You’ve probably experienced this when a site like Facebook or GitHub goes down, and the world panics while everyone watches the status page… frantically refreshing so they can use it again.

A short MTTR is ideal. A long MTTR can be a disaster.

The Other MTTR

Let’s face it. AI is going to cause a lot of crashes. And not just servers in the cloud, but career paths and entire org charts.

People accustomed to fitting into nice, neat roles with a clear 5-10 year plan are going to feel uneasy as their roles and responsibilities change quarterly (instead of alongside a promotion every 2-4 years).

The big question (to paraphrase 90s band Chumbawamba)… when these people get knocked down, will they get up again?

And how fast?

What will their mean time to reinvention be? What’s their MTTR?

A few years?

A few quarters?

Or can they join a new team and be valuable in under a month? A week? Immediately?

From PhD to a No-Nothing Developer

After spending 10 years getting a BS and PhD at MIT, many were shocked that I “threw it all away” and became a backend Drupal developer. “Developer” is too strong a word, because I could barely understand the complex UI, let alone write useful PHP code or database queries.

I went from the world expert on van der Waals forces of carbon nanotube systems to… the bottom of the ladder.

It was not easy. And it took me many, many years to claw my way back up to a normal income level and a position among my peers.

But then? I kept on rising the ranks, eventually becoming one of the leaders in the space.

And Another One

So did I stay in Drupal forever? Hell no.

I threw that all away and started again. And then again. And then again.

Depending on how you count, I’m into my 6th startup or business. And every time I jump into a new space… yes, I bring a lot of transferable skills and experience. However, I also have to spend A LOT of time drinking from the firehose trying to catch up to everyone else.

Now I can join a company and start figuring shit out on day 1. Will I be perfect? Hell no. But I’ve survived through many near-death experiences in business, so I’m largely unfazed by my “normal” day-to-day disasters.

Continuous Education to Continuous Reinvention

With AI, we no longer get to choose when to leap into reinvention.

The world is changing so fast that many of us will be forced into continuous adaptation.

Some will succeed in this new paradigm. Many won’t.

Those with slow MTTR (requiring years to reinvent themselves) will learn things just in time for them to be out of date again. How awful! They will forever be at the back of the line.

Those with a fast MTTR? They have the highest probability of adapting to and benefiting from every new wave of change. On net, they will be the leaders at the forefront.

So… what do you do?

Change is coming, so embrace it.

If you haven’t built up a high tolerance for change, start building this muscle NOW.

Start introducing more change into your life now. New hobbies. New experiences. Read books that challenge your thinking. Visit new places. Question all of your existing strategies. Try new AI tools.

When Netflix wanted to improve their MTTR, they would actively introduce “Chaos Monkeys” into their infrastructure to test how resilient it could be. Start shutting down servers. Disconnecting network lines. How fast would the system detect and come back online? If it were slow and fragile, you’d design the system to be stronger next time.

Add a dash of chaos monkey into your life. Or as Scott Adams would say, if you’re bored, you’re not embarrassing yourself enough. Embarrassment can ONLY happen when you put yourself way out of your comfort zone. No risk of embarrassment means you’re not taking risks.

Yeah, I Yolo’d This Article

I wrote this article in a single-word vomit while waiting in the hot parking lot outside my daughter’s school dance.

I could (and should) have spent more time refining it. But whatever.

I had been sitting on this idea for a week, and it ALMOST died on the vine.

But I know it’ll connect with at least a handful of readers. And it would be a disservice not to share it.

This is true for you too.

Get out there more. Do hard things. Do weird things. Do half-baked things.

It’s ok. The world will judge you anyway. At least make it an interesting life to judge!

PS. Thank you, Grammarly, for the rapid spell check. Only 40 this time! Giddyup 🤠

AI made building cheap. Starting around November 2025 w/Opus 4.5:

• The things that used to take a developer two weeks started taking an afternoon.

• And the thing that took an afternoon started taking ten minutes.

Building was cheap, but managing all the output got expensive (at least without a plan).

Many AI users are sitting on piles of prompts they wrote once, can’t find, and never ran again. No habit formed. No system built.

The AI “prompt libraries” were supposed to be a part of a well-oiled machine, but they now look like an episode of Hoarders.

Hundreds of work-in-progress prompts all stuck in purgatory. This is a travesty, because the fix is a little more time in the right places. We need to follow the patterns and playbooks by those who have already solved this.

Kieran Klaassen at Every wanted a codebase that got better with every feature shipped. I want the same thing for workflows: every lap around the loop should make the next workflow cheaper, easier, and better.

This post describes these loops. One version for a soloist, one for a team, and the honest reason most of the effort occurs before and after the build steps.

The Inspiration

Orie Steele put together a deck he calls vibeslop. It’s an entire software development lifecycle for the AI era: Pitch, Sketch, spec-kit, Ship, Score, with the named frameworks at every stage (e.g., Jobs to Be Done, the Hook Model, Double Diamond, etc.).

It’s serious work, and I mean that as a compliment for an article title, as a joke. It’s also built for engineers, not the rest of the organization.

I wanted the version that fits on a whiteboard and works for someone who has never opened a repo, because it’s the work I do day in and day out with my clients.

Why Me

I’m an engineer and repeat startup founder who became an executive, so I live on both sides of this loop. I wrote about that split as the Forward Deployed Executive, so I won’t re-explain it here.

What I’m about to share is a combination of solutions I’ve applied across a dozen clients across as many industries. I’ve already published articles on various facets of this, but I wanted to bring it all together into one Sesame Street Simple map.

And while there are already multiple takes on AI-native software development lifecycles (SDLC), the reality is that there hasn’t been as much focus and success in mapping this out for the non-engineering portions of an organization (which can represent a large majority). Also, AI is still evolving rapidly, so best practices will likely continue to evolve across all types of organizations.

My bet: learning to run a loop like this is the skill that survives the next model release.

The Value

Here’s what you get for running a loop instead of a one-off. Four things, and each one depends on the one before it.

Quality. You stop one-shotting. The second and third passes are where the good stuff shows up, because follow-through is the actual superpower.

Consistency. One-off, then prompt, then skill, then automation. Every step down that ladder narrows the variance in what comes out.

Automation. You can’t automate what you don’t trust (hence the need to master quality and consistency first).

Compounding. Time saved and improved outcomes free up resources to go around again. That’s the whole game.

Again, builds are cheap. Given that, expect to spend 90-95% of your time into the workflow design and the structure around the build. That’s the part that pays back on every future lap.

The One-Off Strategy

You know this one. Someone had an idea, typed it into a chat window, and it worked. The demo impressed everyone at the meeting.

Then nobody ran it again.

The prompt lives in a Slack thread, or a Notes file, or in the head of the one person who wrote it. Next time the job comes up, it starts from zero.

The paradox is that it’s the packaging and automation where the real, long-term value comes from in any of this work… and most people stop 10 yards from the finish line.

The Solo Loop

Five stages for each lap around the loop.

• Prioritize. Rank what’s worth doing against a rubric (I score Value, Effort, Reach, and Recurring). Ignore the rest.

• Organize. Gather what the job needs before you open a tool: the knowledge, context, APIs, and systems it touches.

• Build. Proof of concept first. Once it’s approved, polish it into the version someone will actually use this week.

• Document. Write down what good looks like, in cases you trust, plus the SOP for running it. Plan for organization and recall. Your future self will thank you.

• Automate. The goal: you can one-click trigger an entire process: bonus, the work fires on a schedule or a trigger.

The loop closes at the bottom, and that’s the part I care about. Time saved and skills upleveled go straight into the next thing on the ranked list. You never start from zero twice.

Expectation control: this is where the hours go. Build is the short bar. Organize and Document are the tall ones because planning the job and writing down what good looks like make up most of the work. Skip those two to “just build it” and you get a one-off with extra steps.

The dopamine trap of moving fast and furious ends with no long-term durable workflow improvements. Resist it. Finish the loop!

The Team Loop

You’ve heard the proverb: if you want to go fast, go alone. If you want to go far, go together.

There’s a big difference when you go from solo mode to teams. Now you gotta prioritize the right things, ensure proper change management and training, etc. If you don’t, your amazing workflows stay on an island with you instead of leveraging the entire team or organization. Resist the temptation to skip this!

You can slice and dice this further, but I like to think of this as 8 steps:

• Idea! Get the ideas out of people’s heads, write them down, and keep a shared list you can draw from as needed.

• Selection. Rank the list using an effort-and-value rubric, and name the outcome before anyone opens a tool. This is the kill box.

• Mapping. Sketch the UX, who’s responsible for what (RACI), and paint what done looks like. If you uncover many problems, update the priority scores and send it back.

• Build. PoC, then MVP, then production. Don’t skip the ugly middle.

• Benchmark. Does it work beyond a “Hello World” demo? Test against the end-users’ golden set until it’s delivering value.

• Automation. Reduce as much friction as possible to make remembering, finding, and triggering easier again and again.

• Sharing. Ensure everyone knows where to look and how to use it, and that they can successfully achieve the outcome.

• Actualization. Leadership, culture, collaboration. This is where feedback and new ideas come from, and then you go around again.

Two arrows point backward, and they will save you tons of headaches if you stay disciplined and use them.

• Mapping sends work back to Selection when the map doesn’t survive contact with the real job.

• Benchmark sends work back to Build when the real work fails.

Past Benchmark, nothing comes back. That’s on purpose. Benchmark sits between Build and Automation, so passing it is what earns a trigger. Every other framework I looked at puts evals inside Build (to gate the merge) or after ship (to measure the outcome). This one puts them in front of autopilot. You don’t get to automate something that can’t pass real work.

Ideally, you keep forward momentum wherever possible. And of course, don’t be too rigid. This set of constraints is meant to help you achieve full actualization of your efforts! Not to straitjacket you into sitting still. But lots of frenetic motion without a plan can be even worse. Find the balance point in your organization and apply the parts that work for you.

The Future Phase

I want to go much deeper on this, but only if this topic resonates with enough people. No matter what, I plan on using it in my AI consulting practice for the foreseeable future.

The longer follow-up would include:

• The frameworks at each stage (the way Orie did it in his article)

• Skill files per step so an agent can run the loop with you.

If this post gets at least 10 comments from people saying they’d be interested, I’ll proceed.

Follow-up

• Give this to your agent and have it draft your own version of the loop. Then argue with it (and make it yours).

• Subscribe to my newsletter so you get a notification.

• Skills are coming, one per step. Some are already on github.com/rickmanelius/skills.

• Share it if it helped. If this gets 10 comments, I’ll know it matters enough to build the long one.

• Comment “follow-up” and I’ll DM you when the long version is published.

PS. Here’s a HyperFrames explainer for those that prefer another angle at this.

Me: “No problem! I’ll do it myself.”

But my 5-year-old had other plans. She’s known for asking a question every 15 seconds. And the few times she was silent, the AC kicked on and started humming in the background.

I needed 90 seconds of clean audio. But after 30 minutes of interruptions, I rage quit and sent the AI voiceover instead.

The HyperFrames Background

I LOVE HYPERFRAMES.

Distilling a complex topic down to a minute-long explainer video has been a game-changer. They are even better with voiceovers because they can add storytelling, context, and emotion.

However, the two ways of adding this to a HyperFrames video are both… not ideal.

1. Stock AI. They are precise, but people hate them because they sound fake and generic.

2. Record your voice. Great if you have a good mic, a good voice, and a moment of silence.

Clients despise #1. I despise #2. There had to be a better way…

The Podcast Room Tax

Here is the frame I keep coming back to.

Every voiceover charges you the same three things:

1. A quiet room

2. A good mic

3. A clean take

Call it the podcast room tax. And it’s getting more expensive every day given that I’m generating more and more video content.

The solution? What if you could pay the tax once and benefit forever? This is what I explored this morning. I wanted to record my own voice and then feed it into AI to reproduce my tone, cadence, and voice.

The initial result? Not bad.

Yes, it needs some tweaking… but it’s getting there.

Getting Started with xAI Custom Voice

Here’s how I did it.

1. Go to console.x.ai

2. Open Custom Voices

3. Record your voice live in the browser by reading the passages.

4. Copy the ID for your voice template and hand it to your agent.

Now I know what people are thinking. But can’t everyone use this? The constraint is the consent model. IMHO it is the right one. There is no “paste in a podcast episode and become that person” path. You must show up, talk, and use your own voice.

What surprised me

• My clone talks faster than the stock narrator, 3.13 words per second against 2.33, so a script written for the old voice finishes early.

• TTS also reads your typos aloud (so be sure to spell-check!).

• It still feels too “annoucer-y”, especially in the beginning of the recordings. But as they keep playing, I feel it sounds more and more like me.

• It’s cheap! Something like $5 for an hour of audio is way cheaper than I expected.

• It’s fast! From request to done takes barely longer than the audio itself.

• It’s addictive! I’ve already generated 5-6 different audio takes now that I have the skill baked and ready.

My first use of HyperFrames wasn’t perfect, but I’m sharing it as is just to timestamp my first experience. I expect that this will get better with each iteration to the point where you won’t be able to tell at all.

15 minutes could save you 15% or more on car insurance.

Well, what if 15 minutes could net you $ 1,500 in lost cash?

That’s what happened with Will Tjernlund (ok, it was really $1400).

So… how? It’s a website called “Missing Money.”

I know, I know. Gotta be a scam, right? I can feel people’s BS meters already being triggered. I was skeptical too, but the referral came from my uncle, who uses the site every year to find unclaimed property he’s owed.

I also spent about 30 minutes searching around to see if the website had ever been flagged. However, the real proof is that I used it myself and collected checks. Results below:

My Results: $450

Since I had never used the website, I had about 10 entries, dating back to 2010 in Colorado and 2005 in Boston. All told, we found $250 for me and $200 for my wife.

I was owed money from companies ranging from Epson printers to old Chase bank accounts. Hell, apparently I never collected a refund from my old Massachusetts E-ZPass.

The Meta Lesson: Hidden Resources

Some people may search for “Missing Money” and find a few hundred dollars. Some will find $0.

However, I bet there are several situations in your life where you can find these hidden cash-out opportunities hiding in plain sight.

One of my relatives has a bike he bought in 2010. It’s barely used and still worth at least $2,000. It’s currently hanging in his garage, and my guess is there is ZERO chance he’ll ever use it again (at his age and physical condition).

Why not sell it and use the funds for something else?

My wife and I do a yearly Facebook Marketplace purge of stuff our kids can no longer use. Some years are $50, and others we make selling $500 or more of stuff we no longer use.

Or does someone still owe you money?

The meta lesson is that if you look hard enough, there are missing money opportunities.

My recent experience with Missing Money was another good nudge for me to keep my eyes open.

PS

You know it’s a government operated website when the design is this old-school. LOL

1. Clearing my desk

2. Writing a small TODO item on a bright 3x5 index card

3. Placing this card on my keyboard so I can’t miss it.

Why?

To overcome the resistance of getting the workday started.

Steven Kotler says most people waste 2-10 minutes “dithering” between tasks. They seem to be gathering up the willpower to jump in and get the next thing done.

I used to be that way when I showed up at my office.

I’d plunk down, open my laptop, and immediately waste 5-10 minutes scrolling through Twitter before deciding what to work on first and getting to it.

But now, I leave myself an index card; a momentum matchstick.

I sit down, see the TODO I can knock out in 1 minute, and jump right in.

Why The Matchstick Metaphor?

A matchstick is a single strike that can ignite a fire.

Momentum and flow work the same way.

Attention drives focus.

Focus drives flow.

Just finishing one task can lead to the next, and then the next.

I love stretching a good metaphor. Let’s go nuts here.

1. You strike a momentum matchstick.

2. To generate a fire of flow.

3. To burn through your TODO list.

You may groan, but you will remember this (you’re welcome). And enjoy an AI visual:

Personal Results

Lately, I’ve found myself immediately dropping into a 1-2 hour session, knocking out 5-20 things off my TODO list.

I encourage you to give it a try.

• Leave yourself the smallest possible action.

• Start it as soon as you see it.

• Finish and repeat.

Don’t forget to leave yourself another card for the next day.

I get asked this question a lot. For the past 15 months, I’ve been leading AI training and team transformations, and building, building, building (my current interest is in open-weight models on local AI hardware). As a result, I tend to find and share as many resources as I can with my colleagues. And while there are great resources everywhere… the answer is simple.

Me: “You need to be on X.”

This is where I pause and wait for revulsion (from at least 50% of my colleagues).

X: “Ugh. But I hate Elon so much. I’ll never use X.”

And that’s the crux of the issue… one’s choice to harbor hatred towards a person/organization is not without career consequences. But before we dig in…

I Love a Principled Stand

When my mom died 4 months ago, there was a flurry of conversations about her on Facebook.

And I refused to create a new account to participate.

It wasn’t an easy decision to delete my account in the first place. MIT was one of the first campuses to get access, and I had photos and connections that span back to 2005. However, I never trusted Mark Zuckerberg. Over the years, there have been dozens of ethical violations involving user data. It was clear to me these weren’t accidents, but it was just who he was. So in 2021, I deleted my account.

This decision inadvertently severed many friendships.

The thing about a platform with 2 billion active users is that it’s the center of the social conversation. And if you’re not there, well… sorry. Most of these people are no longer easily reachable. Enjoy the FOMO.

It sucked not being able to connect with people sharing memories about my mom. That had to wait until the funeral when I could see them all IRL.

Still, while I’m not at peace with her loss, I am at peace with my decision to never use a Meta platform (Facebook, Instagram, and WhatsApp).

I love a principled stand, and I accept the (social) consequences of that decision.

Even Zuckerberg Capitulates to Elon

First, let’s separate objective facts from subjective opinions and feelings.

I’m sure you’re familiar with the moral alignment chart (hat tip to D&D). It’s a 3x3 grid that ranks a person on two scales: good vs. evil and lawful vs. chaotic.

We like to think of a person as living in a single fixed square. However, Elon is one of those eccentric individuals who can shapeshift based on context.

There are many sides of Elon. I think I know where he stands most of the time (e.g., chaotic neutral with a dash of chaotic good), but many disagree (e.g., various forms of evil). That’s fine; no matter where you stand, know that many others are locked into another perspective. No need to get upset over it, but…

The fact remains, X is THE definitive place to keep up with the AI conversation.

This is the reason Boris Cherny became active on X just as his Claude Code project was taking off.

Since he joined and started replying all day, every day, both his personal profile AND the project have rocketed in popularity and adoption.

9 months later, it’s become common for other developers and researchers to be pushed onto X. It makes sense, because this is where the customers live and where the conversations are happening.

Hell… EVEN MARK ZUCKERBERG, the CEO of a 2-billion-person social media platform, makes announcements of his new AI models on X instead of Threads for Facebook.

There’s Alpha in What Others Hate

When GitHub had an outage this week (again), Cursor used it as an opportunity to launch their new code hosting platform.

I was excited to hear this because a few months ago GitHub had one of the worst months in SaaS history, with uptime dipping below 90%. This is unheard in enterprise cloud software, where SLAs of can range from 99.9% to 99.99% uptime (with contractual penalties).

I expected my technical colleagues to respond with something akin to “cool” or “FINALLY” given they were experiencing the brunt of these outages. Competition leads to innovation, which should lead to a better ecosystem.

Instead, the responses ranged from (at best) apathy to (at worst) total rejection of the idea. How could an Elon-owned company build anything close to GitHub?!?

Their choice to hate Elon as a person/founder/entrepreneur prevents them from even evaluating what could be a superior product. This is putting them (and their team/company) at a potential disadvantage. After all, this is the guy who has built companies that can catch rockets, implant chips into people’s brains, and self-drive cars at scale. Hosting software should be a cakewalk compared to the other 3.

And Again, With Grok Bot

Lots of AI people on X are sharing positive takes about Grok Bot.

Ryan Carson (super early on AI agents, loops, etc)

Claire Vo (founder of ChatPRD and an overall exceptional voice in the community)

Or Investor Gavin Baker, who feels this is the next “Claude Code moment.”

Even non-technical folks are raving about how Grok Bot meets the “just works” bar.

But here’s the thing: many technical colleagues have:

1. Ruled out Elon (because hate, evil, politics, etc.).

2. Therefore, rule out Grok (because it can’t be as good as Anthropic/OpenAI)

3. Therefore, rule out Cursor (because Elon purchased it, defaults to Grok)

4. Therefore, rule out Grok Bot (because they can’t even get here because points 1-3).

And so it goes.

Maybe they are happy with OpenClaw (X sentiment is that it’s now 3rd, behind Hermes and Grok Bot) and GitHub (even though the outages persist), but many willing to engage on X think otherwise.

They were willing to try, experience firsthand, and make an informed decision.

I think that’s the smarter, wiser approach.

This is Not About Elon

Love him. Hate him. I don’t care.

I don’t care whether you love or hate Zuckerberg, Bezos, or X (where X is a famous figure).

What I do care about is that people may be making a bad career bet by NOT considering superior, cheaper tools, technologies, and platforms, all because of a choice.

And just as I respect my decision to never use a Meta product because of Mark, I respect people who draw a bright line on Elon. Fair is fair.

But I would love for you to consider this idea.

You Can Learn from Someone You Dislike

I’m not the biggest fan of certain VC thought leaders on X. They can say some really dumb, tone-deaf things on the regular (e.g., Chamath’s “this is below my line” quote).

However, now and then they’ll say something brilliant that stops me in my tracks.

There’s a saying in Al-Anon (and other 12-step programs). Take what you like, and leave the rest.

I can filter out Elon’s politics and stupid memes, and eventually he says something so insightful that I journal about it and apply it within my professional career.

I don’t have to like (or even respect) him to benefit from his hard-earned wisdom.

It Doesn’t Have to Be All or None

I’m not the biggest Dana White or UFC fan. It’s just not my jam. But damn, he’s an A++ operator in his business and his brand. I watch all his interviews because his perspective is so different from what I’m usually exposed to in my circles. And many of his insights are dead on (and some of them aren’t, and that’s ok too).

If you hate and block him, you miss out. That’s fine. No judgment. Just know that you don’t get access to the occasional nuggets of gold because you hate when he talks shit.

I’m not for everyone either. But I hope that, despite the occasional cuss word or dumb political view, you find enough value in coming back here and engaging in a spirited discussion with me.

Agree? Disagree? I’m ok with either.

I would love to hear from you in the comments, especially if this helped you recognize you might be blocking a “good” opportunity because of a (real or perceived) “bad” person.

I mean, what if you’ve pegged person X into the wrong position Y?

Until next time. Thanks for listening ✌️

Oh, and the best place to keep up with AI is X (I’ll die on this hill).

Alas! Hat tip to seclilc for tweeting this gem out two days ago.

I’ve been thinking about it ever since. Why? Because there is growing grumbling among everyone about AI writing. And it’s not just others; it’s me! I am getting to the point where I physically flinch (sometimes dropping my shoulders and hunching, or having a slight eye twitch) when someone I respect sends me unfiltered and unedited AI output.

Look, I get it. It’s Q3 2026, and we should expect that everyone is utilizing AI at SOME point in their process (sourcing ideas, creating outlines, refining prose, etc.).

However, I have a new policy.

If you’re not bothered enough to review and edit it...

...then I’m not going to bother reading it.

Yes, there are certain situations in which we should expect 100% AI-generated copy. Customer support would be a perfect example. We’re not looking for artisanal “did you make sure to reset your phone” style dialogue.

But if you’re my colleague and we’re in a Slack discussion and you post a wall of Claude output, then I’m afraid I received a different message than you intended.

The same is true for people’s newsletters and social content. It’s your name on it; are you proud of the prose and weird AI-isms sprinkled throughout it? If so, great. But I can ask Claude directly if I wanted to.

TL;DR (too long; didn’t read) was the solution for social media.

AI;DR (AI; didn’t read) is the solution for AI slop.

May you embrace this policy yourself and seek out those willing to care enough to prioritize a human touch when they talk to you.

Updates

Update 1 2026-08-17: Daniel in the comments shared a wonderful website dontpastetheai.com. Amazing. I especially loved the Take me to the Angry Version version. The intro header says it all.

Update 2 2026-08-18: This made it to #1 on Hacker News on 2026-08-18 with >600 comments. It apparently struck a cord.

Update 3 2026-08-27: Given the popularity of AI;DR, I created graphics that you can use to spread the word!

Graphics

Dictionary Versions

Poster Version

Be sure to reply/share the best thing you read this week (or something that lit you up). Any topic goes!

1. Heart Health

In the last 3 months, 3 people I know had heart attacks (two didn’t make it).

Given I’ve been told I have a big heart (that’s a joke), and given my genetic predisposition (that’s not a joke), I’m now going through a battery of tests and scans to make sure I’m on top of this.

I don’t want to become the Tin Man and lose my heart. My two young kids don’t deserve to go through live without a father.

2. Going Loco for Local AI

Frontier AUI models are powerful, but the people running those companies do crazy shit.

In the last few months, we’ve seen models getting temporarily banned (Fable), degraded when you ask sensitive questions (again, Fable), and now Anthropic is watermarking ALL content from all its models (again, Anthropic).

Many are taking matters into their own hands, literally. I just invested $5K in an NVIDIA GDX Spark to test and run local models. You can follow my journey on X.

3. Peptides Ain’t Weird

For years in men’s circles, it was shameful to talk about things like testosterone replacement therapy (TRT). Well, the Overton window on that and other health/longevity “protocols” (gotta love that marketing reframe) has dramatically opened.

I’m somewhat late to the peptide game, but it seems this is already crossing over into mainstream. What used to be weird is now what everyone is exploring.

4. The Analog-Digital Barbell

Work colleagues see me going all-in on AI and ask… aren’t you worried about becoming less human?

Not at all, because my life has moved to both extremes of the barbell.

Work has become all-in AI. I’m embracing it as hard and as much as possible.

Life has become all-in analog. I’m outside with my kids. We’re swimming, climbing trees, playing Uno.

It doesn’t have to be either-or. You can and-both your way into a great life.

5. First, Care

Merlin Mann wrote a phenomenal article that I read 1-2 times a year (First, Care).

Unfortunately, it doesn’t exist on the web anymore. You have to know where and how to get it on the Wayback Machine, which itself may not be around forever.

It’s a reminder to me that if something matters enough, you have to make it last. I’ve been thinking of ways to permanently record the things that matter, so that my kids could have access to them long after I’m gone.

So I’m probably going to go back to writing some handwritten letters and add them to a vault. They’ll have all sorts of other memories of me, but I want to make sure they never lose some of the important ones.

And You?

What ideas sparked joy? Created a change in you? Would love to hear it.

DDEV was a startup I co-founded back in 2015. And for the first few years, we sucked at marketing. We had an amazing tech platform, but we lacked the time and creativity to explain it in a Sesame Street Simple way.

Eventually, our CTO came up with a brilliant idea. Explainer videos were all the rage because they helped create a compelling story in 1-2 minutes. This is exactly what we needed, so we committed to getting one ASAP.

Problem. They were VERY expensive. Good agencies would charge anywhere from $1,000 to $10,000, and the process was very time- and graphic-intensive. Our CEO ended up spending over 100 hours of his own time going back and forth with iterations on one he built from scratch.

Ain’t nobody got that much time and money these days!

Luckily, with AI, you can build these for a few dollars and in less than 20 minutes.

Here’s my go-to playbook

HyperFrames

Confession: I’m a HyperFrames Fanboy (shoutout to Matt Van Horn for getting me hooked).

Everyone uses AI to create transcripts and summarize meetings, but few read them.

A picture is worth a thousand words, but even the best infographics can only explain so much.

Enter the 2nd coming of explainer videos! They are beautiful, quick, and high signal-to-noise.

I even had a CEO once say, “Super appreciated the video by the way. I was able to get fully caught up over wedding dessert last night”

When was the last time your boss viewed your PowerPoint deck during their sister’s wedding? But videos are quick to consume and respond to. And in a world in which AI verbose-as-fuck, explainers are the best TL;DR solution we got.

Howto

The fastest way to get started? Give your AI agent this article and tell it to repeat these instructions for your website (or any website).

I tested this against a colleague’s website (Matt at Masterpoint.io), and I think it turned out well.

Here’s how to prompt this yourself with your website.

1. Install the HyperFrames plugin (takes 1-5 minutes the first time)

2. Install humanizer (optional)

3. OpenClaude or Codex

4. Use the following prompt.

I want to create a professional, compelling 60-second explainer video for Masterpoint.

1. Visit https://masterpoint.io with a focus on the homepage and product pages.

2. Use the company branding (fonts, colors, styles).

3. Ensure all copy is passed through /humanizer to not sound like AI.

4. Build the video using /hyperframes.

Save the video w/voiceover on my desktop, please!

5-20 minutes later, your video will pop out on your desktop!

Don’t like it? Hyperframes can make modifications quickly. Ask to modify:

• Storyboard

• Visuals

• Copy

• Branding

• Music (change or remove)

• Voice (change or remove)

• Duration (I prefer 30-90 seconds)

I’m usually happy with the first output, but sometimes I’ll iterate 1-3 more times, especially when the audience is a customer or a leadership/board meeting.

But 1-3 iterations is fast and cheap compared to 10 years ago.

Other Ideas

HyperFrames is one of my favorite AI skills that I’ve learned in the last 90 days. I use it daily for a variety of use cases.

1. Summarizing proposals and decks for execs

2. Product demos (polishes raw screen recordings)

3. Pitching product concepts

4. Company week-at-a-glance overviews

5. Meeting summaries

6. Tailored sales outreach messages

Dog-fooding

I’m starting an experiment today.

For all my long-form articles moving forward, I’ll include a 60-90 second explainer video as the TL;DR. If it works, I’ll continue. If it sucks, I’ll kill the experiment.

But it’s an attempt to live my values and prove the point. To that end, here’s this article as a HyperFrames overview. Enjoy!

No clue what I’m talking about? Observe Exhibit A.

On the left, a beautiful cake for the cat lover in your life.

On the right, nightmare fuel.

Or what about Exhibit B.

On the left, a Hallmark photo.

On the right, a high school graduation shame photo.

Few things juxtapose expectations vs reality better than this series.

Which brings us to the recent debates around AI and its ability to generate hundreds of ‘proof of concepts’ rapidly.

Just like Pinterest Fails, we all have visions that are beautiful and perfect in our mind… but become epic fails when they meet reality. It’s best to have a strategy for dealing with these failures when they occur (beyond using them for self-shaming social media posts like the ones above).

If something is cheap, something else is expensive.

This is known as the tradeoff principle.

Once AI made it simple to convert every wild-ass idea into a functioning demo, there were many second-order effects.

Yes, many of these 2nd order effects were positive!

• Dramatic reduction in costs, time, and stress in delivering new features.

• Prototypes give a more accurate feel/experience than mockups or written specs.

• Empowerment for non-technical team members to contribute.

However, there is a laundry list of negative effects. Here’s a laundry list of complaints from people in my circles.

• Lack of Followthrough. People will take a PoC to 80% in a few minutes, then stop. The dopamine rush of running 2-4 more experiments in parallel is more exhilarating than spending hours/days to polish it to 99%.

• Decision Overwhelm. AI can generate volumes of code and docs that someone has to review. And these reviewers are also busy generating volumes of code and documents. At some point, we are overloading ourselves with things to review.

• Sunk Cost Fallacy. Someone spent time building something, so there can be an inherent psychological pressure to keep going just because it’s already built.

• Frankensteining. Features used to take time, so things felt more thought out. Now you can bolt on 10 new features a day.

• Make work. Since devs have extra capacity, are they just throwing stuff at the wall and seeing what sticks?

• Coordination. It’s difficult to keep the team and customers in sync with the amount of changes coming through.

The Downstream Effects

So if AI made building cheap, what became expensive?

Human review, discussion, and decision time.

We’re buried in AI-generated everything, so we go into a sort of survival mode and abandon our process disciplines.

• We let PRs stack up because who has the time?

• We greenlight things we don’t have time to review.

• We don’t follow through because 4 other agents are vying for our attention.

• We don’t push back on a colleague because “at least they are being productive.”

• We stop trying to keep up with Slack, emails, notifications, tickets, etc (white flag waved)

Again, your mileage may vary, but these are the types of challenges I’m seeing people discuss in the channels.

One Solution: Kill Criteria

Annie Duke’s book Quit was written in a pre-AI world, but it’s more relevant than ever.

She tells the story of Google’s Moonshot division. This team is looking for the wild, 100-1000x ideas. Their goal is to attempt 1000s in the hopes of finding one massive win per decade.

So basically, they are willing to have 100 to 1000 bad bets for everyone that becomes a lotto ticket for the company. This means they have to be RUTHLESS about knowing when to abandon ideas that are either 1) straight up impossible or 2) possible but never going to be practical. To do this, they set up kill criteria up front. We must achieve X by Y or else we kill it.

This sounds intense, but it’s essential for focus. If they kept putting time, energy, and attention into all 1,000 bets, they would never discovery and succeed at the moonshots they are after.

My suggestion is to adopt a similar standard when you spin up a PoC. Have a high bar of what you hope to achieve by when, or a fast exit. Life’s too short, and the next idea will come to you soon enough. You don’t have to keep nurturing something that should have been killed at the idea stage. It’s fine, you invested some additional time, but now it’s gotta go. Kill it.

My Hit Rate

I’m very liberal with how fast I’ll create PoCs. Sometimes I’ll one-shot an idea within 5 minutes, brain-dumping a stream-of-consciousness prompt and seeing what happens.

Most of the time, it’s ok. Every so often, I get goosebumps. And sometimes, what came out was outright embarrassing.

My recent fail whale? I tried to use ScreenPipe to create a word cloud of the apps I used in the last 24 hours. Sounds amazingly useful! Claude worked diligently for 2 hours, going through massive research, reviews, best practices, etc. It probably spent over $100-$200 in tokens. I opened the app with “Christmas Morning” anticipation and…

Pinterest Fail

It was absolute fucking garbage. Like, I made better mockups in Photoshop in 2001. And it didn’t work at all.

Now if I kept all the “ok” results, I would probably give myself an 80% success rate. But a strong majority of those are PoCs that either 1) never made it to production and 2) I never opened again.

If I set the bar at a “wow” AND something I use at least weekly from then on, I’m probably at 5%. This is good to know. It means I can confidently reject what I’ve built 19 out of 20 times, knowing that each is getting me closer to the 5% that actually matter.

It’s ok to kill stupid ideas faster. They may have sounded like a good idea, but now you know. Didn’t work out. Move on.

The key is building this muscle NOW so that you can prevent yourself from being a PoC hoarder… holding onto everything you’ve ever tried.

List of Anti-Patterns.

I’ll end with a quick list of anti-patterns. If you’re experiencing these, you probably need to more aggressively 1) filter the PoCs you attempt or 2) kill the PoCs that didn’t work out.

1. Make-work.

2. Token Maxxing.

3. Ease > Value

4. Machine gun style

5. Feigning productivity

6. Building without goals

7. Forcing decisions

Are you struggling?

This is an evolving topic and may change as tooling and standards emerge.

I believe PoCs are more beneficial than harmful (usually), but I’ve seen the downstream effects become harmful with some folks.

Would love to hear where you’re winning or losing on this front!

This sucked. I had been warming up for the 400-meter hurdles with the hopes of winning sectionals. But I had to take cover under an umbrella for over 30 minutes. When the rain stopped, we were given 5 minutes to warm back up and get to the starting line.

By this point, I figured it was going to be a shit race. So when I found myself in the lead in the last 50 meters, I slowed down to get the win and conserve energy for my next events.

I missed the school record by 0.2 seconds.

It was a personal best by over a second.

Whoops.

Conventional wisdom would have suggested that a long delay followed by a short warm-up would have been an advantage.

And yet on that day for me it was… an advantage?

27 Years Later, Re-learning the Lesson

Last night I almost forgot to get a workout in, but I was determined to get it in before bed.

I’ve been doing Hindu squats twice a week for almost two decades (approximately 2,000 workouts).

In college, I could do 100 in 2 minutes and 15 seconds. Over the years, I’ve perfected my form and maintained my strength. In ideal conditions, I can do 100 in 2:00. My previous best was 1:56.

Last night was anything but ideal conditions.

• My garage was 95 degrees (and I hate working out in the heat).

• I just came back from the pool (having a beer with a neighbor).

• I was still sore as hell from boxing on Friday and Pilates on Saturday.

And yet, a thought popped into my head. What if this is an advantage?

So I went for it.

And I broke my personal best by 2 seconds.

I’ll remind you, this is a workout I’ve done 2,000 times over 2 decades. There is no rational reason that this 2,000th attempt was the one where I’d break my PR, and yet here I am writing this article to tell you about it.

Flipping the Script

Maybe these two stories are insane outliers or anecdotes. I don’t intend for this to give you false hope that you can magically think one phrase to break PRs. However, we do know that sports and life are largely mental games. How you show up to the starting lines of life can indeed impact how you arrive at the finish lines.

So my advice for you is to consider… what if this disadvantage is really an advantage in disguise? What if?

The Farmer Parable

This is one of my favorite all-time passages in literature. It’s from Dan Millman’s Peaceful Warrior.

An old man and his son worked a small farm, with only one horse to pull the plow. One day, the horse ran away.

“How terrible,” sympathized the neighbors. “What bad luck.”

“Who knows whether it is bad luck or good luck,” the farmer replied.

A week later, the horse returned from the mountains, leading five wild mares into the barn.

“What wonderful luck!” said the neighbors.

“Good luck? Bad luck? Who knows?” answered the old man.

The next day, the son, trying to tame one of the horses, fell and broke his leg.

“How terrible. What bad luck!”

“Bad luck? Good luck?”

The army came to all the farms to take the young men for war, but the farmer’s son was of no use to them, so he was spared.

“Good? Bad?”

And it’s a good reminder that favorable conditions can lead to unfavorable outcomes, and vice versa. A lot of it boils down to perspective and hindsight.

I’ll start and end this article with two recent stories (names changed to protect the awesome).

Story 1: Two Weeks vs Ten Minutes

Ben is a non-technical colleague that I respect, and I was livid that he was being taken advantage of.

His affordable, ~$30/hour dev was quoting him two weeks to deliver a feature that (I kid you not) was basically a single API query and exporting it into an HTML table.

I sat dumbfounded. I called bullshit.

This was May 2026, 6 months after the “holy shit” moment when Opus 4.5 turned the coding world upside down.

I asked Ben to let me screen-share while I fired up Claude Code. I then verbally brain-dumped a prompt.

“Make a dummy database from industry X. Then create a single HTML page. Then have that page call said database and convert it into an HTML table.”

We went back to talking. And somewhere between two and ten minutes later, the demo loaded on screen.

I turned to Ben. “So you’re telling me your developer with Claude Code is going to take two weeks to do that?”

This lunch meeting was the turning point for how his startup operated. Within days, they hired a new, AI-first developer to upskill the team. Within weeks, a client demo deadline that felt impossible was doable.

It took a technical, Forward Deployed Executive (FDX) to get the Ben over the hump.

Why I Hated the “Forward Deployed Engineer” Title

It’s ok to admit when you were wrong, and I was dead wrong.

The first time I heard about “forward deployed engineers” (FDEs), I thought it was yet another stupid marketing trick.

Isn’t this just software services?

Isn’t this just staff augmentation?

But it was fucking genius, because it solved a problem as old as time.

The Gap

In The Success Principles, mega-author Jack Canfield repeats the mantra of “closing the gap between where you are and where you want to be.”

The self-help, professional consulting, and executive coaching industries are built around this premise of gap closing.

And this gap is MASSIVE in the AI space right now. A few research examples (thank you, Perplexity!):

• 95% of enterprise GenAI pilots produced no measurable return. MIT’s Project NANDA looked at 300+ deployments, 52 case studies, and 153 leadership surveys; only 5% generated real financial value.

• 56% of CEOs saw neither higher revenue nor lower costs from AI in the past 12 months. Only 12% got both. That’s PwC asking 4,454 CEOs across 95 countries.

• 72% of CEOs now call themselves the primary AI decision-maker at their company, roughly double a year ago. Nearly all of them plan to keep funding AI whether or not they can prove it works.

Companies WANT to be at the bleeding edge, and yet most are feeling further and further behind… and their attempts at catching up just aren’t working.

Closing The Gap with FDEs

Palantir recognized the AI gap early. They knew the fastest way to close the gap, get customers to a win, and become indispensable for their clients was to embed the very same elite engineers that built the tech alongside their customers’ teams. Even better, these FDEs would keep identifying more opportunities to expand and grow the accounts, creating a virtuous loop.

So they coined a new job title and went all in on this approach.

I would argue that while the technique wasn’t new (it’s basically staff aug), the mindset was very different.

So how did FDE’s solve this?

Buying is Easy; Actualizing is Hard

Enterprise companies know that change is hard.

This is why sales cycles are typically 18-24 months, and most will not bring on more than 1-2 new tools per cycle.

You have to deal with things like…

• Data migrations

• Tooling integrations

• Code refactors

• Hiring new people

• Training existing people

• Capacity management

• Comms (internal, external)

• Coordination (internal, external)

• Board politics

• C-suite buy-in

• Budgets

• Mercury Retrograde (just checking if you’re still reading)

Oh, and ultimately achieve the goal or ROI you set out to achieve. Otherwise, WTF did you start this in the first place?!?

With this laundry list of challenges and headwinds, it’s no wonder why there’s a >90% failure rate for enterprise AI initiatives.

Add to that that the fact that the world is changing more rapidly than ever, sometimes on a quarterly, monthly, or weekly basis.

But all hope is not lost.

Solutions! Embedding Engineers AND Executives

The goal is to close the gap, but how?

FDEs were a critical FIRST part of that solution. They brought…

• Proximity. They sit inside the customer’s team and see the actual workflow instead of the sanitized version that showed up in the RFP.

• Speed. Something working in weeks (or days… or hours) rather than quarters, which breaks the pilot purgatory that produced that 95% number.

• Ownership of the outcome. They’re measured on whether the customer wins, not on closing the sale or watching usage metrics through a dashboard.

But IMHO, that wasn’t enough. Yes, this addressed the technical gaps. These SEAL Team Six-type engineers can largely address business objectives, but they’re still often too far away from the key decision makers that are critical for decisions and culture.

Two citations that emphasize this.

1. Dan Shipper of Every once said one of the strongest leading indicators of AI adoption is whether a CEO is an avid user of AI (and usually sharing the wins to the team). I’ve witnessed this juxtaposition personally.

2. PwC came at the same idea from the other direction. They found that CEOs whose organizations built strong AI foundations are 3x more likely to report meaningful financial returns. The difference between the 12% who win and the 56% who don’t comes down to foundations and embedding.

The last part is critical. You need an AI foundation, which requires AI champions at the top. And since companies can’t just rapidly hire and replace the entire C-suite in a month, they need outside help… they need a Forward Deployed Executives to help level them all up.

What exactly does an FDX look like? More on that in a minute.

Remember the story of Ben? He didn’t even know he needed an FDE-like resource! He first needed an FDX, an AI-first founder type that could identify the problem and get him over the mental/emotional hurdles that prevented him from making it a priority to hire an AI-first engineer.

The AI Bottleneck is Humans

The smart folks at Anthropic (I’m looking at you, Dario) told us that AI was going to start replacing 50% of white-collar work starting by 2030.

Whoops. It doesn’t look like it’s happening… yet.

We may get their eventually, but right now the bottleneck isn’t the models, the products, the agents, or the token costs.

The gap is how fast humans can successfully absorb AI practices, products, and methods into their companies.

And that’s just the mechanics. There’s also the psychology. Here’s a direct quote from an executive I work with.

“You’re just my security blanket, so I want you here.”

AI is scary and risky. Even if someone hands you a perfect answer, they may not take it unless they trust you.

Why? The rate of change and the potential risks of AI mistakes are so high that they create so much fear, uncertainty, and doubt (FUD). It’s so massive these days that even seasoned executives who have weathered the 2008 market crash and the 2020 COVID lockdowns are affected.

Hell, there’s an executive that I’ve heard about through a friend of a friend. He’s been suffering from regular panic attacks, even throwing up multiple times a week because he’s so afraid of falling behind. I’ve heard others in my direct network say they feel the same pressure.

These execs don’t need another LinkedIn article with 5 new tips. They need a trusted confident to describe their challenges and, most importantly, help them close the gap.

They need someone with both technical AND leadership experience that can help them navigate this chaos and complexity.

What Makes a Good FDX

History doesn’t repeat, but it does rhyme.

A decade ago, one of the hottest hires on the market was a “solutions architect.” This mythical hire had skills and experiences across 3 key areas.

• Technical (i.e. deep understanding of architecture, best practices, etc)

• Management (i.e. costs, timelines, team makeup, etc)

• Entrepreneurial (i.e. business needs, ROI, market opportunities)

You may recognize these factors from the bestselling book The E-Myth.

And rare was the person who could do all three. They were outstanding hires.

Now we’re in an era where the gap requires all three of these plus a few more.

• Adaptable (i.e. able to react rapidly, with high tolerance for change)

• AI-first (i.e. approaching everything from an AI-first mentality)

• DIY, DWY, DFY (i.e. the ability to build, teach, and delegate these skills)

I’ve been doing AI upskilling now for over a year, and you’d be amazed at the number of executives who want and need a coach to sit in the trenches with them (to test, tinker, optimize, vent, etc.). They need someone that’s like them enough to translate into their words and needs as well as technical enough to knock down walls with and for them.

Can I Help?

Ok I didn’t mean to make this a pitch, but why not? Here’s a bit about me:

• MIT BS + PhD.

• 18 years of startup experience (6x co-founder)

• Enterprise, SMB, and security/compliance experience.

• Techstars Mentor x5 cohorts.

• Coached hundreds of people.

• 12 months of AI-first consulting, training, up-skilling.

• Wrote a book, Save Your Startup, to help founders with actionable playbooks.

Enough about me. What matters most is an honest question to. How can I help?

I’d be happy to riff on any ideas. DM me on LinkedIn, X, or Substack.

Now back to the article close…

Story 2: “My CTO is an OpenClaw”

Tyler is a non-technical colleague that I respect, and I was thrilled he trusted me with such an ambitious bet.

I had been sharing AI news with him for weeks/month. Then on Sunday, January 25th, 2026, he asked, “Thoughts on clawdbot?” (later rebranded to OpenClaw). I basically shared that I bought a Mac mini and was about to go all-in. I said it would be as pivotal as Opus 4.5, and the world would understand this in under a month.

Tyler didn’t just say “cool” and move on. Within one week, he had two OpenClaws running his company, one as the CTO. By week two, he was already shipping 3-5x and reduced his team size by half. He’s the CEO of a small, and aggressively AI-first startup.

Similar to Ben, an FDE wasn’t the bottleneck (at first). The first step was another colleague, but an aggressively AI-pilled one, to push him to take a swing. And swing he did. Now, the student became the teacher. Tyler is now so far ahead of me it’s not funny. And that’s awesome. We continue to push each other.

The Billion Dollar Opportunity

I made a bold claim in the title. Is this really a billion-dollar (or more) opportunity?

Absolutely.

But did you catch that I used the word “unblock” vs “unlock” in the title? Why?

First, “unlock” is an overused AI-slop word… abused like my previous favorite punctuation, the em-dash.

It’s also the wrong metaphor. What’s sitting in the way is more like blockage of our own creation. It’s human, it’s emotional, and clearing it is unglamorous work that somebody has to physically show up and do.

US companies are easily spending 100’s of billions of dollars in aggregate to adopt AI as fast as possible.

And again, >90% of these things fail to achieve the ROI… yet.

FDEs were a key part of that solution for forward-thinking companies like Palantir. However, industry-wide, it’s not enough.

If we see enough FDX’s help scope, scale, and deliver better results and move these percentages higher, we’re going to see higher percentages of AI adoption with real, measurable ROIs. This will justify additional investment. This will accelerate a virtuous cycle.

A billion-dollar opportunity is awesome, but that’s just the beginning. The bigger story is the career opportunity for executives worried about AI replacing them (no need to have panic attacks when the amount of inbound demand is rising). Maybe AI will replace us all eventually, but not today in 2026. Today there’s a gold rush of opportunity because there are gaps everywhere, and businesses are just looking for people to help close these gaps.

Maybe that’s you.

I know it’s certainly me.

Which is why I’m so excited about where my career is headed leading into 2027.

I hope you’re feeling more hope than anxiety.

Until next time ✌️

PS. Wrote By Me

There’s a trend these days of Substack articles being 90-100% AI-generated with little to no human editing. That’s a shame. This article is the opposite. I rewrote this draft 4 times by hand before asking AI for assistance, pulling in some stats and research. Then I used Grammarly for spelling, grammar, and brevity suggestions. Then I rewrote sections again because I’m determined to focus and followthrough.

I reject many of Grammarly’s suggestions because I like an occasional cuss word. I also love run-on sentences, ellipses, and other writing no-nos. In the age of AI perfection, I prefer to be proudly imperfect. I hope you’d consider approaching your writing the same way.

So logically, if AI can help us complete tasks 2-100x faster than before, we should be UNDERWORKED and experiencing ZERO burnout, right?

If only it were that simple.

ALL THE THINGS!

You may be too young to recognize this meme, but it’s the feeling most people in tech had when AI started accelerating their efficiency.

Why?

Because for years, all of us had dozens, if not hundreds, of side projects we could have started but had to shelve because there weren’t enough hours in a day.

Then AI came along and teased us with a seductive lie…

Claude: “But, maybe you can do it all now that I’m here!”

For the past decade, I’ve been a repeat startup founder + new dad + always ambitious person. I had over 100 article titles + outlines in draft. I had a someday maybe list full of 50 projects to start “when I had the time.”

I started to dip my toe in the water and YOLO some of these side projects between meetings. Queuing up Claude in 5-minute increments and then letting it rip before jumping to the next call.

The crazy thing is it started to work…

Things I wanted to try, explore, and write about were working, and this only 10X’d my ambition to start more projects! Spin more plates. ALL THE THINGS!

The Crash

You already know how this ends.

Because it always ends this way.

There’s the quote: “You can have anything in life, but not everything.”

Even when AI can 100x your productivity on a single task, we humans still have a finite number of seconds in a day.

And we’re not machines. We’re not meant to operate at 100% speed every waking hour.

What happened was I started to notice I had something like 40 “proof of concept” projects going on, and I was starting to feel that pang of burnout growing inside me. Each project was a new open loop, a new responsibility, and a new thing to tend to.

I used AI to reduce my required work, so I invented an endless amount of make-work.

Make Work: “work assigned or done chiefly to keep one busy”

Don’t get me wrong, I had fun along the way. But this is a failed strategy I had to course correct.

Less, But Better

In his book Essentialism, Greg McKeown beats the drum about doing less, but better.

I think this is critical in an age of AI.

Rather than using AI to expand the number of things you’re doing (horizontal), I have come to realize the best strategy is to go way farther on the things that matter (vertical).

In short, focus and follow through.

Personal Examples

I didn’t intend to write this article this morning, but I had to to stop myself.

I had another one titled “Sesame Street Simple” (coming soon) that I was trying to rush out the door. However, as I kept sitting with that article, I realized I wasn’t giving it my very best. Maybe it was a solid B/B-, but the topic deserves an A+ effort to have the impact I want it to make.

So I stopped.

And I decided to give it at least 2-3 more revisions with focused time and attention.

Partial vs Total Eclipse

Am I being a perfectionist?

An overdramatic artisan?

I don’t think so.

Garry Tan penned an amazing article about the difference between a partial and a total eclipse.

The main thrust? You think the difference between a 99% and a 100% eclipse is a mere 1%, a rounding error. But visually, it’s a 100x difference. A partial eclipse feels like the dimming of a cloud. A full eclipse is like an eerie nighttime befalling the world.

The experiences could not be more different in effect.

Likewise, things that we love (products, books, movies, etc.) pay the price of that last 1% to deliver that 100% experience.

Here’s the hard part. That last 1% may feel like 50-90% of the total time, which is why so many people just say “good enough” and ship.

The Better Way Forward

Now that AI makes things 2-100x more efficient, there’s no reason not to pay the price of that last 1%.

The only way to do that is to be ruthlessly focused and then follow through on the few open projects that matter.

I’m still making lots of mistakes because it’s so hard not to chase the shiny (ALL THE THINGS), but the rewards are worth it.

To that end, I hope to publish fewer articles, but go deeper on each one.

You and I both deserve that.

And no one should be experiencing burnout in the age of AI. The entire point was to offload all the hard labor so we could be save time, focus on what matters, and be more human.

A clear example of this is the utilization vs touch time curve. If your day is scheduled to the minute, a simple 5-minute phone call may take you days or weeks to get to. But if you always have a slack 30-60 minutes in your day, you can get to it in a few hours (if not immediately).

This was beautifully illustrated in this passage of The Phoenix Project.

So my hunch was, the same mechanism would apply to a game I used to play…

What’s Incorrect

Do you remember slider puzzles? If you were a child of the 80s and 90s, you probably saw one.

Anyway! As a kid, these things were frustrating. You had one free space and had to work really hard to get things sorted in the right way. If you don’t know what you’re doing, it can take you hundreds of moves (similar to a newb trying to solve a Rubik’s cube).

My line of thinking went, what if I had two or three spaces open? I bet it could halve the time because you have more degrees of freedom.

10 years ago, I didn’t have the patience or access to someone that could test this. It just felt true.

Enter AI

AI makes it stupid simple to turn ideas into code into demos. So I was like “Hey, I should make a simple web page to show this to drive the point home.”

Problem. The app worked beautifully, but not in the way I had hoped.

I expected it to always be much faster when going from 1 to 2 open spaces, but that wasn’t always true. Depending on the starting position, it could be even or even slower! Check out this screenshot.

Luckily, all hope was not lost. When AI ran 1,200 simulations, I was able to confirm that I was directionally right, even if the jump wasn’t as dramatic as I had hoped.

When you’re an expert and use the fastest path to a solution, the extra space does save some time (up to 2x), but it’s not a 3-5x gain that I expected. Check out the following animated gif of a typical run.

You can run this yourself by visiting the test page here.

A Humbling Experience

Now that the costs of running a simulation are so low, it’s easier to validate AND invalidate claims and beliefs. This is awesome if your ego can take the hit.

And that’s what sucks.

These programs were packed with knowledge I paid hard-earned money for, but it’s difficult or impossible to access the information on demand. At least once a month (and sometimes weekly), I refer people to these resources, but it’s hard to quickly pull up the exact CD, track, and minute/second where the quote is from.

Want to recall Jim Loehr’s 15-second recovery ritual from a Memory Optimizer disc? Your options: re-listen to 7 hours of CDs, or trust a 15-year-old memory.

There’s no search. No way to cite a passage. No way to hand a chapter to an AI assistant and ask, “What was that quote from Caroline Myss about a nation’s collective self-esteem?”

A $2,000 course I own outright is LESS useful to me than a random blog post that shows up in a 5-second Google search.

Again, that sucks.

Finally, I want to go further than search. I want this knowledge to be pluggable in my AI tools. I want it all in markdown files where it can be linked, searched, quoted, and pulled into context when I’m working on something related. Everything stuck in audio just… sits there.

Again, that sucks.

So I fixed this. I used AI to run the following pipeline for courses I bought as CDs or through online portals.

1. Transcribe (verbatim with timestamps)

2. Distill (down to an executive summary per file)

3. Vault (in my Obsidian for easy search/read)

4. Connect (into all my AI tools as needed)

This all happens with a local speech-to-text (free, private, runs on my Mac) and AI agents doing the distillation; the first program took an afternoon, including building the pipeline. Every program after that took one command. 9 programs and 55 hours of audio later, my “dead” course library is roughly 430 searchable notes.

It’s magic. I have another 200-300 hours worth of material to go through, but I can already yank a quote out of thin air in seconds, something that would have taken me 15-30 minutes of digging before.

And if you want a skill file to get started, here’s a generic one to adapt with your AI Agent of choice.

Yes, humans are flawed, and they’ll never meet our idealized expectations.

However, I would argue that it’s more painful to find out that you’ve lost the chance forever.

I know because I just discovered that SIX of my heroes have died in the past 5-10 years.

I was updating my bucket list, and I decided to add a few people I’ve been meaning to connect with since college. Googling led to one obituary after another, and I finally gave up. Too depressing.

The Tail End

Tim Urban wrote an extensive article on this subject. The stat that still shocks me is that by the time a kid turns 18, a parent has already spent 93% of their time together. The remaining 7% happens for holiday gatherings and summer vacations.

The paradox is that you think you have decades of time ahead, but the actual aggregate amount of time left is in the single digits.

Half-Lifes

There’s a term in science called the half-life. It’s defined as the amount of time it takes for half of the radioactive material to decay into something else. Some materials last forever (e.g., uranium), and others decay quickly (e.g., Oxygen-15 and other isotopes used for medical scans).

I’ve been thinking about this ever since finding out many of my heroes were dead, many of them way earlier than expected (early 50s). My assumption is that they were all younger and would live longer. I thought I had 30 years easy. In reality, the half-life was 10 years. And then another half-life of 10 years.

To those who aren’t good with math, I’ll summarize. Life is fucking short.

Do This Now

Now, at age 45 and looking around at those who remain (mostly in their 50s and 60s), I realize I have less time than I think to make it happen. The optimist thinks I have decades. The reality is that if I don’t make it happen by 2030, all bets are off. Too much risk that life happens and another hero becomes an obituary.

I realize this is a bit morbid to think about. However, I lost both my mom and biological dad a month ago. I lost my sister-in-law in her late 30s two years ago. And one of my good friends in Oklahoma has a brother-in-law battling stage 4 cancer at age 38. If anything, it’s just being a realist.

We all have less time than we think! If a person matters, make the time now to avoid regret later.

• The people are REALLY nice.

• Making friends is REALLY hard.

Strange, yes? But stick with me.

Oklahomans are genuinely, reliably nice. Like SUPER nice. It’s one of the things I love most about living here.

On any given day:

• Every stranger will wave and say hello.

• The guy at the coffee shop asks about your weekend (and genuinely waits for the answer).

• Then they go home to hang with friends they’ve had since the third grade.

That last part was what the warning was about. Most people here have been here forever. They’ve got their people, their church, their standing Friday-night plans, and they are not shopping for anyone new. So you get five friendly minutes (and rarely a sixth). And you’re never invited to hang out or join the BBQ. The friendliness is real. There are just no open seats at the table.

For a while, I took it personally. Then I saw the “nice vs. mean” meme.

Nice vs Mean

You’ve probably seen it, and it basically divides the country into 4 quadrants:

• Northwest: “acts nice, is nice”

• Northeast: “acts mean, in mean”

• Southeast: “acts nice, is mean”

• Southwest: “acts mean, is nice”

Yes, it’s a gross over-generalization (to the point where you can find different conflicting versions of this meme!). Still, there is some truth to it.

I lived in Boston from 1999 to 2009, and everyone just felt angry all the time. However, when you actually got through to someone, they were as nice as can be. Then there’s the stereotype of the southern sorority girl, who is as outwardly friendly but secretly judging you.

I personally disliked the particular comparison because I feel “meanness” was the wrong dimension to score, so I made a different grid.

Two New Axes: Approachability & Depth

Approachability. How easy it is to get in the door.

Depth. Whether anything’s on the other side of that door.

The niceness label hid this from me: the two move independently.

• A place can be wide open at the front door, but feel empty inside.

• Or locked at the front, but it’s full of heart and soul inside.

Plot the places I’ve lived (or know people from there), and they each land in a corner.

Oklahoma. Low wall, warm welcome. Nothing behind it to hold onto. You’re through the door before you notice, and the door doesn’t go anywhere.

Argentina. The corner everyone wants. Low wall AND real depth. You meet someone at lunch and you’re actual friends by dinner. Open and lasting in the same breath.

Denver. A higher bar, and not much waiting past it either, because half the city is on its way somewhere else. Friendly enough. But nobody’s putting 20 years into a friendship with someone who might leave by spring.

Denmark. This is the one that gave the whole idea its name. A guy in my men’s group told the story: the Danes are tough to crack. Cold at the gate, slow to let you past it. But once you’re in, you are FAMILY for life. He called it “high wall, warm home.” Apparently, others refer to it as “hard shell, soft core.” Well said.

What the map is actually good for

Two things.

One, it explains the friction. That lonely feeling inside a friendly town stops being a paradox once you separate “easy to meet” from “easy to keep.”

Oklahoma is warm and shallow for a newcomer. Naming that, I quit expecting the warmth to ripen into weeknight dinners on its own. It was never going to, and that’s fine. Oklahoma is just the wrong corner for what I came looking for.

Two, don’t take it personally. You can’t force people to be something they’re not.

When you want depth, and you’ve landed in a warm-but-shallow place, what you’re feeling is the local wiring, the default setting of nearly everyone around you. It says nothing about whether you’re worth knowing. And it runs the other way too. The person who reads as cold may have a warm home behind a high wall, and the wall is just how their world is built. Stick around long enough and you find out.

I’ve been on both sides of that.

People who lit up at me for ninety seconds and evaporated. And people who felt shut until, six months later, they turned into some of the best friends I have.

What you want out of a friendship might not match the norms of the place you’re standing in. Worth knowing early, because it changes where you spend your effort.

In a high-wall-warm-home place, you spend it on patience. The wall is the toll, and the home is real, so you pay it.

In a low-wall, shallow place, you stop waiting for an invitation that was never coming. You go build the depth yourself, with the few people looking for the same thing you are.

The map won’t lower a wall or warm a home. It just tells you which one you’re standing in front of, so you stop blaming yourself for the architecture… and you can make a high-agency decision on what to do about it.

It’s also terrifying and overwhelming. How can we hope to have a chance to stand out from the crowd? Is the bar of exceptionalism too high?

Back to Reality

It’s sobering (and sad) to reflect on our day to day interactions with the people we meet. Aa simple example: in the last 30 days, do you have one memorable moment interacting with a person at a grocery store, on public transportation, or at a coffee shop? If you are like most people, those situations are seen more like a chore than as an opportunity to have a meaningful connection with another human being. I know because I’ve had experiences in my past where I’ve spent 30 minutes getting groceries and never once looked at a person in the eyes. If I were forced to have small talk, I would generally end with a one-word answer and let the conversation die off from there.

I have no data to back this claim, but here are my observations of how conversations with strangers break down. Of course, this will change based on location, age, upbringing, culture, and a variety of other factors. Still, I bet you have at least observed this at some point in your own life.

• Typical: Other people in the immediate vicinity might as well not be there. Most people are not noticed unless they are in the way or making a scene. Actual communication barely acknowledges the other person and is limited to only what is necessary to complete a transaction. This represents almost half of all conversations with strangers public.

• Friendly: Conversation may start with a routine “how you doing?” and ends with a quick exchange of “I’m fine” or “good” by both parties. It’s friendly, but there is no real thought or feeling that went into it.

• Above Average: A person might actually ask a random question, OR a person may respond with a small talk question with a more thoughtful, meaningful answer. This conversation may last for 2-3 more sentences before returning to silence.

• Great: There might even be an exchange of names along with an “it was a pleasure to meet you!” at the end. Already, this experience is in the top 5% of all interactions with strangers.

• Exceptional: Someone watching the interaction might think the two people have known each other for years because it appeared to be an honest to God, meaningful conversation. In these situations, the two people are likely to remember each other the next time they cross paths. These types of interactions happy (in my experience) less than 1% of the time and are, therefore, exceptional.

By the way, let’s compare the two extremes just to show you how little effort it takes to go from a non-event to something much better.

Friendly Interaction

• Store Clerk: Hi, how are you doing?

• Shopper: Good. You?

• Store Clerk: I’m fine. Nice day out, right?

• Shopper: Yes it’s beautiful out.

• Store Clerk: Here’s your change, have a good day!

Exceptional Interaction

• Store Clerk: Hi, how are you doing?

• Shopper: Oh just peachy!

• Store Clerk: Peachy, eh?

• Shopper: Yeah I love that phrase because sometimes life is warm and fuzzy, and sometimes it’s the pits.

• Store Clerk: Ha! That’s clever.

• Shopper: Well, tell that to my wife. The dad jokes can really wear her out because they eventually become too routine.

• Store Clerk: Oh I hear you! We have 3 kids, and my husband always makes terrible puns.

• Shopper: Oh yeah? That’s cool that you have 3 kids. Any fun plans for the weekend to enjoy the summer or do you like to keep it nice and relaxing.

And so on until…

• Shopper: Well I hope you have a good night with the kiddos, Mary!

• Store Clerk: You too Rick! See you next time. Say hi to Evelyn.

Now here’s a question: was there anything that was rocket science here? Yes, it’s helpful to have a few quips or convo variations already on hand. However, the key factors were showing interest in the other person, not responding with stock answers, and bringing in a little vulnerability first to allow the other to loosen up and waken up from their routine. It’s not rocket science, and yet it’s by far the exception.

And Then There’s The Internet

Things get even better (or worse) when you are on the internet, specifically social media. There is a known rule of thumb in Internet Culture called the 1% rule. Basically, only 1% of all users of the system actually contribute new, meaningful content whereas ~10% will share/edit that content, and 90% will just lurk and consume. If you are actively posting new things, congratulations! You are exceptional.

And then there is the darker side when negativity is amplified online and where people (particularly marginalized) are abused and harassed into oblivion. For the individuals dealing with this day in and day out, the bar of an exceptional experience is so low that you don’t even have to be friendly. Frankly, as long as you’re not a dick people are pumped! I’m looking at you Twitter…

And Everywhere Else…

The bar of exceptionalism (loosely defined as being above in the top 1 to 10%) is very achievable in other aspects of life as well. Government statistics show that less than 5% of Americans exercise 30 minutes a day. Given that the average American consumes a combined 7 hours a day of TV and Internet usage, this is not a hard metric to hit.

Yes, some areas of life are much more competitive. Career is one. Salary is one. Sports is another. But many, like just being a decent human being or staying reasonably healthy, are a cake walk if you merely try and commit to it. Hell, even something as simple as having good manners or giving a damn can be a novelty in a world full of apathy.

Is this article my best work? No, not at all. But I had an idea, and I wanted to see it launched in the world versus die (as a result of lack of attention) in my ideas list. Everyone in your life has ideas, and only a few, the exceptions are actually doing something about it. Be that person that tries, commits, and executes and people will take notice. You will be the 1% of people on a social media platform that actually has something to say versus repeat it (10%) or sit on the sidelines (90%). You will be the exception. Be the exception!

Photo by Pedro Kümmel on Unsplash. Even the small (yet important) act of being kind is a remarkable quality to have.

Article originally published Friday, July 6, 2018 on rickmanelius.com

This could not have gone better. It was beautiful, and I’ll have more to share on it another day after I process the stories, pictures, and memories.

2. Banana Water

I think my brother is pulling my leg, but he regaled us at breakfast for a full 15 minutes about several people who had life-changing experiences with… banana water? My bet is it’s more snake oil than the next hot trend, but I’m willing to try anything once. But $5/can?!? Now that’s bananas.

3. 45-year-old tree

My best friend’s mom showed me this giant, 100-foot-tall tree she had planted in her backyard when her son was born. It was a great reminder that while good things take a long, long time to grow, you can still experience the beauty of it all in your lifetime if you’re patient and stick around long enough.

4. Principles

I’m only a chapter into Ray Dalio’s book, and it’s good. I was a huge silver bug (thanks, Uncle Warren!) and didn’t realize the Hunt brothers were the richest people in the world until they almost went bankrupt by mistiming the market. It’s a good reminder that timing is everything, and no one is immune to the booms and busts of life.

5. Reconnection

It’s funny how many times I’ve written off a friendship because 5-10 years went by. And yet I’ve now had more than 5 experiences where we’ve picked up like a day hasn’t gone by. Maybe I’m just lucky, or maybe I should make it a point to keep reconnecting with others from my past.

6. New Connections

Apparently, it’s still harder to make friends than it’s ever been. I admit that it does take some intentionality. However, the reverse is also true… people are craving to find connection. If you provide the means, you’d be surprised how many people show up.

7. Breaking Sleep Cycles

My 70-year-old uncle works 7 consecutive 12-hour overnight shifts as a pharmacist. And somehow he manages to get in 20+ miles of biking a day. He’s my standard for health and longevity.

One thing that surprised me was how sporadic his sleep is. If he wants to nap at 3 PM, no problem. If he wants to get up and start reading at 2 AM, no problem.

So I couldn’t sleep last night, which is why I’m up writing this at 4:19 AM. Might suffer later, but I’m enjoying writing while the world is quiet.

Thanks for Listening

It means the world to know you’re out there, and I appreciate all the comments and feedback.

Until next week! Thank you 🙏

The Captain Class was just such a book.

Several times, I had to pause the Audible version and pull my car over to take notes and consider the ramifications.

Best of all, the material wasn’t just some talking head giving a hot take on a sports talk radio show. Sam Walker had done extensive research across every sport and every era, looking for patterns that explained what made exceptional teams so exceptional.

The big theme of the book was that much of it came down to an elite captain, but not in the way most business books would portray it. Key characteristics that made me rethink leadership at the most elite level:

• They didn’t break the rules, but they often pushed the limits of norms and behaviors.

• They had extreme emotional control.

• They weren’t the prima donnas. They often did the grunt work in the background.

Why Revisit This Book Now?

I have the privilege of speaking with team and company leaders daily.

As you might have guessed from my previous articles, everyone is struggling with AI. More specifically:

• Predicting the future of work, management, and leadership.

• How should they adapt their strategies and tactics to be effective leaders in this new paradigm?

To be fair, I struggle at times too. There are lots of conflicting predictions, advice, etc. However, startup life has always returned me to one key lesson: it’s less important to have the right answer than to have the right algorithm. And in a time of rapid, persistent, and accelerating change, my bet is to widen my search and research over different styles and philosophies to find the best algorithms for adaptability.

I’m betting there are some lessons to revisit in The Captain Class (and other leadership books) because these lessons will be even more applicable in the age of AI.

The number one that comes to mind? Sheer doggedness and grit. I see a lot of leaders throwing up their hands and giving up trying to keep up with it all. I think this is the wrong approach. Staying curious, determined, and gritty… that has been extremely helpful for me over the past few months. While others are quitting, I’m doubling down. The result? More and more people are coming to me for advice because they know I’m actively testing and experimenting every day.

Your mileage may vary with this book. However, if you’re looking for a different angle on leadership, consider adding it to your reading queue.