Interview
If there’s one thing Catherine Brinkman made clear this year, it’s that AI’s future won’t be defined by model size or benchmark wins. It will be defined by whether the tech world can earn the trust of the most sustainability-driven generation in history.
"Gen Z is not just a passive audience; they are active change-makers who influence corporate policies... Companies that fail to adapt to their sustainability expectations risk alienating a generation that prioritizes eco-conscious decision-making."
This is just one of the insights she's brought to the table, challenging companies to rethink how they build, power and deploy AI. Below, we break down Brinkman’s other top takeaways of the year.
1. Sustainable AI Will Put You in Gen Z's Good Graces
Gen Z’s relationship with AI is the tension every tech leader needs to pay attention to, Catherine Brinkman argues.
This generation may be the fastest adopters of AI tools, but they’re also “deeply concerned about AI’s energy consumption, carbon emissions and electronic waste.” Companies that fail to address AI’s footprint — from data center emissions to the surge of electronic waste — risk losing credibility with the most sustainability-driven consumer base on record.
Brinkman points out that transparency is quickly becoming a competitive differentiator. Gen Z wants to know “the carbon footprint of the AI tools they use and whether companies are actively mitigating their impact,” pushing firms to disclose everything from energy sources to lifecycle emissions.
And it’s not just about reporting — it’s about action. As she notes, AI companies that “align with Gen Z’s environmental priorities will have a competitive advantage,” whether through energy-efficient models, renewable-powered data centers or hardware designed to reduce waste. In Brinkman’s view, sustainability is no longer a corporate virtue signal, it’s a business strategy.
Related Article: AI in Hiring: What Gen Z Wants From Employers Today
2. Use AI to Ditch the Hype Cycle
"If you’re trying to keep up with every new development, you’re setting yourself up for burnout." It's Brinkman's reality check for anyone feeling swamped by the AI hype.
Instead, she says, leaders need to ditch the panic and get selective. “AI is a tool, not a tidal wave you need to drown in." That means ignoring the hype cycle and zeroing in on the updates that actually move the needle for your work.
How do you do that? Brinkman recommends using AI to tame the chaos it creates. Tools that consolidate research and “surface only what’s important” help cut through the noise so professionals can think clearly. Ultimately, staying ahead in AI isn’t about volume, it’s about boundaries, focus and playing the long game.
3. AI Tools Can Aid the ADHD Brain
AI can be a lifeline for ADHD brains. Not a cure, but “scaffolding” that finally adapts to how people with ADHD actually think.
As Brinkman puts it, “executive function is the driver’s seat of the brain, and for ADHD, it often feels like there is no one behind the wheel.” AI changes that by breaking overwhelming projects into “micro-steps,” fixing time blindness with “attention receipts” and acting as “external RAM” when working memory collapses. The result: paralysis becomes momentum, and messy to-do lists become navigable systems instead of shame spirals.
She also notes that the science is catching up with lived experience. Studies show AI-enhanced tools help with “cutting procrastination and improving quality of life,” while wearables now track focus and sleep with up to 93% accuracy.
AI isn’t replacing executive function, it’s supporting it. And for millions of people who’ve spent their lives fighting their own brain wiring, that shift is nothing short of amazing.
4. Gen Z Won't Tolerate ESG Lip Service
Gen Z is making a big splash in the corporate landscape, and one thing they won’t tolerate is ESG lip service.
Companies can no longer hide behind vague pledges. AI is exposing who’s actually making progress and who’s stalling. With tools that optimize energy use, analyze climate risk at scale and strengthen ethical sourcing, Brinkman argues AI is “turning sustainability goals into measurable action” and holding brands accountable in real time.
AI is transforming the social and governance pillars Gen Z cares about most. From bias-free hiring to detecting workplace harassment, discrimination or burnout, Brinkman says AI is enabling the transparency younger workers demand.
And as Gen Z moves billions into ESG investments, AI-powered scoring, greenwashing detection and predictive analytics are changing how responsible companies get funded. Businesses must evolve with AI-powered ESG strategies or, as Brinkman warns, risk irrelevance in a generation that expects proof, not promises.
Related Article: Hyper-Personalized Ads: Marketing’s Biggest Win (and Risk)
5. The Future Is Highly Personalized — and an Ethical Grey Area
We’ve officially entered a new CX era, one where “every single interaction you have with a brand feels like it was designed specifically for you.” Hyper-personalization is no longer marketing fluff. It’s AI-driven prediction at scale, powered by algorithms that “anticipate your needs and preferences in real time.”
Brinkman notes that this shift is primarily about revenue. Fast-growing brands are already pulling in up to 40% more revenue simply by doubling down on personalization strategies that feel “insanely individualized” to each customer.
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Her reporting also breaks down how this shift is unfolding across industries. In ecommerce, recommendation engines are getting so precise they “feel like they’re inside your head,” while customer service bots now handle 70% of issues without a human — but still feel personal and real. In entertainment, AI crafts scripts, music and gameplay tailored to a user’s “personal vibe,” and in healthcare, diagnostics powered by machine learning detect disease with “better accuracy than human radiologists.”
But Brinkman warns that the future raises big ethical questions. As AI gets better at interpreting context, behavior and emotion, she asks whether we’re ready for how much data we’ll need to give up, and what it means when machines know what we want before we do.
