by Robyn Bolton | Mar 25, 2026 | AI, Leadership, Strategy
“AI is the new cigarette.”
When a colleague said this in the waning days of 2022, days after ChatGPT burst on the scene, she took my breath away. The idea that this miracle would kill us seemed confined to hysterical handwringing foretelling the birth of Skynet.
She was right.
But neither of us knew it was designed to be that way.
Designed for addiction
My friend predicted that ChatGPT would stay free and helpful until usage reached “critical mass,” and then we’d have to pay. Less than three months after its November launch, OpenAI introduced its $20 per month service.
But it’s not the “first one’s free, the next one will cost you” aspect of drugs that makes AI addictive. It’s the design decisions at its core that keeps you coming back:
- Purchase Decoupling in which you convert real money into tokens, creating psychological distance between you and your actual spending
- Difficulty Curve where skills and benefits accumulate quickly giving you the sense that you’re becoming more capable over time and therefore more committed after progress slows.
- Skill Atrophy where every skill you stop practicing because the machine does it for you, quietly disappears.
Even casual AI users have experienced one or more of these:
- You get a message mid-chat telling you you’ve used all your tokens and need to come back in three hours even though you’ve paid your monthly $20 fee
- You’re prompting in all caps because it’s the only way you can think of to get the LLM to stop hallucinating, while reminiscing about the days when it was a brilliant thought-partner
- You’ve relied on AI to outline articles for the last several months, but you need to write in a different style and have no idea how to get started.
And yet, we keep going back.
But it’s not just individuals who are addicted. It’s entire organizations.
Signs that your organization is addicted to AI
Your CFO asks for the total AI spend across the organization. Three weeks and four departments later, the number is three times what anyone expected because the licenses are buried in IT infrastructure budgets, the pilots are expensed as innovation projects, and half the tools were purchased by business units on corporate cards.
The board approved the AI transformation initiative based on the pilot results. Eighteen months later, the pilot case study slide hasn’t changed, headcount has been reduced in anticipation of productivity gains that haven’t materialized, and the team running the pilot has quietly moved on to other work.
You eliminated the analyst pool two years ago because AI could do in minutes what they did in days. Now you need to evaluate whether the AI’s output is actually correct, and you’ve just realized there’s nobody left in the organization to check it because everyone who’s done it is gone.
Sound familiar? Your organization is an addict.
Recovery is possible
Addiction can’t be cured, only managed. The same is true for AI.
The road to recovery starts in a similar place: Visibility
- Centralize AI spending the way you centralize other business processes AND allow some flexibility by setting strict spending limits and clear decision-making criteria and ownership.
- Start pilots with the end in mind by establishing success metrics and scaling plans at the start of the pilot, not when it’s already in process.
- Treat certain human capabilities as strategic reserves the same way you’d treat any critical operational dependency. Before automating a function, explicitly document what judgment and expertise currently lives there, who holds it, and what it would cost to rebuild it if needed.
Unlike cigarettes or gambling, we’ve reached a point where we can’t quit AI.
But we can be aware of our addiction and we must manage it.
The first step is admitting that it’s real. And by design.
by Robyn Bolton | Mar 24, 2026 | Podcasts
by Robyn Bolton | Mar 18, 2026 | AI, Customer Centricity, Leadership, Leading Through Uncertainty, Strategy
If you’re uncertain, you’re not alone. According to data from FactSet, 87% of Fortune 500 companies cited “uncertainty” during their 2025 Q1 earnings calls. And while things are definitely a tad chaotic in the world, I’ve started asking my clients, “What would you do if you were certain?”
It’s not an academic thought experiment. It’s a very practical exercise that radically shifts the way the think about and lead their businesses.
An Example That Proves the Rule
Most leaders facing disruption do one of two things: freeze and hope that “this too shall pass” or follow and hope that there is safety in numbers.
Neither is a strategy. Both are knee jerk reactions rooted in fear and communicated in the language and buzzwords of business.
This behavior didn’t start with AI. It happens every time a disruptive technology or philosophy bursts onto the scene. The printing press. The industrial revolution. Microchips. Each time, a new leader and paradigm emerges. How do they do it?
They’re certain.
Not because they’re omniscient. But because they know the answers to three questions
Question 1: Who Are You?
When photography made academic realism obsolete, Picasso didn’t freeze. He didn’t pick up a camera. He created something entirely new. Why? Because he knew exactly who he was. “I don’t seek,” he said. “I find.”
Today’s business icons are no different. Richard Branson describes himself as curious and someone who challenges the status quo. Lou Gerstner, when he arrived at a floundering IBM, declared himself a results man, not a visionary.
These self-definitions aren’t marketing. They’re decisions filters that define what you are and aren’t willing to do, agnostic of events, technologies, and capabilities.
Question 2: What Does Your Organization Actually Do?
Not what you make. Not what you sell. What Job to be Done do customers hire you to do?
Nintendo’s answer has been consistent across 130 years of radical product change: help me have fun with friends and family. From playing cards to the Game Boy, Wii, and Switch, their products changed completely. The Job didn’t.
IBM has done the same. From punch card tabulators to consulting and AI, the Job of helping customers make sense of complex information to run better never change. Amex moved from freight forwarding to credit and debit cards, but it’s commitment to move value securely when direct exchange isn’t an option never wavered.
When you know the Job you do, you stop chasing trends and start making choices.
Question 3: How Do You Move Forward?
You can’t answer this question without answering the first two. When you try, you get caught in the same freeze/follow trap as everyone else.
But when you answer the first two questions, the answer to this one becomes clear. For Picasso and Branson, they create. For Gerstner, he optimized the status quo. For most businesses, the answer is “And, not Or.” They must stabilize today’s business, step into (even follow) the next wave, and invest in creating the new.
Satya Nadella’s transformation of Microsoft is a perfect example. He defined himself as a learner, not a knower. He defined Microsoft’s job as helping people make a difference in their roles. From those two answers, every major move followed logically: maintain Office 365, step into cloud, create quantum computing technology.
None of it was reactive. All of it felt certain.
Your Moment Is Now
Yes, the world is uncertain. You don’t have to be.
Before you close this tab and tell yourself you’ll think about it later, answer the first two questions. You can change your answers later, but you need to start now.
The leaders who navigate this moment won’t be the ones who wait and see or follow the crowd. They’ll be the ones who know themselves and their organizations well enough to be certain.
by Robyn Bolton | Mar 13, 2026 | Podcasts
by Robyn Bolton | Mar 11, 2026 | AI, Leading Through Uncertainty, Strategy
Thursday, February 26.
3:35 pm PST – Jack Dorsey said thank you and goodbye to 4,000 people. Block;s profitability was growing, but the promise of “intelligence tools…paired with flatted teams” enabled a fundamental shift in how the company could be run
4:12 pm PST – He posted his farewell announcement to X for the world to read. In it he wrote, “I know doing it this way might feel awkward. I’d rather feel awkward and human than efficient and cold.”
Is there anything more darkly humorous than a CEO trying to avoid appearing efficient and cold when communicating a decision to make the company more efficient and cold?
Only the moment when your boss calls to ask how your plans to grow the business and going and then informs you that the C-Suite wants a plan “to do what Dorsey just did”
Tuesday, March 10.
Time unknown – The agenda of Amazon’s weekly “This Week in Stores Tech” focused solely on investigating why “the availability of the site and related infrastructure has not been good recently.”
More specifically, why, for SIX HOURS, Amazon customers could not access their accounts, view product prices, or complete checkout. That is nearly $300M in lost revenue assuming the outage only affected North America.
All because, after years of cutting headcount and ramping up AI, junior engineers basically vibe-coded production changes..
As best practices and safeguards are yet to be “concretized,” it’s now the responsibility of senior engineers to review all production changes prepared by junior programmers.
How efficient is that AI looking now?
What we lose when we bet on hype, not proof
Researchers at Oxford have documented companies using AI as justification for cuts they had already planned. A January 2026 survey of 1,006 global executives found that 60% have or will make cuts in anticipation of AI’s impact while 29% plan to slow hiring. Only 2% have laid off staff as a result for actual AI-driven results.
Thousands of people are being laid off based on hype, not proof.
It’s reasonable to expect that, one day, AI will live up to the hype and deliver on all the promises promoters are making. But that’s a long-term bet that only pays out if you survive the inevitable crashes in efficiency, revenue, and institutional knowledge.
When organizations swap out people for “intelligence tools,” they lose institutional memory, the subtle, often unspoken, sometimes subconscious knowledge that makes things work. These are the people who understand your clients, your controls, and why past decisions were made. AI can automate workflows. It cannot replicate that knowledge. And once it’s gone, it’s gone.
And the loss continues even amongst the people who remain.
Research from MIT shows that regular AI use reduces activity in brain networks responsible for creativity and analogical thinking by 55%, and the atrophy persists even after people stop using AI tools. You are not trading people for AI. You are trading people for AI while simultaneously reducing your remaining team’s capacity to think creatively, adapt quickly, and catch mistakes. Operations get fragile. Innovation stalls. And when the AI-assisted work fails, as it did at Amazon, there’s no one left to fix it.
The root of growth is never hype
When the call comes down from on high to “do what Dorsey did” it’s hard to counter with cautionary tales like Amazon or reality checks about the state and capability of the organization.
But you can ask questions:
- Are you cutting based on what AI has delivered or what we expect it to?
- How will we ensure essential institutional knowledge isn’t lost?
- If (when) AI-assisted work fails, who fixes it? Amazon’s answers were still on staff. Will ours be, too?
Growth is essential to every organization. But you can’t cut your way to growth.
AI doesn’t change that fact.
It just makes it easier to believe the hype.
