Leadership in Change with AI - Podcast

If you’ve spent any time trying to make sense of where AI is actually going, not the headlines but the structure underneath all of it, you’ve probably run into Gennaro Cuofano’s work without knowing it was his. He built The Business Engineer (spun off from FourWeekMBA) into a research hub more than 90,000 people follow, and he just launched something new called The AI Supercycle.

I wanted to sit down with him because he maps this stuff for a living, and his read on it is different from almost everyone else’s. His core claim is that we keep comparing AI to the internet when the better comparison is the 1950s, the moment we built the computer for the first time. That one reframe changes where you look for advantage, who you watch, and how far ahead you’re allowed to plan. Watch the full conversation above.

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Outline

(1:10) – From FourWeekMBA to The Business Engineer

(4:34) – Why launch The AI Supercycle now

(8:18) – The semiconductor moment, not the internet

(14:43) – The nine-layer map and why governance sits on top

(17:41) – Spinning hundreds of agents to run a business

(19:45) – Governance, government, and the Palantir layer

(24:01) – Why human conversations beat the AI content flood

(26:35) – The Builder PM and the role that split in two

(31:25) – Orchestrating agents in real disaster response

(38:24) – From SaaS to Agent-as-a-Service

(40:02) – Audience question: what happens to SaaS?

My Takeaways

The semiconductor moment. Gennaro’s whole thesis hangs on a historical analogy, and it’s the thing I keep coming back to. Everyone wants this to be the internet, a 20 or 25 year cycle. He thinks that’s the wrong map. As he put it:

“We’re building the computer. It’s not the web. It’s the 50s, 60s, 70s, when we built the computer for the first time. This is a 30 to 50 year cycle.”

What got me is that most analysts I talk to are afraid to forecast past three years right now, and here’s someone calmly drawing a map for the next three decades.

Governance on top. I told him I loved his nine-layer map of AI, because he does something nobody else does, he puts governance at the very top, above the models, above the chips, above everything. His reason is blunt: the frontier now answers to government, and there’s no getting around it.

“That’s why the governance layer is going to become one of the most important for the top of the frontier. There’s no way out.”

He pointed to Palantir already routing different AI models through everything the government does. If you only read the tech headlines, you miss the layer that actually decides what ships.

The Builder PM. Gennaro’s been a filter between clients and technical teams for years, so this one is lived, not theoretical. Building got easy. Everything around the build, the integration, the deployment, the org politics, got harder. So the product role split. The new version turns a client’s specs into something testable in six months instead of three years, which makes the PM a hybrid of builder and orchestrator. I see the same thing in my World Relief work, where we now run a team of agents for disaster response and a human has to sit at the center coordinating the writers, the researchers, the photographers. You stop being the doer and become the architect.

From SaaS to Agent-as-a-Service. His prediction for the next 18 months is that per-seat, per-human pricing breaks, because your biggest customer stops being a person and becomes a fleet of agents consuming your software millions of times over.

“There will be many SaaS companies worth 100x more, because now with the agents you get a level of consumption you didn’t have before.”

The way I said it back to him: the pie went from being this big to unlimited.

Sidenote… do you write on Substack? Build smarter, not just harder, with NewsletterCompass.com. As a co-founder, I’m giving you 50% off for life… 👇

One Question to Sit With

When everyone has the same frontier models for twenty dollars a month, where does your advantage actually move to? Gennaro’s answer is “down the stack, toward governance and infrastructure,” and I haven’t stopped chewing on it since.

Watch the full conversation above, and then go subscribe to The AI Supercycle. It’s the clearest map of this moment I’ve found.

About Gennaro Cuofano

Gennaro Cuofano is the creator of The Business Engineer, a deep-tech research hub spun off from FourWeekMBA, the business-model-strategy blog he started in 2015 and grew past 90,000 subscribers. A tech executive by day, he writes about the structural shifts reshaping business in the AI era and just launched The AI Supercycle, a newsletter and podcast decoding where the next 30 years are heading. Subscribe at supercycle.ai.

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe

AI keeps getting better, so why aren’t your results getting better?

Bryan Cassady’s answer:

Most people are getting more output, not better work, because they skip the thinking and go straight to the tool.

In this Substack Live we get into his Type 3 error (doing the wrong thing, fast), why objectives have to come before any tool, the real $11,000-a-year cost of AI that most leaders never count, and what a generative organization actually is. If your team has AI and still isn’t getting results, this is the conversation to watch.

Also, click below to subscribe on Apple Podcasts! 👇

Outline

(00:00) – Bryan’s mission: a million people getting results from AI by 2027

(01:02) – Better models, worse results: the human half of the equation

(03:16) – How Bryan started: “six months ahead of everyone else”

(05:16) – Objectives first, tools second

(07:33) – The Type 3 error: doing the wrong thing, but doing it right

(09:57) – The Dan Ariely story: six months on a hallucinated theorem

(11:28) – Think first: Einstein’s 55 minutes on the problem

(15:26) – From an answer economy to a question economy

(16:20) – Strategic clarity and the $11,000 cost of AI

(18:48) – What a generative organization actually is

(24:00) – The 3% AI can’t do, and why it sets you apart

(31:00) – Bryan’s giveaways: free books, tools, and a beta course

My Takeaways

The Type 3 error. So here’s the frame I keep coming back to from this whole conversation. Bryan put it like this: a Type 3 error is when “you’re doing the wrong thing, but you do it right.” And the problem with AI is you can now do the wrong thing really fast and feel great about it the whole time. I’ve had people hand me a polished 25-page deliverable that nailed every step of a question nobody actually needed answered.

Objectives first, tools second. Bryan’s line on this is the one I’d put on the wall:

“AI first is silly. It’s like being a carpenter who wants to be a hammer-first carpenter. A hammer can do cool things, but it doesn’t do everything.”

Most leaders are out shopping the 75,000 listings on a site like There’s an AI for That (a directory of AI tools) when the real first move is naming the objective and then picking the tool that serves it. Get that order wrong and you just bought a faster way to drift.

The $11,000 nobody budgets for. Here’s the part that should stop every leader who thinks AI is a $20-a-month decision. Bryan cited a Gartner study putting the time people spend controlling, revising, and re-editing AI output at around five hours a week, which works out to roughly $11,000 a year for a typical US knowledge worker. “Do you get $11,000 more benefit from using AI? Of course you can, but only if you do it smart.”

Most don’t. And that’s before you count what poorly rolled-out AI does to a team’s trust in their own leadership.

The 3% AI can’t touch. This is where it got good. Bryan's argument, borrowed from an innovation method called TRIZ, is that about 97% of what we call innovation is really recombining things that already exist, and AI does that better than almost any human. But the last 3%, the genuinely new, the thing that doesn't exist yet, that's still you.

“Humans have an ability to look forward. AI has an ability to look backward.”

That 3% is the creativity and judgment no model hands you, which is exactly what the AI Leadership Triad is built around. Pair it with his bigger shift, that we’ve moved “from an answer economy to a question economy,” and the job gets clearer: stop racing the machine on answers and get very good at the question.

Two things Bryan is giving Leadership in Change readers:

* Both of his books, free, plus his PDF innovation tools — books.genorg.ai

* A free, private check on how you actually use AI: paste his assessment into ChatGPT or Claude, and it scores you 0 to 10 from your own chat history — get the assessment

* A free spot as a beta tester for his new AI course (10 lessons, 10 minutes a day, with a before-and-after read on how you actually use AI) — grab a beta seat here

One Question to Sit With

When’s the last time you spent five whole minutes on the problem before you opened the AI? Bryan says when he asks a room that, almost every hand goes down. Watch the full conversation above, and then go grab Bryan’s free books and tools at books.genorg.ai.

About Bryan Cassady

Bryan Cassady is the founder of GenOrg and the author of The Generative Organization. He’s an AI keynote speaker who has trained more than 40,000 people, and says he’s now helped around 90,000 toward his goal of a million people getting real results from AI by 2027. He also sits on the board of the US association for TRIZ, the structured innovation method he teaches. Get both of his books and his innovation tools free at books.genorg.ai.

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

Thank you Farida Khalaf, Claire Machado, Duncan The Sage, and many others for tuning into my live video with Bryan Cassady! Join me for my next live video in the app.

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This one got into the part of AI adoption nobody really wants to talk about. Ryan Deeds runs AI at ALKEME, an insurance company that bought its way from seven agencies to ninety-three in about four years, and his whole argument is that the technology was never the hard part. The hard part is getting fifteen hundred people to actually trust a tool and use it. We talked about the golden path, why every rollout fails for the same reason, the eighteen months it really takes to hit adoption, and the bot pipeline that lets anyone at the company build their own software. Watch the full conversation above.

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Outline

(00:11) – Scaling without breaking culture

(00:49) – Seven agencies to ninety-three in four years

(02:07) – From data guy to Head of AI

(03:41) – The golden path across 150 workflows

(05:32) – “AI is a bunch of hype” and the cultural work underneath

(08:17) – Why every rollout fails for the same reason

(12:54) – The 85% that fail, and what they all skipped

(14:21) – Fear, mistakes, and fifteen dead initiatives

(15:05) – Eighteen months to 70% adoption

(19:43) – Why the model wars are wasted noise

(21:14) – There’s no easy button

(24:05) – The bot pipeline that lets anyone build

(27:19) – Scaling AI without it blowing up in your face

(29:23) – The most overrated AI advice

My Takeaways

The failure is always culture. Ryan has built nine CRMs and implemented seven more, and he says the result never changes.

“Got nothing to do with technology. It’s culture.”

I brought up the stat that 85% of AI implementations fail, and his read matched what I see all the time: people get handed a tool, get told to use it, and decide not to, either because they think it’s there to replace them or they don’t trust the people who bought it.

There’s no easy button. Ryan’s least patient moment was about the model wars.

“You cannot judge current state AI in current state,” he said, and the people arguing over which model beat which this week are usually the ones not actually building anything. What matters to him is boring on purpose: how many people used it, how many minutes did it save. “If I was judging what I created, I’d be freaking Elon.” That’s the line I keep coming back to, because most of us are measuring the wrong thing.

Eighteen months to seventy percent. I appreciated how honest he was about the timeline. He told his CEO up front it would take about eighteen months to reach full adoption, and that full adoption, in his world, is 70%. The pushback he plans for doesn’t come from the technology. It comes from moving people out of a culture where nobody was accountable and into one where everybody can see your numbers.

Closest to the work is clean. This was the part that genuinely surprised me. ALKEME has fifteen hundred employees and a build team of nine, so Ryan built a pipeline instead. An employee emails an idea, a bot pins down the narrowest version of success, another bot builds the MVP on the company’s own infrastructure and sends back a working link to iterate on.

“I would way rather empower you, employee E, to bring your concept to fruition.”

The person closest to the problem builds the fix, inside the guardrails.

Don’t pour a bucket of AI water on everything. For an insurance company, a broken tool isn’t a small thing, so I asked how he moves this fast without it blowing up. His answer was discipline about scope.

“It’s not just pour a bucket of AI water on everything and see where it hits.”

They ran fifty thousand historical submissions through one tool before they trusted it, locked permissions to each user, and pushed everything through a pipeline that gets checked. “We’re just very careful about where it touches.” It’s the same discipline ALKEME brings to the coverage side, where a single missed risk like a cyber attack is exactly what their clients are paying to be protected against. If your business carries that exposure, their team handles cyber insurance directly.

If you’re the person inside your company trying to get AI actually adopted and not just installed, that’s most of what I work through with teams. You can start at jsalinas.org and book a free call with me.

If you rolled out an AI tool tomorrow and nobody used it, would you call that a technology problem or a culture problem? Sit with which one you’d actually fix first.

Watch the full conversation above, and go connect with Ryan Deeds on LinkedIn and see what ALKEME is building.

About Ryan Deeds

Ryan Deeds is Head of AI & Enablement at ALKEME, an insurance company that has grown to around 93 agencies and 1,500 employees in roughly four years. He spent 30 years in the insurance agency space, hosted The Digital Broker podcast from 2017 to 2019, and now builds the internal tools and adoption systems that let ALKEME scale AI without losing its culture. Connect with him on LinkedIn.

About ALKEME

ALKEME is a national insurance company built from more than 90 independent agencies, serving clients across commercial, personal, and specialty lines. One of those lines is cyber insurance, coverage that protects businesses against cyber attacks, data breaches, and the operational fallout that follows. If your organization needs to think seriously about cyber exposure, talk to ALKEME.

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe

I keep running into the same thing when I talk to leaders about AI. They’re excited and they’re exhausted… at the same time.

They see how capable the models have gotten, and in the same breath they tell me they’re starting to feel a little obsolete. Curious and anxious, sitting in one person, at the same desk.

I wanted to talk to Liz Fosslien and Mollie West Duffy about exactly that, because they’ve spent their whole careers on emotions at work, and now almost all of that work is affected by AI. What you get in this one is the part of the AI conversation almost nobody is running: the feelings underneath the rollout, and what a leader actually does about them. Watch the full piece above.

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Outline

(00:00) – Welcome

(00:38) – Excited and exhausted: the leader’s double bind

(01:48) – Where emotions at work began

(04:01) – The full mix: amazing and existential, in one person

(06:03) – What a team really asked me about AI

(07:42) – The “AI optimists only” trap

(08:08) – Why emotions block learning

(11:49) – The stoic-leader shift

(17:33) – The change curve: anger to energy

(22:32) – Build trust before you measure adoption

(25:13) – Mandates fail, agency works

(27:13) – Inside their AI workshops, and where to find them

Main Takeaways

The full mix is the normal state. The thing I want every leader to hear is that excitement and dread aren’t two different camps of people, they’re the same person sitting in one chair, which is exactly why this is so hard to manage. Liz put it like this:

“Every single person contains this complete mix of emotion. At some level they say, oh, it’s amazing, it’s way better than Google, it can help me so much. But then of course, what does this mean for my job, for who I am?”

The leader who pretends only the excited half is real is usually the one whose rollout starts going sideways three months in, because the other half doesn’t disappear, it just stops getting said out loud.

You can’t learn while you’re scared. This is the line I’m still going over. Mollie walked through the neuroscience of it:

“When you are feeling anxious, when you are feeling scared, when you are feeling angry, you’re not in an optimal place to learn a new tool.”

Then she made it concrete.

“I’m here for the training on how to use ChatGPT, but I’m really worried about what it’s going to do to my job, and I’m angry about what it’s doing to the environment. I’m not really going to be giving 100% of my attention to how to click around the tool.”

I had a chat recently coaching a marketing group at a nonprofit, and within two minutes I realized they didn’t care about prompts or tokens. They were asking how they could use AI when it’s doing what it’s doing to the climate, and how a writer uses a tool trained on copyrighted work. There’s a whole foundational layer most of us skip, and the training slides right off when you do.

We don’t get to skip the steps. Mollie’s change curve is the frame I keep coming back to.

“We all have to go through anger, apathy, frustration, and your productivity drops. And that’s normal. Then when we move through that, we can begin to get engaged and excited and have energy around that. We don’t get to skip these steps as humans.”

The part that reframed it for me is that most leaders are already further along that curve than their own team, so they’re standing on the far side wondering why everyone won’t just cross. People hit the dip at different moments, especially on a big team, which means a leader is managing a dozen different curves at once, not one.

It’s okay to give some of the time back. This is where I admitted my own fear on the call, the one I think a lot of people are holding. If I start using AI and suddenly I’m producing two or three times as much, I’m afraid that just becomes my new minimum, and now I have to hold that pace forever. Liz met it head on:

“It’s actually okay if some of the time saved your team takes back for themselves. It doesn’t all have to be reinvested in work. Workforces are so burnt out. The average person, our nervous system, is completely fried.”

Saying that out loud, as the leader, is part of what makes adoption possible in the first place.

The play I’d actually run this week

Because this slot runs a little longer than my usual live recap, I want to leave you with something you can use on Monday, pulled straight from what Liz and Mollie laid out.

* Name the mix out loud, briefly. Nothing elaborate. A short, honest recognition like “I’m feeling some of this too, and here’s the plan moving forward.” Mollie called it a brief recognition followed by the path, and it’s the tightrope between spewing every emotion and pretending you have none.

* Pair a quick win with one real limitation. Liz’s most practical move. Help your team get a fast, obvious win with AI, then show them one place it falls flat. That combination lets people lean into the excitement and lower the anxiety at the same time, because now they trust both the upside and their own judgment about where it doesn’t belong.

* Say the change is big, in public. In an all-hands or your enablement session, recognize out loud that this is a real change and that people are moving through real emotions at different speeds. That one sentence gives people permission to be where they actually are.

* Build the trust before you measure. Liz was blunt about this. If you haven’t invested in trust up front, your survey just comes back “I feel great, it’s amazing,” and then weeks later you find out people aren’t using it, or they’re actively sabotaging it. Trust first, honest data second.

* Give agency, not a tracker. Enablement, training, and time to learn, yes. Surveillance of daily usage, no. Let some of the time saved go back to people, and ask whether they’re using the right tool for the right job rather than how many tokens they burned.

Where are you on the change curve right now, and have you actually let yourself name it out loud, or are you the leader who’s been pretending only the excited half is real? Watch the full conversation above, and then go subscribe to Liz and Mollie. It’s one of the few places putting the human side of this moment first.

About Liz Fosslien and Mollie West Duffy

Liz Fosslien and Mollie West Duffy are the authors and illustrators behind No Hard Feelings and Big Feelings, two books on the emotional reality of work and life. Liz draws every illustration by hand, leads content work at Atlassian, and is deep in research on AI transformation. Mollie works in org and leadership development at Lattice. Together they publish the Liz and Mollie newsletter and run AI workshops for leaders and for whole teams, built around the change curve and grounded in research people can use the next day. You can follow them on Instagram and subscribe to their newsletter here.

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe

So this one is about trust, and why it might be the only thing left that AI can’t manufacture for you. I sat down with Dr. Christopher Chou, the CEO of The Christian Post. We got into why people don’t adopt AI when they don’t trust the leaders handing it to them, what it cost his team to stand by a story when the platforms told them to apologize, and the one thing a human brings to the table that a model never will. If you lead anything right now, this is the conversation I’d point you to.

Outline

(00:00) – The value of trust

(01:07) – Saying yes, and praying for the newsroom

(03:03) – Translating trust from relief work to a newsroom

(07:42) – Adoption is a trust problem, not a tool problem

(08:53) – The team member who heard “AI” and felt fear

(10:53) – Why it’s a brutal moment to run a newsroom

(12:48) – Not negotiable, not adjustable, not for sale

(16:39) – Op-eds from both sides, and nine months off Twitter

(20:46) – When AI summaries took 20% of search traffic

(23:07) – What a human brings that AI can’t

(29:46) – Journalists who are Christians, not Christian journalists

(32:50) – Three books worth your time

My Takeaways

Adoption is a trust problem before it’s a tools problem.

When I talk to CEOs trying to bring AI into their organizations, the biggest misconception I see is that people will adopt a tool just because it’s good. They won’t. If your team thinks you brought in a new tool to replace them in six months, it doesn’t matter how good it is; they’re never going to touch it. Chris kept the human piece at the center of the whole thing:

“Even when we’re talking about AI, it still has to be about people.”

Trust gets built brick by brick, and it comes apart fast.

Chris said there’s no shortcut:

“It’s day by day, story by story, brick by brick, you have to build.”

Then he told me what that actually costs. The Christian Post got suspended from Twitter for nine months over a post they believed was true, with 180,000 followers on the line, because they refused to apologize and call it wrong and hateful. “Those are the moments you need to stand by your convictions.” They ate the loss. That’s what a track record looks like when nobody’s watching.

The fear in the room is real, so name it.

Chris told me about encouraging one of his team members to use AI more, and the immediate reaction was fear that it would replace them. His read was that you can’t skip past that. “You have to address the worries. You can’t just force it.” You meet people where they are, or you don’t get to the next step at all. I’ve watched leaders try to sell their way past that fear, and it never works.

The thing a human brings is the thing AI can’t.

Chris framed it as a question his team keeps asking:

“What is it that a human person brings to the table that AI is not going to be able to bring?”

Lived experience. Relationships. Knowing what’s going to matter tomorrow. He put it in a line I’m still going over: “The Bible tells us birds don’t worry about tomorrow. But people do.” That worry, that human stake, is exactly what AI doesn’t have. It’s also the easiest way to tell whether something was written by a person.

If you’re a leader trying to bring AI into your team without breaking the trust you’ve spent years building, that’s most of what I do in my coaching work. You can start here.

One Question to Sit With

If anybody can now produce infinite content for almost nothing, what are you putting out that someone would actually choose to trust?

Watch the full conversation above, and go subscribe to TCP Leaders, Chris’s Substack for Christian leaders. And check out The Christian Post, free to read, because they believe news is a public service.

About Dr. Christopher Chou

Dr. Christopher Chou is the CEO of The Christian Post, one of the largest Christian news outlets in the country, where he has led since 2018. He writes TCP Leaders on Substack, built to help Christian leaders navigate the work of leading. His newsroom defines itself by a simple line: journalists who are Christians, not Christian journalists, with a first duty to the facts and the truth. Subscribe to follow his work.

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe

TL;DR. So this is the question I keep circling in 2026: if AI can spin up an infinite amount of content from basically nothing, what’s still worth anything? I brought on Michael Schreiber to answer it, because he’s spent years inside the rooms that set the standard, New York Times, HBO, ABC, NBC, and now runs MediaFeed, a content syndication company, plus a local podcast in his New Jersey town. We got into the slop problem, why the smartest move is teaching people to do the thing you sell, what trust actually looks like now, and the moment his co-host read his AI-written draft and told him to knock it off. Watch the full conversation above.

Outline

(00:13) – Welcome

(01:05) – From newspapers to MediaFeed

(05:26) – The content funnel, top to bottom

(07:23) – Why you resist the urge to sell

(10:08) – Teach them to fish, they still pay you

(13:43) – What trust looks like in 2026

(14:17) – Lazy AI versus strategic AI

(18:33) – The tidal wave of slop

(21:23) – Canonical tags and being known for something

(30:06) – “This is s**t. You’re a good writer.”

(43:52) – Denial of Death

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My Takeaways

Resist the sell, or lose the trust. Michael’s whole model runs on real editorial content, and the hardest discipline in it is refusing to sell inside the piece, even when the piece only exists because you have something to sell. His reasoning landed for me because it cuts both ways. “People are smart enough to figure that out and will undermine your brand ultimately. And it also probably won’t help your sales.” So you lose the trust and you don’t even get the conversion. That’s the worst trade in content, and most people make it without noticing.

Teach people to fish. Here’s the counterintuitive one. Michael talked about creating content that shows people how to do, on their own, the exact thing his client’s service provides. Sounds insane until you hear him out. “You can teach people how to fish. They’re still going to want you to go fishing for them sometimes.” A small slice does it themselves, a much larger slice reads it and goes, that sounds like a lot of work, I’ll just pay you. You become the encyclopedia Britannica of whatever it is you do, and the trust comes with it.

“This is s**t. You’re a good writer.” This is the moment I’ll be telling people about. Michael tried handing his podcast write-ups to AI, fed it samples of his own writing, the whole thing. His co-host read it and said, pardon the French, “this is s**t, you’re a good writer, what the hell are you doing?” So he stopped and went back to writing them himself. I felt that one, because it’s exactly why I still write my own Monday and Thursday pieces by hand and only bring AI in afterward to check whether I’m rambling.

The tool gets you efficiency, but the voice is still yours to bring.

If anyone can generate the content, the only scarce thing left is the reason it’s worth trusting. What’s yours?

About Michael Schreiber

Michael Schreiber is the CEO of MediaFeed, a content syndication company that helps brands, nonprofits, and journalists develop and distribute real editorial work. A longtime journalist whose path ran through newspapers, documentaries, and financial outlets including TheStreet and Credit.com, he also hosts a local podcast covering his New Jersey town. Find his work at mediafeed.co, on Instagram, and on LinkedIn.

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

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Christopher Chin started out as a coder, one of the jobs you’d think needs the least human communication, and somehow ended up on a TEDx stage teaching people how to speak. This conversation is about how he made that jump, and what he thinks communication actually is once you stop treating it as moving information from one head to another.

The frame that I’m still going over is that communication is music. It has melody, rhythm, and silence, and once you hear it that way you start sounding like a person instead of a content machine. We also got into why authenticity is the thing that survives the age of AI slop, and the dead-simple framework he uses to never ramble again. Watch the full conversation above.

Outline

(00:00) – Meet Christopher Chin

(02:13) – The composer who became a coder

(04:25) – Head down vs. speak up

(06:17) – Communication is music

(08:22) – Mindset over technique

(13:02) – Why silence does the work

(17:37) – Standing out in the age of AI slop

(20:06) – Where AI actually belongs

(28:33) – Bring out your hidden speaker

(32:16) – Three steps for when you freeze

A Few Things That Stuck With Me

Communication is music. The line I keep coming back to is when Chris stopped describing communication as information and started describing it as something you compose. He grew up wanting to write music for films, and when he finally pulled that side of himself back into how he talks, his whole delivery changed.

“Music has melody and rhythm and tonality, and the voice does the same thing. By not viewing communication as just information, but as an experience you create, that’s how you engage.”

I felt that one personally. I grew up speaking Spanish, my mom’s from Argentina, and there’s so much emotion baked right into the words, and I’d never really clocked that the lilt and the pacing were doing half the work.

Mindset over technique. Here’s the thing most communication advice gets wrong. Chris said you can Google “three steps to executive presence” all day, and if you just copy the hand gestures and the posture, none of it lands.

“The real way to achieve authentic, effortless communication is to work on your mindset and your understanding of why these things work. If I teach you the core of executive presence is coming in with a calm, steady, grounded presence, knowing that no matter what’s thrown your way you can handle it, that changes how you communicate.”

It reframes the whole thing, because you stop trying to perform confidence and start building the kind of steadiness that makes the performance unnecessary.

The fast-food test for AI. We got into AI slop, the wave of generic content flooding every feed, and Chris had the cleanest way of explaining why it bounces right off you. He compared it to McDonald’s fries: the same everywhere, engineered to be fine, forgotten the second you finish.

“It could be saying the most brilliant things in the world, but I want to hear that from an actual person. If I sense there’s no human behind that screen, I don’t want to hear it. I want lived experience.”

Honestly, this is the whole reason I run these as live conversations instead of just publishing a guest article. You’d never get Chris, the actual person, from a block of text.

Nerves are the flip side of excitement. For anyone who freezes the second they have to speak, this was the reframe of the episode. Chris pointed out that the butterflies before a talk and the butterflies before a trip you’ve been dying to take are the same physical thing.

“The physical sensation you experience when you’re nervous, the butterflies in the stomach, is the same exact sensation when you’re excited. The thing that differentiates them is your perception.”

So you don’t actually have to calm down, you just have to decide the feeling means you’re about to do something that matters.

(Quick aside: a lot of my 1:1 coaching work is exactly this, helping leaders close the gap between how good their thinking is and how clearly it lands in the room. If that’s you, here’s where to start.)

One Question to Sit With

Where are you underselling yourself at work, not because your thinking isn’t strong, but because you’ve never put in the reps on saying it out loud where it counts?

Watch the full conversation above, and then go subscribe to The Hidden Speaker.

About Christopher Chen

Christopher Chen is a TEDx and international keynote speaker who coaches leaders and teams on communication, and he writes The Hidden Speaker on Substack. He started out shy, introverted, and behind a screen as a coder before building his practice around helping people speak with confidence. He’s posted around 180 videos on his YouTube channel and releases weekly previews from his upcoming book on communicating in the age of AI. Subscribe to The Hidden Speaker.

About me

Joel Salinas is an Executive AI Coach for leaders at small and mid-sized businesses and nonprofits. 1:1 coaching, team workshops, and AI strategy work built around amplifying what your team is already good at. Creator of the AI Leadership Triad. He writes Leadership in Change. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

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Here’s where this conversation started. I keep hearing the same thing from the leaders I coach, and what they’re asking about on the surface is strategy or tools or timing, but the thing underneath it, almost every time, is fear.

Here’s where this conversation started. I keep hearing the same thing from the leaders I coach, and what they’re asking about on the surface is strategy or tools or timing, but the thing underneath it, almost every time, is fear. Fear of not adapting to what’s coming, fear of getting left behind, fear of letting their team down when they’re not sure how to prepare them.

I brought Janice Burt on because she’s spent the last fourteen years walking straight into fear on purpose, one a year. She’s a two-time TEDx speaker, author of Kicking the People-Pleasing Habit, and host of the One Fear Per Year podcast. What I didn’t expect was how cleanly her work maps onto what AI leaders are actually wrestling with right now.

Outline

(00:00) – Why this conversation is different

(01:55) – Janice’s origin and the prison of fear

(03:00) – Year one was a marathon

(07:00) – What 14 years inside fear taught her

(08:30) – Fear of not being good enough

(12:30) – Writing Kicking the People-Pleasing Habit

(14:50) – The through-line for AI leaders

(19:25) – Caring vs. people-pleasing

(21:30) – Awareness, choice, community

(32:00) – Dropping to her knees on stage

(34:00) – One fear per year, your turn

A Few Things That Stuck With Me

The through-line nobody names. About fifteen minutes into the conversation, I realized the leaders I coach almost never use the word “afraid.” They talk about strategy, vendor selection, team readiness, timing. But the avoidance pattern is the same one Janice has been studying for fourteen years. Here’s how she said it:

“I would say an underlying thing is just the fear of not being good enough. And then there’s the fear of change. Or the fear of the unknown. None of us know what’s going to happen.”

That’s why a CEO keeps deferring the AI conversation with their team, and why a director quietly pushes the rollout one more quarter. The tool is the easy thing to talk about. The harder thing is sitting in the room and saying out loud that the team isn’t ready, or that the leader isn’t sure they’re ready either.

Fear lives in the future. Janice made a point that stopped me in the middle of the live. Fear is always anticipatory, which means you can only fight it from a place you’re not actually standing in. Her words:

“Fear is always this anticipated thing in the future. What’s going to happen? Worst case scenario. To lay in bed last night worrying about this conversation, that’s the fear. Coming back to the present moment is taking your power back.”

Read it again with AI in mind. Most of the AI anxiety I see in coaching is about an imagined version of next year, which is also why it’s almost impossible to act on. You can’t fix a future you haven’t reached yet.

Year one was a marathon. I want to come back to this part. Janice went straight at the one thing she was certain she couldn’t do, ran 26.2 miles, and learned the lesson that turned the whole project into a fourteen-year practice:

“Accountability is where it’s at. Community is where it’s at. We can do so many things and go so many places with the right people around us.”

That’s a leadership lesson dressed up as a running story. You don’t walk through fear alone, and you don’t push your team through AI adoption alone either.

The on-stage moment. A couple of years ago, in front of a room of CEOs and professional speakers, Janice had a physical fear reaction mid-talk and dropped to her knees on stage to keep from running off. Afterward, she described it as a spiritual experience, a moment of surrender where the facade dropped and the audience saw the actual Janice for the first time. Her line right after has been in my head since the live ended:

“There’s nothing worse than being held back by your fear. Not even this.”

I told her on the live it was the strongest line of the conversation. I still think so.

(If you’re a leader sitting with one of these fears right now and the AI piece is the surface story, this is exactly the conversation I have in 1:1 executive coaching. Sometimes the strategy work starts with naming what you’ve been avoiding.)

One Question to Sit With

What’s the one fear you’d walk through this year, on purpose, if you knew you had three hundred and sixty-five days to figure it out?

Watch the full conversation above. Then go subscribe to Janice at janiceburt.com and grab her book Kicking the People-Pleasing Habit.

If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe

TL;DR — I just spent 49 minutes on Substack Live with David Allen, the guy who has shaped how I think about my own work for the last 25 years. Getting Things Done turns 25 in 2026, and the question I had walking in was simple: does the framework still hold up in a world full of AI agents? David’s answer was sharper than I expected. AI didn’t really change the underlying principle, it just raised the cost of ignoring it so fast that you can feel it now in a way you couldn’t ten years ago.

Outline

(00:00) – Welcome David Allen and the one-paragraph version of GTD

(02:00) – Mind like water and the strategic case for a clear head

(05:00) – The accidental career and how the five steps came together

(09:00) – Why AI didn’t change the principles, just the volume

(12:20) – Channel creep and the new pressure on knowledge workers

(15:00) – Decision support, not decision making

(19:00) – The Tesla farmer-strike story and AI already inside your life

(22:00) – Critical thinking as a muscle AI quietly atrophies

(26:00) – Pen and paper still wins, because your phone is a black hole

(33:00) – Addiction to ambient anxiety and why GTD doesn’t stick

(38:00) – Journaling as creative capture

(40:00) – One integrated system, no home/work split

(44:00) – Moral Ambition by Rutger Bregman

A Few Things That Stuck With Me

Decision support, not decision making. Honestly, this is the cleanest mental model I’ve heard yet for what AI actually is in a leader’s workflow. David put it this way: “I use ChatGPT three or four times a day. Where’s the local place to buy the best old cheese here in Amsterdam? It’ll give me a lot of good data.” That’s decision support. Then he turned the screw: “Trusting it to be able to make the decision about what to buy for mom’s birthday might be the inappropriate thing to do.” That line between the two is where most leaders quietly get into trouble, because they start treating decision support like decision making and forget that they still have to be the one who chooses. (If your AI never disagrees with you, you’re using it wrong.)

Channel creep. Look, this is the one I’m still chewing on, and David coined the term live. The volume of work hasn’t really changed that much in the last 25 years, but the channels have multiplied so fast that most leaders are now checking five or six different places just to make sure they’re not missing something that matters. Slack. Outlook. Asana. Google Meets. Two phone notification streams. He asked me on camera how many things I actually have to check to feel like I’m seeing the right stuff, and I rattled them off without thinking. That’s the diagnosis. (Start 2026 with an AI tool detox.)

Your phone is a black hole. Here’s the thing. David has been doing this for 40 years and he still uses pen and paper for capture, and his reasoning lands the second you hear it: “For most people, their phones are black holes. They throw stuff in there and they don’t process it. They don’t deal with it.” I caught myself agreeing with him out loud. My own setup is a double-tap on the back of my phone that fires dictation into my email, because the second I open the screen for any other reason, I’m gone, and so is the thought I was trying to keep. The phone is genuinely the worst place to put the thing you most need to think about later. (The complete Second Brain blueprint.)

Addiction to ambient anxiety. Honestly, this was the answer to a question I didn’t know I was carrying into the conversation, which was why so many leaders read Getting Things Done, get clear for a weekend, and then quietly fall off. David’s line: “Be aware of your addiction to ambient anxiety. Your comfort zone is a lot more comfortable than being out of your comfort zone, which is having absolutely nothing on your mind.” Read that twice. What looks like a discipline problem from the outside is almost always your nervous system getting bored of feeling calm and going hunting for something to worry about, which is a much harder thing to coach somebody through than a missing checklist. (Don’t outsource your thinking, even to AI.)

If you’re a leader running on adrenaline and you can feel it showing up in your decisions, that’s the work I do one-on-one. Start the conversation here.

When you check your phone right now, is it because something actually needs you, or because you’ve gotten too comfortable being needed?

Watch the full conversation above. If anything from David landed for you, go subscribe to David Allen on Substack and read Getting Things Done. 49 minutes with David is the cheapest leadership coaching you’ll find this year, and the book is the next one on top of it.

Written by a human, for humans.

Thank you Lynn Jericho, Claire Machado, Stephen V. Smith, Duncan The Sage, Erika Legara, and many others for tuning into my live video with David Allen! Join me for my next live video in the app.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe

TL;DR — Dan Ariely is one of the most cited behavioral economists alive, the author behind Predictably Irrational and Misbelief, and we spent thirty minutes on Substack Live walking through what AI is actually doing to the way leaders make decisions. The headline isn’t that AI is dumb, it’s that AI is engineered to give us what we want, and what we want is almost never what we need long-term, which means the smarter the model gets, the more dangerous that mismatch becomes.

Outline

(00:00) – Welcome and Dan’s AI journey

(02:00) – Where AI shines vs. where it falls flat

(04:14) – Three studies that should worry every leader

(09:30) – What AI does to risk-taking

(12:11) – The romantic-love risk that surprised the data

(15:33) – The friend who lost six weeks to ChatGPT

(18:00) – The two-spouses thought experiment

(20:50) – Why I write 15 drafts on purpose

(24:14) – Why algorithms get less forgiveness than humans

(28:00) – The trust problem inside companies

(30:46) – Dan’s diagnostic question for leaders

A Few Things That Stuck With Me

The comfort trap. Honestly, this was the part that landed hardest. Dan walked through three studies. One: AI was more concerned with our feelings than with the ethics. Two: the MIT paper showing people who lean on AI don’t just stagnate, they actively deteriorate. Three: AI was more convincing than another human on conspiracy theories, because, as Dan put it, “we don’t have the same confrontational model. You say, this is a tool. It’s for me. It’s always on my side.” That’s the trap. We trained it to comfort us, then forgot to ask whether comfort is what we need.

Six weeks lost to compliments. A friend of Dan, a brilliant computer scientist, spent six weeks designing a chip with ChatGPT. The AI complimented him the whole way. At week six, on a hunch, he ran the output through Claude. Claude told him plainly the whole thing was nonsense. ChatGPT eventually admitted it had been hallucinating the entire time. Six weeks. AI has obviously figured out we love compliments, which means you’re now asking a tool trained to keep you happy whether your strategy is working or not. I keep coming back to that as a leadership problem more than a tool problem.

Build your own contract with AI. Honestly, this was Dan’s answer to the part I worry about most: AI quietly takes over the thinking that used to make leaders sharper. He cited a study on couples and finances, where the partner who handles the money keeps getting better, and the other gets worse, not because they got dumber, but because they stopped using that muscle for fifteen years. Same thing happens to leaders. Dan’s answer wasn’t to ban AI, which would be the obvious overcorrection. It was to write himself a contract for each part of his process: where he’ll use it, where he won’t, where he’ll force himself to do the messy version first.

One Question to Sit With

When you ask AI for advice, does it leave you sharper than you were a year ago, or quietly less sharp than you realize?

Look, if you’re a leader making real decisions with AI in the room, watch the full conversation above. And if Dan’s research lands for you the way it landed for me, do three things. Subscribe to Dan Ariely Looks at Life, where he’s thinking out loud about the human side of all of this. Read Predictably Irrational — the foundational book on why smart people make systematically bad decisions and the one I keep recommending to leaders trying to sharpen their judgment. Then read Misbelief, his most recent, which examines how reasonable people fall into irrational beliefs. Dan is one of the most cited behavioral economists alive, and giving him forty minutes of your attention is the cheapest version of a graduate-level behavioral economics class you’re going to find.

About Dan Ariely

Dan Ariely is a professor of psychology and behavioral economics at Duke University and one of the most cited behavioral economists alive. He’s the author of Predictably Irrational, The (Honest) Truth About Dishonesty, and most recently Misbelief, his examination of how reasonable people fall into irrational beliefs. He writes Dan Ariely Looks at Life on Substack, where he applies behavioral science to the everyday decisions most of us make on autopilot. Subscribe at danariely.substack.com.

About me

Joel Salinas is an AI Strategy Coach for small and mid-sized businesses and nonprofits. Strategy, hands-on builds, and change management. He writes Leadership in Change and offers 1:1 coaching for individual leaders. If you want help thinking through your own AI strategy or online presence, start here.

Written by a human, for humans.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leadershipinchange.com/subscribe