A New Ethic for a New Era
We can’t solve the conflicts of AI within the same story and values that created them (4 min read)
Recently, I spent some time in Bangalore, India, giving a talk and a workshop to an audience of leaders, tech makers, educators, and entrepreneurs at DesignUp Conference. I spoke on what it means to be human and conduct business in this era of omnipresent AI, growing economic inequality, and environmental distress. I stepped onstage, looked out at the crowd, and said, “I’m feeling quite tired hearing about AI. Does anyone else feel the same?” Nearly 1,400 hands went up at once.
Sometimes it’s best to pry open fatigue with honesty, because in that opening, the fog clears, curiosity awakens. These days, I’ve been curious about the values that shape what gets built: speed, scale, efficiency, growth, and the stories we’ve built around them. Efficiency for whom? Progress towards what? Scale, at what cost?
Then again, do values ever really make it into the equation when building new technology? ChatGPT—on track to become a trillion-dollar AI tool, condensing the whole damn internet into a chatbot—emerged from a hackathon by volunteers, shipped in ten days. Apple, Microsoft, Amazon, Google, and Meta all came from the world of hacking, in a garage, a dorm room, a research lab. Hacking isn’t concerned with values, use cases, customers, or consequences. Don’t overthink it: just build it, fast, so you have something tangible to test and see what people actually want. This is what makes hacking so toothsome—we’re rarely good at existing in hypotheticals, especially when it comes to AI.
But, more potently, hacking taps into something primal: the carefree ecstasy of creating something from nothing. It can temporarily—yet instantaneously—satiate our curiosity, ego, insecurities, fears, and aspirations. Who wouldn’t want to feel that? And yet, it is in this mindset—or story—of hacking, that consequence becomes irksome; responsibility conveniently shirks.
In a recent episode of Lenny’s Podcast, Head of ChatGPT Nick Turley reflected on ChatGPT’s scale: “With scale comes responsibility. We’re going to hit a billion users soon, and you kind of have to begin acting in a way that is appropriate to that scale.” It’s a familiar script—so familiar that, for a moment, I had to check the title of the episode to confirm this was a podcast about OpenAI and not the Amazon leadership principles. But how long can we keep putting scale and speed first, and responsibility second? Have we waited too long for responsibility to show up, as electricity demands skyrocket, local water resources drain, phishing scams proliferate, art gets stolen, the unemployment rate among college graduates rises, and people fall into chatbot psychosis? What might technology look like—and how might its impact change—if care and responsibility led the way before scale, not after it?
These questions remind me of the philosophies of Audre Lorde: We can’t solve the conflicts of AI within the same story and values that created them. A story that says speed is the only way to build technology, that scale always equals progress. What if we demonstrated the value of care by testing special-purpose experiences at a smaller scale—and for longer—so we actually know what goes into the training data and where it’s used, allowing us to surface harms and unintended consequences before they spread? What if we demonstrated the value of responsibility by measuring the success of new tools not only by traditional business KPIs, but also by its impact on human, economic, and environmental conditions? Might we, then—and only then—gain a true, more complete understanding of technology’s impact, and, in turn, our own impact as leaders and tech makers?
It was a joy, in particular, to explore these questions and values of care, responsibility, and meaning with 1,400 people in Bangalore. The young leaders and tech makers I’ve met around the world are remarkably sharp: they ask piercing questions, seek new values, and, if they aren’t interested, they simply opt out. It meant a lot to see them so engaged with what I had to share.
I also shared six roles, or six storytellers, we can embody—as leaders, tech makers—to bring new stories forward: The catalyst, the bridge, the listener, the curator, the subverter, and the repairer.
The catalyst asks: Why is AI the right solution to this problem? What are we catalyzing? What values are driving this design?
The bridge asks: How might we connect the gaps between people, ideas, and systems? Because technological breakthroughs often lead to breakdowns—within organizations, systems, communications, families, and politics.
The listener asks: What’s changing in people’s lives or values and that our product or service isn’t noticing yet?
The curator asks: Are we helping people find meaning, or just giving them more AI?
The subverter asks: Why do we accept this as normal, or default? What assumptions about speed or scale might be limiting our creativity and care? How might we create distinction in a crowded landscape of chatbots and AI slop?
The repairer asks: What harm, confusion, or bias could AI features cause—and how might we design transparency or recovery directly into the experience? How might we measure the impact on human, economic, and environmental conditions too?
By embodying these six roles, and the questions and methods they carry, we can let a new story and new values take root, hour by hour, meeting by meeting, pixel by pixel. If you’re curious to learn more, or if you’re looking for your next speaker or workshop, reach out.
After the conference, I saw this comment on LinkedIn from an audience member, bringing hope and clarity: “We need many more subverters in this space to keep human empathy’s heart beat going...I would love to be a subverter in this space.”
