Teach Them What Good Looks Like
I had the pleasure and privilege of leading a training workshop at a Google offsite in Phuket this week. I will write more, later, about that entire experience, but there are a couple of things that I couldn’t wait to write about.
First, imagine my surprise a couple of months ago when I got a note from one of the key leaders at Google, asking if I would be interested in designing a workshop based on my approach to AI.
Google is asking me to talk about AI - mind-blowing and humbling to say the least.
I wasn’t expecting this
Something happened during the workshop that caught me off guard and stuck with me throughout my day and my trip home.
A question came up during the Google training session. One of those moments when the room is warm, the afternoon sweets and coffee have settled, and the collective attention of forty people turned to me.
“Chris… a lot of what you’re showing us seems to come from your thirty years of experience. What about younger knowledge workers? People who haven’t lived through all the shifts you’ve lived through. How are they supposed to succeed with AI without all that accumulated wisdom?”
I am paraphrasing, of course, both for dramatic effect and to mask the fact that my memory isn’t what it used to be, but I think you get the idea.
I smiled. I paused — in equal parts theatre and aging. But the answer was already there, fully formed, almost waiting for the question to find it.
“Teach them what good looks like.”
That’s it. That’s the whole thing.
Teach them what good looks like so they can recognise it — in themselves, in their teams, and in their collaborations with AI. Because if they can’t recognise good, they will drown in the ocean of “generated.”
It struck me that for all the talk about prompting, all the obsession with agents and workflows and feature releases, almost none of it matters if you don’t know the difference between something that exists and something that is good. AI can produce more output than any of us can read, let alone evaluate. But AI can’t tell you what to care about. It can’t tell you what’s worth fighting for. It can’t whisper, the way a great creative director once might have, “This isn’t there yet. Keep going.”
The decades that shaped me
During the workshop, I shared a timeline of my life — not a sentimental scrapbook, but a map of the cultural and technological frontiers I’ve lived through. The TRS-80 at RadioShack. The Macintosh “1984” ad. MTV. Hotmail. The first iPod. The early days of social media when the future shifted from linear to generative. The moment the iPhone quietly rearranged the architecture of our behaviour. And the day transformers were invented — arguably the single most consequential technical breakthrough of the century — which set us on the path to the present moment, a moment in which intelligence is no longer a scarce resource.
I didn’t build that timeline for nostalgia. I built it to make a point: my relationship with AI isn’t technical. It’s experiential. It’s the sum of decades spent absorbing taste, rhythm, timing, craft, intuition, and cultural change. It’s the hours I’ve spent with work that answered the brief, that got the job done, and the hours I’ve spent with work that made me stop for a second because it was genuinely great. You don’t forget those moments. They shape you.
Younger talent hasn’t lived through those cycles. It’s not their fault. They just haven’t had enough time on this big blue rock as I have had. And while we can’t change that, we can try really hard to get them up to speed as quickly as possible, using our collected history of work, both good and bad, so that they will recognise it in other’s work, that they come in contact with, and in their own, as they begin to learn, and think, and make.
This is what good looks like.
This is the part almost everyone forgets: organizations teach metrics, not standards. They teach KPIs, not judgment. They teach speed, not sensibility. They’ve become astonishingly good at the numerics of work and embarrassingly weak at the aesthetics of it.
No one teaches a young strategist how to know when an idea is thin. No one teaches a young creative how to feel when a sentence is hollow. No one teaches an emerging marketer how to sense when a story is built on sand. We hand them dashboards and performance targets and a stack of briefs that are already broken, and then we wonder why the outputs feel… off. Polished, perhaps. But hollow.
And now we are layering on generative AI. We are going faster, deeper, wider, but are going in a direction that is ultimately good – for the work, and for the person?
I think “good” is a human aspiration we don’t talk about enough. Good is not perfection. Good is not greatness. Good does not require decades of life experience. Good simply requires intention. To make something good is to decide, before the first word is written or the first frame is generated, that the work matters enough to be held to a standard.
And that standard is deeply human. It’s not a data point. It’s a feeling. A coherence. A sense that everything inside the work aligns — the thought, the expression, the emotion, the timing, the context, the craft. Good is the quiet, unassuming ambition to leave something better than you found it.
The old man in me is getting nervous
I worry that in this new age — when AI can produce a dozen variations before you finish your coffee — we’re losing the ability to recognise good. Not because good has disappeared, but because the flood of “content” has numbed our senses. When everything is possible, discernment becomes the rarest skill in the room.
But that’s exactly why emerging talent can thrive in this moment. AI has given them access to infinite drafts, infinite experiments, infinite attempts. What they need now is the eye. The taste. The instinct. The internal compass that tells them when something clicks. When something holds. When something transcends the generic.
That’s what experience once gave us. But experience doesn’t have to take thirty years anymore. Not if we teach them what good looks like.
We’re in the first era in human history where the next generation can accelerate their judgment — not by cutting corners, but by seeing more, trying more, evaluating more, reflecting more. AI can’t give them wisdom. But it can give them an unlimited canvas on which wisdom can form.
All they need is someone to point to the past and say, “There. That’s good. Now go make your version.”
Because the future of work will not be defined by who prompts fastest or who automates the most. It will be defined by who has the courage and the taste to say: Let’s make this good. Not perfect. Not viral. Not optimised. Just good. And from there, greatness will follow. But this too will take time.
Our role, in this strange new age, is to pass on what we’ve learned about good work and good judgment, so our next generation of creatives, writers, strategists, can move past us, not because they copied us, but because we taught them what good looks like.
The older I get, the more I want to teach - to pass on the experiences and point of view I have gathered along the way. Lately, much of that teaching has been centered around how I think about AI, about the questions and approaches I have defined that lead to my work on projects, using AI as a force multiplier.
I would love to talk with you about the workshops I have designed, and would love to bring to your agency, brand, or organization. You can get a more than a taste of how I approach thinking in thinking in the Age of AI by reading my book, “To Question Is to Answer. How to Think Critically and Thrive in the Age of AI.”
One last thing, before I hit publish... A MASSIVE thank you to those from Google who attended this workshop, and stood in a small but oh so meaningful line to have me sign my book. This was a first for me. And I will never forget that.
