Exploring Marketing Technology

Opinions are all my own

  • This AI bubble is built on nonconsensual magic tricks

    This AI bubble is built on nonconsensual magic tricks

    In her most recent special, stand-up comic Isabel Hagen talked about hating what she calls nonconsensual magic tricks. While a clerk was giving her change, he pulled a quarter out of her ear. She explained, “You gotta ask first, ‘Do ya wanna see a magic trick?’ Otherwise, it’s just deceit.” As someone who was working in technology when I witnessed the bursting of the dot-com bubble in the year 2000, I can speak firsthand about the similarities of that bubble and what I’m seeing today. But so what? It’s a claim every other technologist old enough to remember the original Charlie’s Angels might make. 

    There’s something more unusual that qualifies me to play Cassandra here. An earlier career had trained me in the psychology of deceit. I learned a mental Jiu Jitsu that can also be used to sustain bubbles.

    That first career started when I was 12, the year I was “discovered” while performing magic tricks in the basement of my hometown’s public library. I had been studying magic and building a rudimentary act for a year or so. This was my first show to kids I did not know. It went well, and although I couldn’t articulate it at the time, what most drew me to magic was the psychological principles that make it possible. 

    Here’s a simple example: I learned early that adults were much easier to fool than children. And paradoxically, the more intelligent an adult was, the easier still. I’ll explain why later.

    When I say I was discovered, I’m not kidding. My hometown children’s librarian happened to be a dear friend of a touring, highly respected magician, one who was in town between gigs. He was standing in the back and afterward pulled me aside to ask how serious I was about magic. Dumb question! It was an obsession. That was the beginning of a decade-long apprenticeship, and my first career: Fooling people for fun (theirs and mine) and profit.

    AI itself is no illusion

    Let me be clear: I think AI is miraculous. Especially for marketing technologists like me. Just as the internet and the world-wide-web have proven to be of sustained, world-changing value, AI is the same. The difference? AI is more consequential than the internet by at least one order of magnitude, and probably several. 

    I love AI. But I’m terrified by the warning signs I’m seeing, and feeling twinges of a quarter-century-old PTSD.

    Cory Doctorow’s latest book, The Reverse Centaur’s Guide to Life After AI, is mostly a case for how we’re experiencing a bubble. Also, apropos the title, he explores how AI market forces are driving an unsustainable proliferation of reverse centaur jobs. You can get a definition of a reverse centaur in my prior post, AI for Marketers: Welcome to Darwinian Hyperscaling. 

    Doctorow explains that growth stocks are valued quite differently than mature ones. It’s this difference that has triggered this particular technology bubble.

    Historically, Meta, Amazon and Google (Alphabet) stocks have had much higher values compared to mature stocks like Johnson and Johnson, Walmart, Procter and Gamble. The higher values compared to present earnings reflect the fact that investors are betting that there are still many large and lucrative bites to be taken out of growth stock apples … before nature takes its course and the companies mature, at which point earnings still grow but at a lower, steadier rate.

    Companies, like humans, yearn to appear younger

    Meta, Amazon and Google have really enjoyed being growth stocks. As Doctorow explains, “When a company has a solid growth stock, other people want that stock. The company can use its stock to buy stuff, like other companies. Key personnel can be hired at whopping compensation rates, with the majority of that compensation coming in stock.” The problem is these three stocks and many others are getting a little long in the tooth.

    They’ve recently sought something to help them resume a rapid revenue growth rate. That’s why, for maturing stocks, going all-in on AI was a no-brainer. If their investments in AI work out, everyone wins. Including you and your growing retirement stock portfolio.

    You may have noticed, however, that things aren’t going swimmingly.

    Here’s one example: To quote Aswath Damodaran, of Stern School of Business at NYU and a guest on an episode of Prof G Markets podcast, called Big Tech Has No Idea How AI Pays Off, “When I was looking at the prospectus of SpaceX [the home of xAI and Grok], I noticed that they were making more money by leasing out their data centers to others — in this case, Anthropic — than they were making from AI. I thought that was seriously at odds with their AI story. They were telling, in the prospectus, of this huge market, $28 trillion. [Now they reveal they are] making more money by selling into the AI architecture space. 

    “It’s very revealing. It tells me that you are not as confident as you claim to be … If you were really confident that there was going to be a huge market you wouldn’t want to lease the space out to potential competitors.”

    From compute supply shortages to a demand crisis

    Co-host Scott Galloway added it’s the same story from Meta. “Both Musk and Zuckerberg are trying to spin it as, ‘Look at the premium we’re getting for the infrastructure we’ve built!’ What I see in that is that the AI demand curve has been vastly overestimated. And now you have, essentially, hundreds of billions, if not trillions, of dollars in [capital investments], all going to only two sources of demand creation, OpenAI and Anthropic. It feels like we have all of a sudden pivoted from a supply crisis [because of a shortage of compute from data centers] to potentially a demand crisis.”

    How downright bubblicious.

    The chart below shows a uniquely high level of capital investments (capex). This money is in service of basically just two companies addressing all AI demand, OpenAI and Anthropic … which are, by the way, both hemorrhaging cash.

    Look at the height of that far right bar, showing comparative gross domestic products (GDPs) in various technology transformation spurts. Our country’s current annualized GDP is just 1.5 percent. The takeaway: If there was no investment in AI infrastructure, we would already be in a pretty nasty recession.

    What about the AI job apocalypse?

    It’s worth noting here that Professor Galloway believes, along with Doctorow and others, that the catastrophizing about an “AI job apocalypse” is actually, “fundraising and techno-narcissism.” What, you ask? Fundraising? Yes. It turns out when you wave under the noses of CEOs confident assurances that thousands of their employees can be replaced by AI, few can resist. 

    When those same CEOs discover that what they bought was mostly empty promises, and cancel their contracts, they add to what appears to be that AI demand crisis. We’re already seeing that with businesses slamming the brakes on rewarding employees for their AI token expenditures. 

    I’ve linked to the full YouTube recording of the podcast and I recommend you check it out. Damodaran and the hosts discuss other signals that reality isn’t living up to the AI hype. And when I say AI hype, I mean AI stock value hype. Just as the dot-com surge a quarter century ago was not an illusion, and spawned the very Metas, Amazons and Googles we’ve come to profit from as they’ve fattened our 401(k)s and investment portfolios, AI itself will change the world. But AI values today appear to be mathematically untenable. 

    Two things you can do right now

    1. Your savings — I’m not a financial advisor, and the strategy you take to protect your retirement savings to the greatest extent is something you should discuss with one. Keep in mind that if you are younger, you might just hold tight with your existing savings strategy. Why? It took only seven years for the S&P 500 to return to pre-bubble values.
    2. Your career — Knowledge is power. If you want to learn more about the new growth of reverse centaur jobs, this companion to Doctorow’s book, also published this year, is by award-winning Financial Times journalist Sarah O’Connor, We Are Not Machines: The Fight for the Future of Work. I enthusiastically recommend them both.

    Nothing up my sleeve! Four tricksters’ secrets revealed

    So how do you, if you’re in the business of growing already overvalued stock, keep alive the illusion of those future riches you promised? The key word is illusion. Below I list methods I first learned when my voice was still changing. 

    Tactic #1: The Byzantine premium

    In my childhood studies I learned that around the turn of the last century, stage magicians would roll out complicated and exotically decorated props from “The Orient.” To Western audiences of the time, things from Asia were considered mysterious and prone to magical properties. One notable American magician, William Ellsworth Robinson, went so far as to perform in elaborate make-up, under the stage name of Chung Ling Soo. A portion of a poster promoting his show adorns this blog post.

    He guarded this ruse of being Asian as tightly as if his career depended on it. It probably did. Appearing inscrutable made his tricks more convincing.

    Fast-forward to today. Cory Doctorow explains, “Tech bubbles are surprisingly easy to generate, thanks to something economists call ‘the Byzantine premium.’ That’s the extra value that investors place on an asset that they don’t understand.” That is as true today as it was in the year 2000.

    He goes on, “They assume that any pile of shit of sufficient size must have a pony under it somewhere.”

    Tactic #2: Pleasant surprises deliver dopamine hits

    The performances of comic Isabel Hagen and those of magicians have something in common: A series of delightful surprises. What we’re feeling when we experience them is literally visceral. It’s the release of dopamine, the pleasure hormone. 

    Now picture yourself in a boardroom, seeing a live demonstration of AI that a company wants to sell to your enterprise. Super impressive! Then imagine each of these same board members going home and playing with a chatbot. 

    As I described in this post on my personal blog, Reading and Writing in the Age of AI, “The intelligence we confer to the text coming out of chatbots is an illusion. … Like a magic show. At a Penn and Teller performance, the magic doesn’t occur onstage. The illusion takes place in our minds, as the duo crafts situations where our assumptions and biases fill in perceptual blind spots, and we consequently ‘see’ the impossible.” 

    The response from the board members, who have had their beachheads sufficiently softened by these repeated dopamine hits? “Get out our checkbook!”

    Tactic #3: Cognitive biases can influence us and we don’t even realize it

    I’ve spent the most recent part of my marketing technology career overseeing testing and personalization systems, specifically for selling to consumers. As I mention in this five-minute YouTube excerpt of a talk I gave in 2018, to an Adobe Summit audience in Las Vegas, hidden but measurable psychological quirks, such as confirmation bias and loss aversion, are the fuel that powers those personalization tools. (And, incidentally, a fuel for magic tricks.)

    Yet I warn my audience that the people operating personalization systems can be just as susceptible to biases as the consumers they sell to. They can fool themselves and not even realize it, ruining the results of their controlled randomized tests.

    In a similar way, I am not saying that those in the AI Value Hype Industrial Complex are conscious of their biases toward AI as an Enterprise Money Printing Press. They may be true believers. What’s more, as Upton Sinclair famously wrote, “It is difficult to get a man to understand something when his salary depends upon his not understanding it.”

    Tactic #4: Build hidden compartments

    This isn’t so much a subtle psychological tactic as naked deception. But you cannot argue with success. There is nothing in stage magic more tried-and-true than hidden compartments. We’ve all seen exposés revealing that the assistant was hidden in that fancy box the whole time. 

    Now imagine the box is a corporate balance sheet, and the hidden assistant is a monster. 

    Major hyperscalers are building financial artifices, to the alarm of respected authorities like Ed Zitron. It’s true that many started by relying purely on debt they disclosed on their balance sheets. Yet today, as costs skyrocket, much of hyperscaler capital is externally financed, hiding considerable financial risk. A popular way is something called a special purpose vehicle (SPV).

    To quote Zitron: “The problem with these SPV-based deals is that they allow companies to, at least on a balance sheet, hide the scale of their debts. … This is all legal, worrying, and yes, a little bit Enron.” 

    Similar to how collateralized debt obligations (CDOs), which caused the Great Recession of 2008, were eventually regulated, someday so will SPVs. Someday. But like CDOs, I’m convinced that regulation won’t happen until the damage to our world economy is done.

    If you want to vanish an elephant, do it in front of a Mensa chapter

    I promised to explain why highly intelligent adults are easier to fool than those with average IQs, and why both are easier to fool than children. 

    Younger brains are still being wired. That means they see the world much less predisposed to assumptions than you or me. On the other end of the spectrum, the most intelligent adults got that way by considering certain observations as settled law. They’re called heuristics: That glass salt shaker is full of salt, not painted to look that way. That single playing card is just that, not carrying another card flush against it.

    Some of the greatest intellects in the world have convinced themselves that the law of gravity behind meteoric stock gains has been rescinded this time, ignoring history. It’s a truism that goes all the way back to the Dutch tulip bubble, in the 1630s. 

    They all fell victim to nonconsensual magic tricks.

    Cover image via Creative Commons

  • How did this streaming experience get made?

    How did this streaming experience get made?

    Early in the 18th century, an English poet wrote advice on how to tactfully correct someone. It still holds up today.

    Men should be taught
    As if you taught them not
    With things unknown
    Proposed as things forgot

    — Alexander Pope

    I was reminded of this wisdom when I was renting a film on YouTube. It was brilliantly optimized to suggest something, as if I had forgotten, to start the rental period. 

    Don’t judge me harshly on my film choice, shown on the image below. This terrible action comedy from 2020 was an “assignment” as a loyal listener to the long-running podcast How Did This Get Made?, with improvisational actors Paul Scheer, Jason Mantzoukas and June Diane Raphael, most recently of Elle fame (she plays the mom). They roast bad movies.

    I imagine that there were many A/B/n tests behind this final user experience, with the goal for optimization being what has to be the most costly part of renting steaming films: How do we reduce the number of complaints from users who don’t remember agreeing to our rental duration? I say there was much testing involved, because the solution is so elegant!

    Netflix Sign-up Buttons

    Another test from the world of streaming media is one I’ve included in an A/B testing and personalization quiz. The quiz helps me illustrate to my audiences why the cognitive biases we all possess necessitate scientific testing.

    We cannot guess our way to the best user experiences!

    It’s a cellphone-based quiz where folks are shown two or three options and they have to vote in real time which is the winner, based on randomized controlled experiments in platforms like Adobe’s Customer Journey Optimizer.

    My quiz content is from the excellent GoodUI.org (subscription required). 

    I call the quiz How Good Is Your Gut? Of the hundreds who have participated in the quiz, no one has guessed correctly more than 50% the time, which is a great a way to demonstration how important testing is to optimize outcomes.

    Feel free to take your best shot in the embedded quiz, from these three button options tested:

    When you take the quiz, you’ll be shown the cumulative results from all of the times I’ve conducted it.

    Netflix Test

    Netflix ran a test on their sign-up button. Take a guess — which button size improved clicks and sign-ups?

    Here’s how everyone answered:

    • Small Button
    • Medium Button Correct Answer
    • Large Button

    The next time you’re renting media and are impressed with the friction free experience (or not … I’m looking at you, Hulu!), remember there was a ton of behavioral science behind what you’re experiencing!

  • AI boosts ecommerce conversions by compressing evolution

    AI boosts ecommerce conversions by compressing evolution

    Every sale on your ecommerce site travels through four phases: Attention, Interest, Desire and Action (AIDA). Before AI, optimizing each meant months of randomized controlled tests, slow feedback loops, and lots of guesswork. AI compresses that personalization cycle dramatically. Marketers using it have a distinct competitive advantage

    To explain how, I’m pulling from two creatures found in nature. They evolved over millennia to do exactly what your ecommerce experience needs. One of them might be the strangest and most instructive thing you’ve never heard of.

    I described those two creatures recently on another blog site, Dyslexic Data. The title: Using data to visualize evolutionary forces. There I explained how the pair have evolved to catch and eat the optimal number of bugs, in a feat of natural selection that evolutionary biologists have visualized.

    Although you should not expect either showing up in an upcoming Pixar feature, in my mind’s eye I do see them, above, fitting in nicely outside The Haunted Mansion. (I describe the significance of this Disneyland attraction in another post, also on Dyslexic Data). 

    First, the spider: A creature so reviled it even has a phobia named after it. I asked AI to make it look friendly. Still, all those legs! It’s hard to avoid the creep factor.

    The other isn’t shown, because, 1.) It is genuinely hideous, and 2.) It spends its phase of life hiding in loose sand, at the bottom of funnel-shaped depressions like the three shown in the foreground. 

    This shy, larval stage of the lacewing, is called the ant lion.

    Growing qualified visits by optimizing attention and interest

    AI is rewriting the economics of all four phases of AIDA. The first two, before modern LLMs, were addressed by the human optimization of paid and organic search, as well as digital display ads. Algorithms helped, but decisions based on feedback loops were slow and data was thin.

    You can think of this process, pre-AI, as similar to the silk spun by the very first ancestors of modern spiders. They weren’t very efficient. 

    The computer simulator described in my evolution blog post makes a case that natural selection and food scarcity inevitably drive generations of spiders to spin webs optimized for the lowest calorie expenditure yielding the greatest caloric intake. If you ran that NetSpinner simulation a thousand times, over multiple generations, each pass would iteratively, gradually evolve to what you see above: the classic Charlotte’s Web bug-catcher. 

    Every single time. Now that’s optimization!

    Similarly, modern machine learning works to optimize your campaigns for better attraction of attention and generation of interest. Here are just three of the mechanisms:

    1. It does a better job of stitching identities in your customer data platform (CDP)
    2. It refines your organic and paid campaigns by providing more actionable data
    3. It continually interprets that data to launch and test multiple ad variants

    Those actions include look-alike modeling, to find more prospects similar to your best customers, and suppression models, to ensure you aren’t “wasting silk” — in this case, your ad dollars — on people who are already customers.

    In these ways and the others below, AI compresses evolution by quickly learning the best placement of its own “sticky silk strands” to capture the attention and interest of ideal prospects.

    From search rankings to AI recommendations: the new attention engine

    Organic search marketing, also known as search engine optimization (SEO), is still important. 

    AI helps there too. 

    It can optimize your product description pages to rank higher in search engines for the phrases your customers use to find the products.

    Rapidly emerging as a counterpart to SEO is agentic engine optimization (AEO … sometimes called GEO for “generative engine optimization”). AEO / GEO acknowledges that attention and interest take place in ways we marketers find difficult to quantify, in recommendations made by ChatGPT and other chatbots.

    Agents such as Optimizely’s Opal can build the data schemas within your product description pages that are invisible to human visitors but instructive to bots.

    Those schemas serve as training datasets for the LLMs. They help persuade chatbots to recommend your products over competitors when consulted by your prospects.

    Measuring Success

    Success metrics in a world of AEO / GEO can be tricky. 

    In the old world, interest was measured by the raw number of people who arrived at your site. Since the emergence of Google Gemini, ChatGPT and their ilk, marketers are noticing significantly fewer visitors. That’s because consideration for many is happening on search engine answer sections and the recommendations served up by the chatbots. 

    Here are two metrics that still work:

    1. An increased return on ad spent (ROAS)
    2. A reduced number of clicks leading to conversion, since people are generally arriving more knowledgeable about your offerings, with fewer questions

    Boosting desire and action through optimized conversion funnels

    I’ve known about ant lions since I was a morbid little boy. As a digital marketer, I recognized they’ve evolved to produce a literal, perfect conversion funnel.

    The ant lion hangs out at the bottom. When it senses an ant has stumbled onto the rim, it flings its shovel-shaped head to toss grains of sand up and over.

    The unwitting ant works against the avalanche as it begins a downward slide. The ant lion counters with more targeted shoveling. In stages, similar to the steps of conversion funnels that are your site’s shopping experiences, any slip-up would mean escape, and a ruined dinner.

    The Action at the end of the Desire phase is, of course, a sale. Improving your conversion funnels to optimize sales, before AI, was slow and imperfect. 

    True, funnel-optimizing A/B and multivariate testing succeeded in the pre-AI world, by using complex math to measure improvements. But today, AI makes these calculations and decisions in an instant, based on more variables than you could ever hold in your mind (the segment of that prospect, the visit source, day and time, and purchase history, to name just a few attributes).

    AI ensures that every method to reduce conversion funnel attrition is put to practice in real time. The result? A greater share of your prospects pass from Desire to Action.

    Measuring Success

    What metrics would tell you if your conversion funnels are performing like a well-dug ant lion trap? Here are two:

    1. Conversion rate, as measured from sales divided by entries into the relevant product description page.
    2. Shopping basket size, since AI can also supercharge your “You may also like” recommendations

    Better webs, smarter funnels

    AI doesn’t change what a sale is. It compresses the time it takes to get there … to serve up the right message and experience for the right person at every stage of the process. 

    Chances are some of your competitors are already using AI to spin better webs and dig more efficient funnels. The question isn’t whether to adopt AI. It’s which phase of your AIDA cycle you optimize first. 

    Start there.

  • AI for Marketers: Welcome to Darwinian Hyperscaling

    AI for Marketers: Welcome to Darwinian Hyperscaling

    If AI has made you question your value as a marketer, you’re not imagining things. The tools are getting better fast, and some tasks are disappearing. But here is the good news: marketers who combine AI fluency with human judgment are becoming more valuable, not less.

    I call this Darwinian Hyperscaling: adapting your skills faster than the pace of changes in our marketing environment.

    In practice, that means three moves. Build decisions on strong mental models. Strengthen the social skills that machines cannot replace. Train your attention so you can think clearly when everyone else is reacting.

    Do those three things, and AI becomes your multiplier, not your replacement.

    Gradually, and then suddenly

    Mike Campbell, a character in Earnest Hemingway’s novel The Sun Also Rises, said he went bankrupt two ways: “Gradually, and then suddenly.” That quote aptly describes how we got to our current scary employment climate.

    Enter: Darwinian Hyperscaling. Just as the forces of environmental changes can accelerate evolutionary adaptation, the forces changing how we deliver business value will only reward those who are ready.

    The unifying strategy I recommend is to aggressively morph into a Centaur — pictured above from Greek mythology. In this book from Cory Doctorow, he describes the Centaur and Reverse Centaur, as follows:

    “In automation theory, a “centaur” is a person who is assisted by a machine. Driving a car makes you a centaur, and so does using autocomplete.

    “A reverse centaur is a machine head on a human body, a person who is serving as a squishy meat appendage for an uncaring machine.”

    When creating and executing marketing strategies, show your employer’s AI models who’s the boss.

    Apply these three lessons to firmly graft your torso onto this powerful business intelligence, instead waking up and finding yourself resembling the rear end of a two-person horse costume!

    Lesson 1: AI models are terrible at knitting mental model lattices. Exploit this and prosper

    Before his death in 2023, Charlie Munger wrote and spoke often about collecting mental models. He freely shared how he used them with his business partner Warren Buffett.

    Mental models are logical frameworks for solving tough problems.

    Without models from multiple disciplines, you will fail in business and in life.

    — Charlie Munger

    Munger famously said, “The first rule is that you can’t really know anything if you just remember isolated facts and try and bang ’em back. If the facts don’t hang together on a latticework of theory, you don’t have them in a usable form … You’ve got to hang experience on a latticework of models in your head.”

    Case Study: The “Follow The Incentives” Mental Model

    One of Munger’s most repeated quotes also happens to be a powerful mental model. It’s one that I applied to solve a tough problem last year with the help of AI — to great success. He said, “Show me the incentive and I will show you the outcome.”

    This case study and the lesson it teaches follows a standard Problem – Complication – Solution format:

    Problem: I was asked to identify what challenges were keeping marketers — those in a specific business category — up at night. But I knew nothing about that sector, especially since it contained more than a dozen sub-sectors, each with their own unique marketing pain points. Additionally, my assignment was to match my agency’s offerings to whichever pain points were relevant for that sub-sector. This would take months of research … Before AI!

    Complication: A simple prompt, such as an expansion of “List sub-sector X’s marketing challenges and how we could solve them,” is practically an invitation to hallucinate. How do you trust the results?

    Solution: Crowdsource, in an anonymous way, the people with the most to lose if they do not intimately understand their audience’s marketing challenges. In other words, follow the incentives and learn from their findings. 

    As I was thinking about who has skin in the game in identifying pain points, I realized the following:

    • Every sub-sector has at least two professional conferences catering to its unique marketing needs. Many sub-sectors have four or more conferences. That’s a competitive environment — especially when budgets for attending conferences are shrinking.
    • If one of them programmed their conference break-out sessions in a way that attendees found unhelpful, they would go out of business. (Again, Darwinism at work!) Now that’s an incentive to get your programming right!

    I created an AI Agent that would poll each conference’s website, and capture its break-out session titles and descriptions. The Agent could then easily match the relevant ones to my employer’s offerings.

    The carefully researched break-out sessions were proxies for the marketing pain points I was seeking! 

    It worked beautifully.

    I was even able to confirm, talking to colleagues who knew sample industries intimately, that the pain points indeed rang true.

    … All this research and matching, accomplished within an hour or less, for each major business category.

    The takeaway: Only automate where an AI model cannot fail you. The workflow behind the Agent should be based on one or more mental models you employ to the task. Be the Centaur.

    (By the way, don’t ask me to share the actual output. That is the property of my employer at the time. But the methodology? That’s as open source as Munger’s freely-shared wisdom.)

    Action: Make a study of mental models

    You cannot go wrong by starting with The Great Mental Models.

    Six years ago when Farnam Street Publishing announced it was producing this volume, I eagerly pre-ordered it. It did not disappoint. 

    Lesson 2: Improve your social skills

    In 2017, a working paper called The Growing Importance of Social Skills In The Labor Market, concluded that those workplace skills are far more important in this century than in the 1980s and ‘90s. That finding may seem counter-intuitive to you, especially if you are old enough to have worked in teams at that time, as I have. Its findings, controlling for education, demographics, and region, include the following:

    • Workers with higher social skills enable their teams to specialize more efficiently, generating larger productivity gains
    • Social and cognitive skills (being productive across many workplace tasks) are complements: The wage premium for social skills is higher for workers who are also cognitively skilled

    If you wonder how the growth of AI since the paper’s publication has changed the dynamics, I have news for you. Yes, AI has made your ability to do more workplace tasks (what the paper calls cognitive skills), but these new cognitive skills must be paired with the cohesion you encourage within your teams by demonstrating excellent social skills. 

    Last year in my personal blog I posted about how AI has reshuffled the recipe for career success, citing Professor Scott Galloway. He listed his 3 human skills that make you irreplaceable in an AI world. The third of those skills?

    Connection.

    I’ll quote heavily from Professor Galloway below, since he provides plenty of specifics [all emphasis is his]:

    “AI can summarize, analyze, and even write with fluency. What it can’t do is care. It doesn’t build trust, show emotional investment, or make someone say “I want that person in the room.”

    “That’s why, in an age optimized for competence, connection is the real premium.

    “Connectivity means showing up with warmth, curiosity, and follow-through. It’s being the person who brings the group together, who makes others feel smarter when they work with you, or more confident because you’re on the project. When others see that your heart’s in it, they trust you’ll go the extra mile, pay attention to the crucial details, and take personal responsibility. That trust is hard to earn and impossible to automate.”

    Action: Consider finding a coach 

    If the person described above does not align with what people think of you, find out how you can move closer to that professional leadership style.

    For inspiration, I strongly recommend Brené Brown’s latest book, Strong Ground: The Lessons of Daring Leadership, the Tenacity of Paradox, and the Wisdom of the Human Spirit. This was my introduction to Ms. Brown and I’m now a superfan!

    Lesson #3: Practice mindfulness meditation

    I’ll bet you weren’t expecting that one! 

    I may be biased, because I started meditating in my 20s, and when I lived in Milwaukee I was an active member (and OG webmaster!) of the Mindfulness Center of Milwaukee. But it turns out mindfulness is the only reliable antidote to AI-induced cognitive decline, a malady described by two Wharton scholars: Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender.

    To be clear, their paper described the problem, not the solution. It was cognitive neuroscientist Dr. Sahar Yousef who posits mindfulness as the remedy. At last month’s SXSW conference, she and Section CEO Greg Shove shared research on AI’s cognitive effects on university students.

    I was surprised / not surprised to learn from them that “Mindfulness is the only cognitive protector. Of all the traits measured, only one showed a protective effect against both cognitive dependence and AI companionship reliance: the ability to be fully engaged in the present.”

    Other academics would agree. In Yuval Noah Harari’s 21 Lessons for the 21st Century, each of the first twenty chapters describes a different challenge facing us. 

    I won’t lie. Reading it was a rough ride. I recognized each of the many challenges he described in detail, either facing us today or swiftly approaching. 

    Reading each progressive chapter, I felt like Hemingway’s Mike Campbell, realizing during a meeting with his accountant that he would soon be very, very insolvent. I wondered: What solace could Harari leave me with? How was he going to help me handle the onslaught, including that of machines thinking for us, making us gradually unfit to reason for ourselves? 

    The title of his final, twenty-first, chapter: 

    Meditation.

    Action: Learn more about mindfulness

    I started my own pursuit of meditation with Jon Kabat-Zinn’s Full Catastrophe Living. It still holds up!

    He is professor emeritus of medicine at the University of Massachusetts Medical School, where he was founding executive director of the Center for Mindfulness in Medicine, Health Care, and Society.

    I read the book shortly after it came out, to cope with chronic pain and the depression it caused. He taught me that meditation, removed from Eastern dogma, could do truly miraculous things to our minds. 

    That includes thinking clearly and strategically in stressful business situations. 

    The introduction of the book explains the meaning behind its title (hint: it’s from a classic musical). I think you’ll agree, we need all the skills we can find to face and overcome today’s occupational and societal “catastrophes.”

  • The Intelligent Enterprise: A Book Review

    The Intelligent Enterprise: A Book Review

    I’ve been blogging here for nearly 20 years (don’t go digging — I’ve learned a lot about writing and MarTech since my tech consulting infancy), yet I’ve never before posted a book review. And with this precedent comes a major disclaimer: Vincent Yates and Jason Goth, the authors, are leaders within my employer, the global consultancy Credera. So this could smell of log rolling, or worse. But I’ve always posted what I sincerely thought at the time. Conversely, I’m not stupid. If they had conceived a truly ugly baby I may be whispering that opinion to my colleagues, but certainly not posting it here. What follows are the objective reasons you should buy and read this book. It’s both excellent and timely.

    Enterprise leaders are hungry for information on how they can adopt artificial intelligence (AI). They are also, if they’ve earned their positions in the C-suite, allergic to glowing citations of easy success. I’ve read lesser business books that suffer from something called survivorship bias — the tendency to elevate success stories while diminishing the more frequent failures. David Ogilvy put it well when he talked about the need for perseverance, but especially also caution, when running a business. He was reported to have said, “The road to success is dotted with many tempting parking spaces—and strewn with the bodies of pioneers.”

    The authors address this head-on, by describing attempts at investing in AI that failed to take into account all the elements needed for success. One example: I was thrilled to see a section about the need to incorporate design thinking. Nothing kills a new technology more surely than workers who are overlooked in its design and roll-out, who then quietly circumvent or sabotage that investment.

    I also found it refreshing that they mentioned Gartner’s hype cycle, where inflated expectations by an enterprise inevitably lead to a trough of disillusionment. 

    Practical Advice for Avoiding Regret

    I should mention, since I focus on MarTech on this blog site, that the most successful implementations of AI aren’t related to marketing at all, unless you count customer service in that category. In my own experience, and within the pages of the book, it’s primarily the many back office processes that AI can profitably automate. These aren’t necessarily the most sexy applications. But they tend to make the greatest impact on an enterprise’s bottom line. 

    Speaking of the bottom line, I loved the list of the many often unanticipated costs of owning and operating an AI solution. Headlines everywhere talk about the easy path to positive ROI (see above: survivorship bias), but data hygiene, ongoing model improvements and smart governance — to name just three — come with ongoing costs that need to be considered up front. 

    AI is not set-and-forget.

    Speaking of governance, I loved seeing the section on AI safety. The book talks about how AI is basically “a brain in a jar” (ick!), but with this power comes risks of unintended consequences. Guardrails are essential. I recently wrote in my personal blog how important this is overall, especially at a nation state level.

    I need to also call out the clarity of the writing. This book excels at bringing a blindingly complicated technology within the grasp of the enterprise leader. It was an actual pleasure to read.

    One Small Quibble

    It’s a truly small knock on the book, but the quote that begins it, one in praise of learning from the wisdom of others via books — ostensibly by Socrates — is most certainly a fabrication, like the many howlers attributed to Albert Einstein or Ghandi. A quick search for the origin of the quote using Copilot (thank you, AI) indicated there is zero evidence of this sentiment in the writings of Plato, Xenophon, or other contemporaries of his. What’s more, although he certainly valued learning from others, he preferred the Socratic method (speaking of log rolling!). 

    In fact, Plato wrote that Socrates thought writing things down instead of memorizing them led to a lazy or weakened mind, particularly in terms of memory and genuine understanding. As a relatively new technology, he was suspicious of writing.

    “Employ your time in improving yourself by other men’s writings, so that you shall come easily by what others have labored hard for.” — Almost certainly not Socrates

    Like I said, a small quibble. As a leader, you will “improve yourself greatly” by reading it and taking heed of its advice. Unlike that fallacious quote, The Intelligent Enterprise is the opposite of AI slop.