Beyond the Hype: 5 Surprising AI Truths That Will Change How You Think
It’s nearly impossible to escape the constant noise surrounding Artificial Intelligence. The hype cycle is in full swing, filled with promises of utopia and warnings of dystopia. But what are the leaders actually building and thinking on the ground, away from the headlines?
At a recent Google X WonderWoman Tech event, a panel of leaders from Google's Cloud and Policy teams, enterprise AI, and legal scholarship gathered not just to celebrate technology, but to discuss the critical work of building a human-centric AI future. The stakes were made clear by speaker and tech pioneer Arabian Prince, who delivered a memorable warning: if the people who are scared of AI don't get involved in shaping it, humanity risks a future like the one depicted in the movie WALL-E.
This article cuts through the noise to share five of the most impactful and surprising takeaways from that conversation—practical truths that challenge common assumptions about how we should be learning, building, and implementing AI.
Forget Online Courses Real AI Learning Is "Hand-Holding"
The prevailing wisdom for learning a new skill like AI is to sign up for an online course. But according to the experts, this approach is fundamentally broken. The most effective learning comes from "hand-holding" personalized, implementation-focused support and coaching that guides learners as they apply concepts to their own real-world problems.
Emi Wymer of Google:
"Classes are one thing. But what I was told by the teachers and government officials like classes aren't really that helpful. Hand holding. That's really when you see that evolution changes happening on the ground. Online courses, people don't really go to the online courses."
The power of this "hand-holding" approach lies in its alignment with the core principles of Andragogy, or adult learning theory. Unlike children, adults require relevance and immediate application. Data shows that traditional professional development often fails despite high costs because it doesn't meet these needs. Adults learn best when new knowledge connects to their existing experience and helps them solve a problem they are facing right now. Passive courses fail this test, while guided, problem-based implementation succeeds.
To Build AI That Actually Works, Think "Me, We, It"
If the first takeaway is about how we learn to use AI responsibly, the next is about how we build it responsibly. A common reason AI projects get stuck in "pilot purgatory" is that they are built in a silo by a small group whose biases inevitably get amplified by the model. To combat this, Vince Lynch, CEO of ivy.ai, offered a simple but powerful three-part framework for responsible AI development: "Me, We, It."
Me: The individual builder or data scientist. This is the person with the technical skills who brings their own unique perspective and biases to the project.
We: The community of diverse stakeholders and end-users. This group is essential for testing the model, providing feedback on its real-world impact, and catching the builder's blind spots.
It: The AI model itself. This is the crucial distinction the AI must be understood as a non-sentient tool, not a thinking entity.
Lynch stressed the importance of remembering that the AI is an "it" a piece of math without feelings or intentions.
"It's an it, it's not a thing, it's not a person, it's not a brain... It is only the things that it learns from, because it's a massive bit of math."
This simple framework is powerful because it provides a practical checklist for building AI that is safe, effective, and aligned with community needs. It forces developers out of their isolated perspective and ensures that the people impacted by the technology have a voice in its creation. As fellow panelist Liz Rothman noted, the framework "made it really, really simple" compared to other responsible AI frameworks that can be "difficult to digest."
The Global Trust Divide in AI Is Staggering
While the conversation about AI feels global, public trust in the technology is anything but. Felicitas Olszewski of Edelman shared stunning data revealing a stark contrast in public perception between the United States and other parts of the world.
The statistics are eye-opening: in the U.S., a full 49% of people are skeptical of or outright reject AI, while only 17% are in favor. In China, the picture is completely flipped, with 54% in favor and only 10% against.
A primary driver of this skepticism, especially in the workplace, is the feeling that employers are not being honest about how they plan to implement AI and are not providing proper training. Furthermore, many people who are neutral on the topic remain on the fence because they "can't picture a future that is expressing their own identity, their personal needs, their use case." To bridge this gap, the conversation needs to shift from technical specs to human impact.
Felicitas issued a call to action for a more human-centered discussion:
"So a lot of the AI talk is about code, but actually the community inside, you know, how do we design for communities in mind? How do we look for again, that human signal and the cultural impact is something that I would love to see more..."
The "Gunslinging" Error: Why Not Every Problem Needs an LLM
In the current tech climate, there's a tendency to apply the biggest, most powerful tool—the Large Language Model (LLM)—to every single problem. Vince Lynch calls this approach "gunslinging," and it often leads to failed projects, wasted money, and no real progress on solving core business challenges.
He shared a powerful example of a project with the UN to organize people working in sustainability. Instead of defaulting to a resource-intensive LLM, his team trained a small, efficient supervised learning model on a dataset of sustainability reports. The model did the job perfectly, classifying and organizing information without the immense overhead of an LLM.
Lynch's advice was direct and impactful:
"Not every problem requires a large language model. I think it's the easiest answer to that."
The lesson is crucial for any leader navigating the AI landscape. It represents a shift from brute-force computation to elegant problem-solving, reminding us that choosing the right, appropriately-sized tool is a smarter, more sustainable, and more effective strategy than simply reaching for the most hyped technology.
For a Massive AI Win, Fix Your Human Processes First
Perhaps the most memorable story was shared by Dr. Natalie, an audience member who offered a potent case study from her consulting work. A company approached her wanting to "do more AI," but her counter-intuitive response was a firm "No."
She refused to begin until the company addressed its fundamental human processes. As she put it, "I don't want to do another stupid, stupid tech project." Her methodology was human-first:
Guarantee no one would be laid off for a year to build trust.
Map out what employees actually do day-to-day, including what they enjoy and what they dislike.
Eliminate inefficient and "stupid" policies that created friction in the workflow.
Only then apply AI to augment the newly streamlined and human-centered processes.
The result was transformative. A year later, the company was "leap years ahead of their competition." This story offers an optimistic, actionable strategy: when technology serves well-designed human systems, we can "integrate us with the technology for the flourishing of us."
Building a Future We Actually Want
Tying these takeaways together is a single, powerful theme: the future of AI must be centered on human needs, community input, and practical problem-solving. The goal isn't just to build more powerful technology, but to build a future that enhances human well-being and agency.
As Liz Rothman, attorney and USC educator on AI and the law, profoundly stated, this is the ultimate measure of success:
"If we don't build a future where we're going to be happy in that future as humans, then we're going in the wrong direction."
As AI becomes part of your world, what is the one human process you need to fix first to ensure technology truly serves you?
Ryan Edwards, CAMINO5 | Co-Founder
Ryan Edwards is the Co-Founder and Head of Strategy at CAMINO5, a consultancy focused on digital strategy and consumer journey design. With over 25 years of experience across brand, tech, and marketing innovation, he’s led initiatives for Fortune 500s including Oracle, NBCUniversal, Sony, Disney, and Kaiser Permanente.
Ryan’s work spans brand repositioning, AI-integrated workflows, and full-funnel strategy. He helps companies cut through complexity, regain clarity, and build for what’s next.
Connect on LinkedIn: ryanedwards2