a tool for illustrations or checking theological accuracy, that’s supplement territory. The human is still the author in all the ways that matter.

AI as the author, with the pastor as editor or simply the voice, that’s substitution, regardless of how polished the output is.

The test to apply. Did this pass through a human soul? Did someone pray over it, struggle with it, carry it to the text and back again? Is there a person behind this whose life is accountable to what is being said?

If yes, I would consider this using AI as a supplement. If not, I would consider this using AI as a substitute.

In practice, this line isn’t always as clean as I have written above. The principle is clear. Start with presence. Start with the human. Let AI extend that. Don’t let it replace it. The message is inseparable from the messenger who is called by God and set apart for ministry (Hebrews 5:4; 1 Timothy 3:1-7).

The God of the Bible is not a God of efficiency. He chose the slow path. Thirty years of ordinary life before three years of ministry. He chose twelve specific, ordinary people over a broadcast strategy. He chose a cross over a manifesto.

That logic doesn’t get suspended when we have better tools. If anything, better tools make it more important to hold the line. As believers, we are called to “test everything; hold fast what is good” (1 Thessalonians 5:21, ESV), and this includes evaluating new tools through a biblical lens.

The human is irreplaceable. This isn’t because humans produce better content than AI, but because the gospel travels through people. It always has and the God who chose to become one of us didn’t do it by accident.

The Faithful Intelligence series addresses how the church should respond to the challenges presented in an age of Artificial Intelligence. If you haven’t read first article in this series, The Medium is the Message, check it out!

Next up? Made in the Image of God, Not the Image of Data — what the imago Dei tells us about what AI can and cannot do.