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  • Executive Coaching Is Expensive. Daily Accountability Doesn’t Have to Be

    Executive Coaching Is Expensive. Daily Accountability Doesn’t Have to Be

    One of the most useful things for personal productivity isn’t a to-do app.

    It isn’t a new notebook, a better calendar, or a more elaborate morning routine either.

    It’s having someone ask good questions on a regular basis.

    That’s the real value of executive coaching. A good coach helps you decide what matters, pushes back when your priorities drift, notices your patterns, and creates enough accountability that you actually follow through.

    The problem is that real executive coaching is expensive. Really expensive.

    For a lot of founders, operators, and ambitious people working on their own stuff, it’s hard to justify spending thousands of dollars for occasional calls, even if the upside is obvious.

    So I started with a simple question: what if AI could deliver even 20% of the value?

    Not by pretending to be a perfect human executive coach. Just by handling some of the repeatable parts well enough to matter.

    What I actually wanted from a coach

    I wasn’t looking for motivational speeches.

    I wanted help with the things that quietly break productivity over time:

    • picking the wrong priorities for the day
    • letting uncomfortable tasks slide for too long
    • spending time on interesting work instead of important work
    • losing sight of quarterly goals in the chaos of a normal week
    • repeating the same self-defeating habits without noticing

    A strong coach is useful because they create a rhythm around all of this. There’s a cadence. A check-in. A follow-up. A little bit of pressure. A little bit of perspective.

    That seemed reproducible.

    The first step was research, not code

    Before building anything, I asked AI to go research executive coaching properly.

    Not the vague internet version. The actual practice.

    I had it pull together material on:

    • coaching best practices
    • common question frameworks
    • behavioral science and accountability research
    • how executive coaches structure sessions and follow-up
    • the difference between good coaching and generic advice

    What came back was a much more structured picture than I expected. The useful parts of coaching are not mysterious. A lot of it comes down to repeatable practices:

    • daily check-ins
    • honest prioritization
    • regular self-scoring and reflection
    • end-of-day accountability
    • weekly and quarterly reviews
    • pattern recognition over time

    That became the foundation for the system.

    What made it work was not the intelligence. It was the design.

    The breakthrough wasn’t simply “build a chatbot.”

    Plenty of chatbots are smart enough to answer questions. That is not the hard part.

    The hard part is creating the conditions where accountability feels real.

    Three design choices mattered a lot.

    1. It had to live in chat

    I already knew from building other systems that a normal chat interface has a very different feel from opening a blank browser tab.

    If something lives in Telegram, it comes with you. It’s on your phone. It’s in the same place as real conversations. You don’t have to remember to open the app that is supposed to help you. It shows up where you already are.

    That sounds minor. It isn’t.

    A lot of self-improvement software fails because it depends on you having enough discipline to go use it at exactly the moment you’re least likely to want to. If you’re avoiding something, you’re not going to voluntarily open the accountability dashboard.

    A message in chat changes that dynamic.

    2. It had to be proactive

    This was the second big insight. The system couldn’t just wait for me to ask a question.

    It needed a heartbeat.

    I’ve written before about how persistent agents become more interesting when they can wake themselves up and check in rather than sitting dormant between prompts. That’s the same idea I covered in Let’s Talk About the Open CLAW in the Room. The value isn’t just intelligence. It’s continuity.

    So I built the coach around proactive outreach:

    • a morning stand-up
    • occasional midday follow-up when something time-sensitive was mentioned
    • an end-of-day recap
    • scoring and reflection
    • longer review cycles over time

    That one change made the whole thing feel less like software and more like a process.

    3. It had to remember

    Without memory, a coaching bot is just a clever prompt.

    With memory, it starts to become useful.

    A real coach remembers what you said last week. They remember the thing you promised to do and didn’t do. They notice when the same excuse keeps showing up in a different form.

    That memory layer ended up being one of the most important parts of the whole system. It let the coach connect today’s priorities to older conversations, recurring friction, and longer-term goals.

    That’s when the pushback started getting good.

    What the conversations actually look like

    Most mornings start with a simple stand-up:

    What are the three most important things today?

    That’s not a revolutionary question. But it becomes powerful when something is going to ask you about it later.

    Sometimes the coach just captures the plan. Sometimes it pushes back.

    If I list something that should obviously be delegated, it asks why I’m still doing it myself.

    If I fill the day with low-value tasks, it asks whether any of them are actually connected to revenue or the highest-leverage goal.

    If I keep postponing something important, it notices.

    And the memory makes the confrontation sharper than I expected.

    It can say things like:

    • this is the second time you’ve pushed off writing that marketing email
    • you keep making room for side projects when the main project still needs attention
    • you said this meeting was important yesterday, so what changed?

    That kind of feedback is useful because it cuts through the story you tell yourself in the moment.

    I’ve written before that goals work better when they turn into measurable daily actions. This system effectively enforces that translation every day. Big intentions have to become concrete commitments.

    The surprising part: even AI can create accountability

    This is the part that surprised me most.

    On paper, it sounds silly. It’s just software. It’s not a real human being. Why should it create any accountability at all?

    But accountability is not only about authority. It’s also about having a witness.

    Once the coach lives in a real chat, checks in proactively, follows up later, and complains a little when you ignore it, the interaction starts to create social pressure. Not the same pressure as a great human coach, obviously, but enough to change behavior.

    That matters.

    Because a lot of productivity problems are not really knowledge problems. They’re avoidance problems. They’re friction problems. They’re “I know what I should do, but nobody is making me face it” problems.

    That’s very similar to the pattern I wrote about in What You’re Really Avoiding Isn’t the Work. The obstacle is often not inability. It’s the gap between knowing and doing.

    A coaching loop helps close that gap.

    It also taught me something about my own habits

    The strongest value wasn’t just that the coach reminded me to do things.

    It showed me my patterns.

    The same weak spots kept coming up:

    • a tendency to drift toward side projects
    • reluctance to delegate certain work
    • the habit of postponing tasks that feel important but uncomfortable
    • confusing activity with meaningful progress

    That sort of pattern recognition is useful because it turns vague guilt into something concrete.

    Once a behavior gets named, it becomes easier to interrupt.

    That is where this starts to overlap a little bit with therapy or journaling. Not because the bot is a therapist, but because repeated reflection makes your own habits harder to ignore.

    And if you are trying to build structure into your work, that kind of reflection compounds over time. I’ve written about the importance of creating structure and the need for small daily wins to maintain momentum. This system is basically a machine for both.

    From a pile of scripts to a real product

    I ran the first version as a bundle of scripts on my own computer for several weeks.

    It was rough, but it worked.

    Under the hood it combined three things:

    • coaching research and prompting
    • a memory system
    • proactive messaging throughout the day

    That was enough to prove the concept.

    Once I saw the benefit personally, it became obvious that it should turn into a real application. Part of the reason is practical: if a coaching system is going to be proactive, something has to stay running. There needs to be a process alive in the background checking time, tracking context, and deciding when to reach out.

    So I rebuilt it as an installable desktop app.

    That turned into its own fun little experiment. At one point I had AI migrate the application into Rust in basically one shot. I don’t know Rust, which made that entertaining, but the result is that the app now compiles cleanly into native desktop software and lives in the taskbar like a normal application.

    It runs on Mac and Windows. No server required on my side. Users bring their own API key, which keeps the economics simple and avoids the usual problem of somebody burning through shared credits.

    Where I think the value actually is

    I don’t think this replaces a great human executive coach.

    A great coach can read nuance better, challenge you more deeply, and bring lived experience that software cannot fully match.

    But that’s not the standard that matters.

    The real question is whether a persistent AI coach can deliver enough value to justify existing.

    I think the answer is clearly yes.

    If a human coach costs hundreds of dollars an hour and maybe shows up once a week, there is a large middle ground between “nothing” and “premium executive coaching.” A system that asks strong questions every morning, follows up in the afternoon, remembers your patterns, and keeps your priorities honest can be enormously valuable even if it only captures part of the full experience.

    Personally, I think I’m going to get far more than $49 worth of value out of it just from better prioritization and fewer days lost to drift.

    If this sounds useful, it’s available now

    After running it for weeks, I decided to make it available as a real product.

    It’s called AI Executive Coach, and it’s available here:

    Read the full AI Executive Coach page here.

    For the first 100 users, it’s a one-time purchase of $49.

    That’s intentionally simple. No server dependency on my end. No complicated subscription decision upfront. Just install it, add your own API key, and use it.

    If you’re the kind of person who knows what to do but still benefits from having someone, or something, force a little honesty into the day, you’ll probably get it immediately.

    Final thought

    The biggest thing executive coaching provides is not advice.

    It’s cadence.

    Someone asks what matters. Someone checks whether it happened. Someone notices the pattern when it doesn’t.

    That loop is expensive in human form. It doesn’t have to be expensive in software.

    And for a lot of people, that may be enough to make the difference between a day that felt busy and a day that actually moved something forward.

  • What You’re Really Avoiding Isn’t the Work

    What You’re Really Avoiding Isn’t the Work

    Everyone has a version of this. A category of work that sits on the to-do list for weeks, then months, slowly accumulating guilt. For some founders it’s legal. For others it’s HR, compliance, or investor reporting. For me, it’s always been accounting.

    Not because I can’t do math. Because every time I opened QuickBooks, I’d feel the weight of everything I didn’t understand, and I’d close the tab. There’s always something more urgent than confronting what you don’t know.

    This week I finally sat down and did all of it. Reverse-engineered spreadsheets. Audited our QuickBooks accounts. Found missing payables. Fixed miscategorized transactions. Worked through international currency adjustments. Even handled an off-the-books equity correction I’d been dreading for longer than I’d like to admit.

    And here’s the part I didn’t expect: it was actually kind of fun.

    The difference wasn’t discipline. It was having AI as a collaborator. And the reason that mattered has nothing to do with accounting specifically.

    The real barrier is shame

    Think about the task you’ve been avoiding. Now think about why.

    It’s probably not because the task itself is impossibly hard. It’s because there’s a gap between what you know and what you’d need to know to do it confidently, and closing that gap feels expensive. You’d have to ask someone. That someone is busy, or expensive, or both. And the questions you need to ask feel like they should be obvious.

    That was my relationship with accounting for years. Accountants always seem busy. When I’d get on a call with mine, I’d feel the clock ticking. Every question felt like it should be obvious. Do I really need to ask what a trial balance is? Can I admit I don’t understand why this line item is negative? Is it okay to not know the difference between cash-basis and accrual?

    So you nod along, say “makes sense,” and leave the call having learned nothing. Then you avoid the whole topic for another month.

    This is the shame barrier. It’s not a knowledge problem. It’s a help-access problem. The help exists, but the social cost of accessing it is high enough that you just… don’t.

    What happens when the shame disappears

    When I sat down with Claude Code this week and started working through our financials, I could ask anything. Literally anything.

    “What does this column mean?” No judgment. “Why is this number negative when we received money?” Clear explanation. “Walk me through how this journal entry should work.” Step by step, as many times as I needed.

    I went deep on things I’d been skating past for years. The nuances of our P&L statement. How the balance sheet connects to the trial balance. Why certain transactions were showing up in the wrong categories. What our cash flow statement was actually telling me versus what I assumed it was telling me.

    Each question led to a better question. And because I wasn’t worried about wasting someone’s time or looking dumb, I kept going. I’d ask a follow-up, then another, then branch into something related. It was the first time accounting felt like learning instead of an exam I was failing.

    If you’ve ever had a mentor who made you feel safe asking the dumb questions, you know how much faster you learn in that environment. AI gives you that dynamic on demand, in any domain, at any hour.

    The concrete results

    This wasn’t a vague learning exercise. I worked through real problems in our actual books:

    Reverse-engineered inherited spreadsheets. We had several financial spreadsheets maintained by different people over time. I fed them to Claude and asked it to explain what each one was tracking, how the formulas worked, and where there were inconsistencies. It found things that had been wrong for months. If you’ve ever inherited a spreadsheet from someone who left the company and spent hours trying to figure out what it was supposed to do, AI turns that from hours to minutes.

    Audited QuickBooks categories. Transactions miscategorized across multiple accounts. Expenses in the wrong cost centers. Payables missing entirely. Claude walked me through each one, explained what the correct category should be and why, and helped me make the corrections.

    Handled the stuff I’d been avoiding. International currency adjustments. An equity correction I didn’t fully understand the accounting treatment for. Reconciliation of accounts that hadn’t been reconciled in too long. These are the kinds of things where I’d normally email the accountant, wait three days, get an answer I half-understood, and still feel uncertain about whether it was done right.

    Thought through the strategic questions. Beyond the bookkeeping, I used the conversation to think through bigger questions. I’ve thought about managing cash flow before, but this was different. What are our actual options right now? What interest rate is expensive versus reasonable for our situation? What are the trade-offs between different funding approaches? These aren’t strictly accounting questions, but they live in the same “financial stuff I’m uncomfortable with” bucket, and having a patient conversation partner made them approachable.

    The pattern worth noticing

    Here’s what I want you to take from this. It’s not “use AI for accounting,” although you should.

    Every business owner has domains they understand well and domains where they’re faking it. For me, the product development, marketing, and technical infrastructure are comfortable territory. Finance has always been the thing I know I should understand better but never prioritize learning. It’s a version of the fear of the unfamiliar that I think most founders carry around quietly.

    AI doesn’t replace the expert. I still need a CPA for tax strategy and compliance. But it fills the gap between “I know nothing” and “I know enough to have a productive conversation with my accountant.” That middle layer of competence is what most people skip, and it’s exactly where AI excels.

    Before this week, my accounting approach was “send everything to the accountant and hope for the best.” Now I actually understand what’s in our books. I can read a P&L and know what I’m looking at. I can spot when something looks wrong. That upgrade happened because the learning barrier dropped to zero.

    Apply this to your thing

    This keeps happening. Tasks I’ve been dreading turn out to be approachable, even enjoyable, once I have a collaborator that’s patient, knowledgeable, and available whenever I’m ready to work. It happened with growth engineering. It happened with the small automations that add up. Now it’s happened with accounting.

    The common thread is that the barrier was never ability. It was the friction of getting help. AI removes that friction, and suddenly the things you’ve been avoiding become the things you’re making progress on.

    So here’s my challenge to you: think about the task that’s been sitting on your list the longest. The one you keep bumping to next week. Ask yourself whether the problem is really that the task is hard, or whether the problem is that you don’t have a safe, low-cost way to close your knowledge gap.

    If it’s the second one, you might be surprised at what happens when you just start asking questions.