The Beautiful Mess · Product & Work
TIER 4 Thu, 30 Apr 2026 21:42:24 +0000
...and the journey of self-awareness we are all going through ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ | | ---|---|--- | | | Forwarded this email? Subscribe here for more --- # TBM 420: The AI Playbook Puzzle ### ...and the journey of self-awareness we are all going through | | John Cutler --- | Apr 30 --- | --- --- | | | --- | | --- | | --- | | --- | | READ IN APP --- | | ---|---|--- I'll be hosting a small TBM pop-up meetup next week in San Francisco (financial district) on Wednesday, May 6th. Let me know if you're interested. I might be able to find a sponsor. **The theme: multi-player mode AI.** SF TBM POPUP ## The AI Playbook Everyone wants tactical advice on "how to AI." But I keep arriving at the same conclusion. We already know a lot, but struggle to explain it in the new context. We don't know a lot, but aren't willing to sit with that uncertainty. And the biggest threat is identity threat. Most of the "tactical advice" I see either reinforces things we've known are broken for decades, or reinvents what we've known works under a new name. Very little of it is genuinely inspired or thoughtful. But the "nothing has changed" argument is also a safety blanket. Consider things we already knew worked/didn't work: 1. Writing strategy as a single-player activity and then flattening it into a four-pillar slide with no guiding principles, no decision heuristics, and nothing experienced people could latch into was always a bad idea. Before AI, after AI, doesn't matter. If your strategy doesn't help people make decisions, it's not a strategy. 2. PRDs as a static document were always a bad idea. Before AI, at best, a static PRD was a one-time snapshot of thinking. It had some value as a forcing function to think things through, but decayed immediately. Teams confused the document with the understanding. 3. Pre-mortems were a thing way before AI. "Imagine this failed. Why?" Good teams always found ways to stress-test their thinking before committing. Now AI can be another adversary at the table, poking holes right alongside humans. Not replacing the conversation, but making it sharper. 4. Prototyping was/is a thing, way before AI. The instinct to make something tangible and react to it is old. Designers have always gone to great lengths to prototype. They'd use paper mockups, clickable wireframes, and throwaway code. Seeing and responding beats speculating in a doc. (See below where I acknowledge that context is a big factor when it comes to _good_ and _bad._) ## Bad and Good Which leave us with two buckets: 1. **AI makes bad ideas worse.** AI just lets you generate these faster and with more false polish. Same bad idea, same trap, higher velocity. And the leadership pressure to check the AI boxes right now is intense -- which only accelerates the rush to automate broken things. 2. **Good ideas before AI can be supercharged with AI** : Living documents, living context, prototyping as shared understanding, continuous co-design, outcome-centricity, "think big, work small," making your meetings worthwhile, supporting real periods of divergence and convergence. These were always the right instinct. Here's a real example. I recently looked at a team's "AI transformation plan." This team was already deep in broken patterns: time allocation as a proxy for capacity, story points as a broken measure of progress, gates everywhere in their SDLC, team members siloed by function despite being nominally on the same team, ship and forget, massive batches. Their AI plan? A "Governance Agent" to enforce the gates. A "prototyping step" bolted into the existing stage-gate process. AI-generated PRDs. Every broken mental model, now with AI. Not a single thing in the plan questioned the underlying model. It was the checkbox problem in its purest form. Was this fear, a genuine lack of imagination, a smart response to constraints (see below), or something else? I don't know. But AI was certainly going to make a struggling thing worse (except for some promotions). Compare that to a team I've been working with through Dotwork. They used AI to automate the repetitive stuff that was creating friction: status updates, keeping shared context current, summarizing decisions so people didn't have to re-explain things in every meeting. They used it to prep for interactions, not replace them. Better 1:1s. More focused design reviews. Less "can you catch me up?" and more "here's where I think we should push." They weren't adding AI to their process. They were using AI to have better conversations and spend more time on the hard, creative, judgment-heavy work. ## The Unimagined. The Meta-Skill Actually, it is four buckets. Let's add: 3. **There 's the stuff we haven't imagined yet.** Beyond fixing old bad ideas and supercharging old good ideas, there may be entirely new workflows, practices, and ways of working that don't map to anything we did before. Things that only make sense when AI is in the loop. Some practices that were genuinely "good" were only good because of a limitation (cost, speed, tooling, access). This is also a huge emotional journey. When a practice you mastered needs to be retired, it's not just a workflow change. It's a huge and unsettling professional identity hit. People built careers and reputations around these things. Letting go is real. 4. **And a meta-skill:** Yes, bad vs. good is an oversimplification. Practices--good, bad, necessary--were always contextual. Something generally "bad" (like a static PRD) might have been the best option available given the constraints (team size, tooling, org culture, regulatory environment). Understanding why something worked or didn't in a specific context is a skill unto itself. Understanding the influence of context on practices has always been a superskill. And it's even more important now. When everything is shifting--tools, constraints, what's possible--the people who can read context, sense what actually matters, and adapt accordingly will thrive. That's not a new skill. It's the skill that always separated great practitioners from people following playbooks. The difference is that the pace of change now punishes rigidity faster and rewards adaptability sooner. I have a background in music, and I watched this exact thing play out with DAWs. Same four buckets. Bad producers made worse music faster. More polished, more lifeless. Good producers found new creative territory they couldn't have reached before. Whole genres appeared that only make sense because of DAWs. And the people who really understood _why_ certain techniques worked? They adapted. They thrived. But I also watched talented people go through real identity crises. Some came through it. Some didn't. So four buckets: bad ideas AI makes worse, good ideas AI can supercharge, genuinely new things we haven't imagined yet, and the meta-skill of reading context that ties it all together. ## The Crunch The irony is that the kind of thinking you actually need right now, systems thinking, the ability to shift between elevations, the ability to challenge your own assumptions, metacognition (of yourself, of others, of computational systems), is exactly what's being discounted the most in the current environment. Everyone wants the tactical "how do I prompt better" and nobody is investing in the deeper cognitive work that determines whether you're even solving the right problem. And there's a trio of identity pressures making this worse, especially for leaders. 1. One: projecting a new identity as "AI-forward" or "AI-native" because the moment demands it. 2. Two: not actually being able to handle the personal identity shift underneath. 3. Three: applying the same outdated models while wearing the new label. So you get people performing transformation while resisting it internally and executing it with the old playbook. All three happening at once in the same person. ## The Three Traps I see three traps (I used a COM-B behavioral diagnostic skill I built for AI to generate the assessments below, which is itself a good example of encoding domain knowledge into a reusable tool): ### Amplify Bad First, completely ignoring what good looked like and just amplifying whatever outdated practices you had. It's like you lay out how you work and just add a checkbox: "now use AI." 80% of the time I check out someone's "I built my chief of staff" or "second brain," I see broken mental models now running on AI. Faster bad is still bad. _(COM-B: The motivation here is real but shallow. It 's pressure and FOMO, not genuine understanding. The deeper problem is capability: their mental model of "how work should be done" is already wrong, and they don't know it's wrong. The brokenness of existing practices has been normalized for so long it's invisible. Easy AI tooling just amplifies all of this.)_ ### Identity Threat Second, for experienced practitioners, believing so strongly in your ability to detect timeless principles that you sit back and wait for the dust to settle. The trap is that confidence in your own judgment becomes the reason you don't become an active part of shaping the reinvention. We need you more than ever. _(COM-B: They have the capability. The opportunity is there. But motivation is blocked by identity. "This isn't who I am" or "I'm above this." When it feels hard, they read that as confirmation it's not for them, rather than a sign it matters. Overconfidence in their own judgment hides blind spots. And the emotional cost of admitting "I need to relearn" is high when your whole career says you're the expert.)_ ### Avoiding It Third, not doing the work to understand what AI actually does and how it works. Without that, you can't apply it to good ideas, you can't recognize when a good idea was really just a constraint, and you definitely can't explore what's genuinely new. _(COM-B: Capability is the bottleneck. They don 't have a working mental model of what AI actually does, and there's no obvious on-ramp to start building one. The learning feels expensive relative to an uncertain payoff, so it keeps getting deferred. But without this foundation, all the motivation and opportunity in the world can't help. You can't apply what you can't comprehend.)_ _**Paradox:** The more certain someone sounds right now, the more likely they're pinning everything on existing mental models. That can be good when the models are sound. It can be dangerous when the models are the problem._ ## The Journey The journey, if you're honest with yourself, sounds something like this: 1. "Something is happening here. I'm not sure what to make of it yet." 2. "Okay, I think I see how this fits. Let me apply what I know works." 3. "A lot of what we were doing was never actually good. We just normalized it." 4. "Some of what I was doing wasn't actually good either." 5. "Some of what was genuinely good only worked because of a constraint that's gone now." 6. "If the things I built my career on don't apply the same way... who am I in this?" 7. "I don't know what this looks like yet. I need to be in it to find out." 8. "The skill isn't having the answer. It's staying in motion while everything shifts." ## Conclusion So where does this leave us? * Some things were always broken. AI makes them worse faster. * Some things were always good instincts. AI can supercharge them. * Some things only make sense now that the old constraints are gone, and we haven't even begun to discover most of them. * And the meta-skill that holds it all together, reading context, challenging your assumptions, knowing when to apply what, has never mattered more. The identity pressures are real. And nobody has the playbook yet, no matter how confidently they're selling one. The way through isn't to cling to who you were or to perform who you think you're supposed to become. It's to stay in the work, let your identity shift with what you're learning, and be open to the possibility that the most important practices haven't been invented yet. Five questions worth sitting with: 1. What practice on your team exists only because of a constraint that no longer applies? What would you do instead if you started fresh? 2. When you look at your "AI strategy," how much of it is automating the existing model vs. questioning it? 3. What part of your professional identity are you protecting that might be keeping you on the sidelines of the reinvention? 4. If middle management skepticism is rational, what does that tell you about how the transformation is being led? 5. What would it look like to invest in systems thinking, metacognition, and contextual judgment as seriously as you're investing in AI tooling? You're currently a free subscriber to The Beautiful Mess. For the full experience, upgrade your subscription. Upgrade to paid --- | | | Like --- | | Comment --- | | Restack --- (C) 2026 John Cutler 548 Market Street PMB 72296, San Francisco, CA 94104 Unsubscribe