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“Aristotle Was a Smart Bugger”

(And: Why You Should Read This Book)

Explain the thinking of Aristotle in the speaking style of Roy Kent.

With those 12 words, my relationship with Generative AI (“GenAI” for short) began. And (although I didn’t realize it then) I’d also taken my first step toward concluding that this book needed to exist.

It was November 30, 2022. ChatGPT’s launch day.

At the time, I was a senior member of a strategy team at a software company. With a business built on a database used in some of the world’s most powerful traditional AI.

Think “predicting your Uber’s time to curb” or which “Netflix series you can’t resist binging on next.”

So, I knew that colleagues and customers would be interested in a point of view on this strange new entrant into the tech landscape. (Not to mention family and friends, as well.)

From my perspective as a technologist, philosophy major, and Ted Lasso fan, that first interaction ticked three boxes:

1. The Large Language Model (LLM) under the hood worked as expected. (If you’re not chuckling at the cleverness of my first experimental interrogation of dense semantic space, don’t worry. As a gift with purchase for reading this book, you’ll gain a level of ability to parse nerdy inside jokes about AI that you never knew you needed.)

2. The output was pretty accurate.

3. It was also hilarious.

Since then, I’ve put GenAI to use in work and life in a great many ways beyond bringing some gruff, blunt, profane-but-big-hearted football-leader energy to summarizing deep thoughts.

Including (a good while ago) using it every day on the job.

On a different timeline, I could amply satisfy anyone’s curiosity, personal or professional, with a few words which they’d find unsurprising, unobjectionable, and readily actionable. And receive with a slight smile, a shrug, and something along the lines of “yeah, that seems about right.

In the best timeline of all, from day one anyone asking an LLM to explain what it was for would have gotten an answer like this:

I’m a machine for joyful, hyper-efficient exploration of humanity’s written knowledge. And as an added bonus, I can help anyone do at least a little bit (and maybe a lot) better at any cognitive endeavor they care to invest some effort into doing well.

But we’re not living on that timeline.

To the extent that too many people are losing sleep over whether or not this thing will kill us all. Or–more realistically and closer to home–whether it will cost them, their colleagues, or their kids a job.

The premise and promise of this book is that we can prove there’s a “better together” way to turn AI’s raw power into commensurate outcomes in real-world cognitive work. One in which our human brainpower makes us more rather than less valuable as employees because GenAI exists.

At least to employers who are pragmatic, prudent, and interested in making progress toward results that matter. In a manner that seems simpler, feels safer, and happens sooner than other ways to go about getting them.

In the next chapter, I’ll start to make the case for how and why what I call the practice of dual intelligence is that solution. One that may start off counterintuitive, but, by the end of the book, just might come across as blindingly obvious.

Here I’ll set the stage for how it changes the game that you, me, and anyone else who loves thinking for a living aim to win.

The first big fork in the road toward future proofing thinking for a living  is recognizing that we don’t have to beat the AI.

We don’t need to outcompete each other, either. We’re not like hunters trying to outrun an angry bear. In fact, to survive and thrive we’ll be better off cooperating to raise each other’s game rather than scheming to leave colleagues in the dust.

Because we only need the other available alternative ways of putting AI to work.

After years with both a front-row seat for how large enterprises have given it a go and a hand on the tiller making it happen within tech companies, I like our chances.

But no one can do the work that’s required of us.

Which brings me to three reasons I felt a sense of urgency about writing it.

First: every day that passes in which a human who values their mind experiences less rather than more happiness or diminished rather than bolstered economic prospects because GenAI exists is a small personal tragedy. Because ways to tip the balance for the better are within our reach.

Second: every day that humanity isn’t firing on all cylinders to make the most of GenAI is a small civilizational loss. We’ve had four figures-worth of such days, and the clock’s still ticking.

And, last but not least: ChatGPT famously shipped without a manual. I wish it had been accompanied by this book.

Not because it describes the only way to think about GenAI and put it to work.

Rather, because it does offer an easy on ramp with a lot of runway for progressively accomplishing more. In a way that will give you confidence that you’re strengthening rather than eroding your mental muscles. While opening the door to compounding benefits for you, the organization at which you work, and even society in general, if you so choose.

At this point, it would be convenient to say that “everything you’ve heard about GenAI is wrong.” But that would be hyperbole. And when it comes to AI, there’s more than enough of that in circulation already.

So let me go with a statement that I believe isn’t an exaggeration: pretty much all of us could benefit from a reset. Meaning a re-interpretation of all that we’ve heard.

And a re-grounding of how to interpret whatever we hear next on a new set of first principles.

To that end, I’ve tried to write this book in the manner of a “fresh start” with GenAI, for any and all readers

To that end, I invite you to adopt a beginners’ mind.

If you’re an expert, I expect I’ll sometimes be telling you something you already know. But (hopefully) contributing to your ability to educate and elevate others. If you’re a newbie, my goal is to require no prior knowledge in order to get every bit as much as an expert out of reading it.

Rest assured that on both of those fronts and more, GenAI has politely but mercilessly beaten me up a thousand times over. Or let’s say instead “coached,” since I willingly invited the feedback.

Went out of my way to get it, in fact.

As a downpayment to “show rather than tell” walking the talk of dual intelligence, I’ll share GenAI’s single biggest impact by far on its creation.

It wasn’t producing the prose.

The first reason why was personal: I enjoy the act of writing.

The second reason why is geeky. The third is principled.

Taken together, they’re a formula for putting GenAI to work that you can try for yourself. That will also help you take the emotion out of the tricky question of if and when to lean on GenAI for writing. That holds true whether you’re biased toward doing it yourself like I am, sorely tempted to turn to AI because you find writing to be a chore, or somewhere in between.

In Test-Driven (software) Development (TDD) you write a test before writing the code that makes the test pass. This forces you to specify what the code should do before deciding how it should do it.

I took inspiration from this practice when I started to write this book in earnest.

I wrote a series of 50 to 100 word promises to readers. One to anyone who picked it up. Another to GenAI novices. Another to GenAI experts. As well as one each for readers who are knowledge workers, who manage or lead knowledge workers, and who prepare young people to enter the world of work, respectively.

These were my tests.

Obviously–just like in TDD–the first pass with a blank slate is 100 percent guaranteed to fail.

To iterate toward success in the world prior to GenAI, I could refer to the promises and try to grade each draft myself. I could ask human readers to use them as the basis for feedback.

The goals: eliminating the defect of failing to fully deliver, while increasing delight as a result of delivering especially well.

The principled part of the equation is asking whether the existence of GenAI puts a shot at fewer defects and more delight within reach. To put this into (ahem) testable language: has it lowered the cost of critique? And has it lowered the price of play?

The answer is yes to both, by a lot.

I tested every major revision of every chapter against them using GenAI. First as a grader to tell me if and how I came up short. And then (once I’d cleared that bar) as an advisor for how to put a cherry on top.

By my back-of-the-envelope calculation, the more than 1,000 times GenAI challenged me to do better would have required my human editor to turn giving me feedback into a full-time job for the better part of six months.

The gift with purchase is that this approach answers the potentially thorny question of when to delegate producing prose to AI: you should only have AI do the writing if it improves test results.

Those tests of prose aren’t 100 percent deterministic like they would be for code. But you’re in the driver’s seat to honestly make the call. Or to dial a friend for a human second opinion when you’re well and truly stumped.

In the case of this book, I was both well-equipped and highly motivated to outperform it at remediating defects nearly every time. I also had a thumb on the scale because I affirmatively wanted it to consistently sound like me.

So verbatim prose generated by AI is pretty much always labeled as such.

But–much as I did, albeit instinctively, on day one–I did find opportunities for play. One of which is presented in a sidebar at the end of this chapter. [Note: this is not currently posted to this website]

The other is offered below.

It occurred to me while writing this introduction to task AI with translating one of those promises into locker-room talks in the style of Roy Kent. I hope you find its output to be a right proper dollop of delight:

If you’re a knowledge worker: I wrote this bloody book for you. Because GenAI is here, whether you’re ready for it or not, and you might as well get damn good at using it. Not just to get more shit done, either. To become the person who sees what’s possible, helps everyone else see it too, and actually figures out what the hell to do next. Do that, and you can build a future of work that’s more productive, more rewarding, and a lot less stupid—for you and the people around you.

End of Chapter Reflections

One take on the impact of GenAI is that as it becomes more capable of handling more tasks that’s intended to be optimistic about the future of thinking for a living is that human “taste” and “judgment” will become more rather than less valuable.

It’s a nice sentiment,  but it begs the question of how much “taste” and “judgment” any of us have or need to cultivate.

Or more bluntly–to channel my own inner Roy Kent–what the hell does that bloody mean?

The reason this book exists is to define a more tangible, plausible, and precise path to future proofing being paid well for brainwork. Which is why in this introduction, before diving in to advancing the argument, I wanted to paint a picture rather than offering platitudes.

Showing a glimpse of all that there is for us to do. GenAI’s existence notwithstanding, and all the more importantly, because it’s at our back and call. A path forward shaping the future of thinking for a living that’s fueled by ambition rather than circumscribed by fear.

Because we can exercise care at turning intentions that may live notionally or as unstated expectations in our or our bosses’ heads into words that we and AI can do something about.

We can practice rigor using promises as tests applied to written artifacts and experiential outcomes at a previously impossible pace and scale.

We can be diligent about inviting critique and remediating defects. We can be clever about engaging in play in search of delightful upside surprises.

And we can be strategic about finding “better together” outcomes where AI and us bring a puzzle-to-be-solved of strengths and weaknesses and opportunities and constraints to the table.

That’s just the tip of the iceberg of opportunities for combining highly capable AI with human ingenuity and drive to get better results than each without the other. While having more fun doing it.

AI’s take on how Roy Kent would translate my painstakingly crafted promise into a locker room talk keeps the faith with its original intent. Including a future in which our work is “more rewarding, and a lot less stupid.”

But to bring it back to how I would pump up the team: the ball’s in our court to create a more beautiful game of brainwork.

If you’re willing to entertain that prospect, please read on.

Copyright 2026 Bryan Kirschner