Food for thought: I saw this ad in the Economist shortly after reading an article about the plethora of errors judges are finding in AI generated legal submissions. 

The first store I can recall going to is Mikie’s Corner Store. Mikie was my first brand ambassador and I was four years old. His place offered me everything I could imagine and a whole lot more than I could afford. When I was seven, we moved from Parkdale to High Park and I got to know Pete’s Sunny Bar. Pete’s store was right across from Annette Street Public School and he held me and four other generations of students captive until he died a few years ago. Now his daughter does the same. In high school, there was Mr. Yonka’s Variety Store. I bought my first pack of cigarettes there. Summer jobs and University took me downtown to a whole new world called Yonge Street.

This is what Yonge St. looked like in the 70's - and how it looks now.

All those people, lights, sights and sounds drew me away from the corner stores that I had grown up with and exposed me to a whole new world of products and services; most of which I had never imagined. My old brand loyalties were severed as I explored the shops and bars of Yonge Street by night and worked or attended school a few streets over by day. I was 16.

As a little boy my loyalty was to a Mikie’s Corner Store and the brands my parents taught me that I could trust.

Psychology played a big part in my shopping destinations and purchase decisions.

As a teenager I sought out stores that allowed me to experiment with new people, products and services that my parents did NOT approve of. Discretion became an important purchase variable. As a young (and then mature) advertising professional, I remained on Yonge Street for another 20 years working for agencies like Foster, MacLaren, Baker Lovick and Ogilvy & Mather. The street changed and my needs changed. In my 30s and 40s I knew what I wanted, when I wanted it and how I wanted it. In turn, I found a handful of stores that served me well . . . until I stopped working down-town.

Psychology continues to play a big part in my shopping destination and purchase decisions.

Today the world wide web works the very same way. On any given day there are some people who are using the web for the first time, and some who are closing down their web browser for the last time.

We need to think of the online experience as an extension of the off-line experience – and vice versa – because cognitive dissonance comes into play any time, any where. When you offer customers on or offline experience that is not congruent, brand loyalty gets eroded.

Whether you’re building your online brand or offline brand presence, you need to get to know all of your customers: the young and the old, the new ones and the regulars, to ensure that you understand what they are buying, how they are using what they buy, and how their needs are evolving relative to the world at large. Their product and service utility insights will help you ensure that your brand is mentally ‘visible’: that I can find it, stay with it for life, or come home to it after I’ve gone through my exploration phase.

Aspirin™ is the first brand my mother introduced me to and it is still a trusted brand in my home. I use it the same way she did.

Duct tape is different. My dad and I used it to make heating and return-air ducts air-tight, and as a general utility tape. 

A few years ago my son made me a duct tape kilt. He changed how I look at that product, where and how I now (mentally) position it.

The two safest places for a brand are in the user’s heart and mind.

Streets and web addresses change.

But, like any good friend, you’ll track down those you love.

Many professionals now rely on Ai to write for them, often on the assumption that no one will notice. That assumption is increasingly false. Across law, consulting and higher education, I see Ai used with growing frequency to draft entire essays, court briefs and reports. The appeal is obvious: the prose are fluent, confident and easy to generate. But it rests on a second assumption as well; that what Ai produces is good enough for work that depends on judgment.

It isn’t.

For a while after tools like ChatGPT first appeared, it may have been hard to tell that what you were reading was Ai. But at this point, many attuned readers can spot it quickly, and, as a recent study shows, those who themselves use Ai for writing or editing can recognize such content almost infallibly.

The reason is simple: we know the patterns. And encountering them more often is prompting us to ask a more basic question: why, in many cases, is writing that looks competent still not good enough for the task at hand?

AI writing tends to have a steady, even cadence. It leans heavily on clichéd terms drawn from its training data, like “delve,” “tapestry,” or “showcasing.” It overuses emphatic descriptors like “game-changing” or “transformative,” and relies on tidy triads (“this, this, and this”) and neat oppositions (“it’s not X, it’s Y”).

Every paragraph resolves without friction. And above all, the writing always stakes out a safe middle ground, betraying no sign of idiosyncrasy and no real sense of voice. It’s not any one of these things that gives the game away, but the presence of many at once.

It’s tempting to assume these are merely temporary limitations that will soon be ironed out as models improve. But this isn’t likely, given how language models work. They generate text by predicting the most probable next word in a sequence. They can be tuned to select words that are more or less probable, giving models different personalities. But they cannot escape their dependence on statistical prediction. This means that AI writing will likely exhibit common patterns for the foreseeable future.

As more of us come to see that AI writing is easy to spot, it will change the way we use AI. The question will no longer be whether the output looks polished enough, but whether someone would mind if the document were labeled “drafted by Ai.” In some cases, the answer may well be no: a meeting summary or short e-mail. But in many cases, the answer would clearly be yes, for one key reason among others.

We expect lawyers to write their own court briefs and consultants to write their own reports because we’re looking for something irreducible to an algorithm. The ancient Greek philosopher Aristotle had a name for it: phronesis, or practical wisdom – the ability to decide what to do when a problem doesn’t conform neatly to prior rules or knowledge. A person with practical wisdom draws on a store of experience and judgment to find the most relevant analogy. A language model makes a random prediction.

Most professional writing does more than merely transmit information. It reflects judgment about priorities and trade-offs. This often involves emotional intelligence, reading the room, or seeing the whole picture. When we outsource a document to Ai, we abdicate this role, a role that no Ai can play for us.

The same is true in education. We assign essays to help students learn to write, but also to grapple with ambiguity and complex ideas – to develop judgment, which Ai can simulate but not acquire.

Ai may be rapidly advancing and useful in many fields. But as we become better at spotting what it produces, we’re reminded of what it can’t do and likely never will. 

The question we should be asking is no longer “Will anyone notice?” but “Would it matter – and why?”

I read an article the other day that got me thinking about the day, 20 years ago, that I, along with many others, was laid off from MacLaren McCann due to General Motors’ sales declines, and how I suggested a few of the same alternate employment suggestions to MacLaren McCann’s HR department to no avail. 

“That’s not how we do things around here.” 

True enough . . . just because MacLaren McCann was a large Advertising Agency that prided itself on its ability to come up with “creative communication solutions”, it does not follow that the agency is willing or able to think creatively from an operational standpoint; despite the irony that any ad agency is only as good its people. 

Highlights of the article are below.

Last week, Jack Dorsey stunned markets with a bombshell: Block, the fintech company he co-found-ed, will eliminate 4,000 positions, blaming artificial intelligence. The move removes nearly half the workforce and ranks among the most aggressive Al-related reductions to date. As thousands of employees were informed that computers were taking their jobs, financial markets cheered.

Block's shares surged 25%, adding more than US$6 billion in market value. In his note to employees posted on X, Dorsey framed the decision in starkly binary terms: "I had two op-tions: cut gradually over months or years as this shift plays out, or be honest about where we are and act on it now," he posted. What is striking is the narrowness of the decision set especially since Dorsey acknowledged that Block is "strong gross profit continues to grow." To be fair, Dorsey didn't sound like a typical CEO cost-cutter. His memo was direct and compassionate, and he offered generous severance packages.

Yet, tone and substance are different things. (While) Dorsey's dilemma is real. The troubling question is why a highly profitable company, led by a CEO who genuinely cares about people, treats layoffs - and only layoffs - as the default response to technological progress? 

So, what other options did he have?

One would be to shorten the work week.

• A four-day workweek has been documented as life-changing for employees. Most would probably prefer it — even at 80% — rather than losing their jobs; 
• Even a 30-hour workweek at 75% pay could work.

Another — in the spirit of Google's "20% time" policy — would allow employees to spend one day a week on self-guided side projects. In Google's case it helped create Gmail, Google News and AdSense.

It would probably generate synergies for Block as well.

A third option would be reallocating labour internally — growing the firm, investing in new products and expanding into new markets.

Lastly, to evade shareholder pressure, Dorsey could have taken the company private, or made it employee-owned, making it more about people and less about profits.

Dorsey's "two options" framing exclude this entire menu. This matters because the corporate narrative increasingly treats layoffs as the natural, almost inevitable, response to Al-driven efficiency.

Amir Barnea is an Associate Professor of Finance at HEC, Montreal

and a freelance contributing columnist.