"Coaching in the age of AI" sounds straightforward—until you ask what it actually means. Niko asked AI and got 20 different interpretations. Are we coaching leaders to use AI? Coaching AI systems themselves? Being replaced by AI coaches? Leveraging AI to become better coaches? The answer is yes to all of them—and therein lies the problem.

Niko anchors a conversation that refuses to pretend coaching will stay the same. Joining him are Mark, who's discovered his 15 years of coaching skills are more valuable in the AI world, not less, and Ali, bringing his characteristic skepticism about what "coach" even means anymore. With AI tools now capable of asking great questions, maintaining perfect consistency, and never forgetting a conversation, the hosts confront what remains uniquely human about the coaching relationship.

Ali frames the stakes bluntly: "Either you gonna become a good question asker in the moment... or you're an expert in something which leans more towards the teacher profile... or you're going to be irrelevant." AI can already ask triggering questions that help people think and contextualize. But can it interject at the right moment? Can it read the room when someone's arms are crossed—and know whether that means they're closing off or focusing deeply?

Mark cuts to what he considers foundational: "The instant that you are not treated as a vault, your ability to coach effectively is gone." When AI enters a coaching conversation—transcribing, analyzing, mining for insights—what happens to the psychological safety that makes coaching work? Niko's response is visceral: "It was the first time in my life I said no to a technology innovation."

Yet Mark also grounds the theoretical in reality: use AI to summarize past coaching conversations, identify patterns across sessions, prepare better for calls. "Really practical, really down to earth. No science fiction required."

The episode doesn't declare coaching dead or triumphant—it maps the territory where trust, technology, and human connection collide. For coaches wondering what to invest in and what to release, this conversation offers something rarer than answers: honest uncertainty from practitioners navigating the same questions.

Three trainers who've collectively spent thousands of days in the room—physical and virtual—sit with a question that's been nagging at them: what happens to training when AI shows up?

Mark opens with a scenario. Your trainee texts you at 11pm, panicking before their first PI Planning. You're asleep. They muddle through. But imagine they had an AI buddy from the course—one that knew the context and answered instantly. Relief that they got help? Or quiet terror that you just became optional?

The conversation moves through what AI might extend and what it might erode. Stephan shares how his AI agent reframed his role: stop being a "knowledge dispenser" and become a "wisdom cultivator." The content isn't the hard part anymore. The hard part is helping people navigate what they don't know when they're in the thick of it. Ali picks up the thread but surfaces what's missing: AI can't interject. It can't say "whoa, stop—we need to zoom in right here." Chatbots are polite companions. Trainers sometimes need to be challengers.

They explore practice and simulation—if someone rehearses a retrospective 50 times with an AI before trying it for real, do they arrive with justified confidence or false confidence? AI is infinitely patient in ways humans aren't. And Ali raises his "doomsday" scenario: if everyone privately asks ChatGPT instead of raising their hand, do we lose the brave question? The one that cracks things open for the whole room?

The James Bond jiggle—courtesy of absent Niko—produces unexpected depth. Stephan casts Q as the on-the-job AI (efficient, tool-focused, never teaches why) and M as the classroom trainer. Mark chooses Blofeld for AI—omnipresent, enabling, but creating dependency—and Daniel Craig's scarred Bond for the human trainer who learned everything the hard way.

The episode lands on two complementary edges: Ali's conviction that AI extends the trainer rather than replaces them, and Mark's sharper take—"If you think your job is to teach people what's on the PowerPoint slides, AI is going to replace you." Stephan's closing haiku captures it: "AI gives answers fast, but struggle builds the muscle—mirror, not rescue."

Does AI make Solution Intent obsolete—or finally give it the living, breathing life it was always meant to have? Four practitioners who've never seen anyone "install" a solution intent explore whether AI creates dangerous rigidity or unprecedented opportunity.

Stephan anchors a conversation that begins with confession: Ali has rarely seen solution intent used as intended. Mark reframes it entirely—not as something to install, but as "a map, a mesh, and a mindset." Niko brings his alternative name: "product memory"—the bus factor savior. Together they navigate Stephan's opening paradox: AI can accelerate solution intent's creation while threatening to make it obsolete or dangerously rigid.

From Automation to Augmentation

The conversation pivots on Mark's sharp distinction: AI as sparring partner versus AI as document generator. "If you let AI generate 80% of your solution intent, the only person accountable for that much rubbish is you." The hosts explore using AI to challenge thinking—"point out the three biggest flaws," "what assumptions should I test?"—rather than producing voluminous output nobody reads.

Intent as the New Source of Truth

Mark surfaces a paradigm shift: "We're moving from code is the source of truth to intent is the source of truth." Citing Llewellyn Falco's hackathon where the team would have chosen specs over code, the conversation explores what happens when specifications become more valuable than what they produce.

Optimizing the Wrong Percentage

Are we optimizing the 8-12% of the value stream where AI writes code, while missing where solution intent actually lives? Mark argues its natural home is the beginning (exploring options) and the end (tracing what was built against intent). Niko is blunt: "We are optimizing the wrong percent."

The Monster Network

Niko offers a memorable framing: Is your solution intent a Jurassic Park monster—one terrifying monolith? Or Monster Inc.—a network of smaller, friendlier creatures you can navigate? The latter is what AI might help create: an entry point where some elements are augmented while others remain human-crafted.

Highlight Moments:

On accountability, Ali cuts to the heart: "There is a need for a single, ringable neck." Delegation to AI doesn't eliminate accountability—it concentrates it.

Niko's warning lands hard: "We have to be careful if we outsource everything to AI to not lose the skills of critical thinking."

The prison jiggle produces unexpected depth—and Niko's meta-observation that AI couldn't have produced these culturally-contextual answers proves his point about what machines still can't do.

Closing:

Stephan closes with a haiku: "AI writes so fast, yet wisdom needs time to grow. Keep options open." The takeaway isn't whether AI should power solution intent—it's that the intent must live and breathe. Producing more documentation nobody reads? That's not solution intent. That's just rubbish with your name on it.