Walking through San Francisco, the conversation is unavoidable. It’s etched onto bus shelter ads for new developer tools and murmured between sips of coffee in South Park. Artificial intelligence is no longer a speculative future; it’s the material of our present, woven into daily commutes, work emails, and playlist recommendations. This pervasive presence breeds a unique cultural moment: one of simultaneous familiarity and deep-seated unease.
People feel this duality acutely. “It’s still a very new technology, still a whole lot of unknowns,” notes Thomas Williams, echoing a common sentiment I hear at tech meetups. For every enthusiast like Ethan Ellsworth, who sees AI making processes “more efficient,” there’s someone like Arlene Kilkenny, who confesses, “I’m afraid it’s going to take away people’s jobs.” This spectrum of reactions—from cautious optimism to genuine fear—isn’t just public noise. It’s a critical signal about how a powerful, diffuse technology is being metabolized by the society it aims to transform.
To untangle this, I sat down with two University of San Francisco professors who view this landscape from distinct but complementary vantages. Michele Neitz, Founding Director for the Center for Law, Tech, and Social Good, and Chris Brooks, a Computer Science Professor with over three decades in AI, helped frame the public’s valid, if sometimes misdirected, anxieties.
“That is a really great assortment of the way in which people are thinking about AI right now,” Neitz observed of the public quotes. “There’s some truth, perhaps, to a lot of it.” Brooks immediately pinpointed the core tension. “A lot of the fears, which I think are very real, are about the confluence of AI, technology, and capitalism,” he told me. The technology itself is often just the vessel; the true apprehension lies in how its power will be directed and who will control its benefits.
Defining the beast is the first challenge. While agencies like NASA offer a clean, functional definition—“computer systems that can perform complex tasks normally done by human reasoning”—the reality is fuzzier. “I would define AI as the umbrella term for many different types of advances in technological development,” Neitz clarified. “Machine Learning, Deep Learning, Neural Networks – these are all fields of development within that larger umbrella.” Brooks added a crucial historical perspective, noting that the definition is a moving target. “It seems like every time we solve something, the target moves.”
This idea of a shifting frontier is key. The foundational concepts aren’t new. Brooks reminded me that the field’s seminal workshop was in 1956 at Dartmouth, and milestones like IBM’s Deep Blue defeating chess champion Garry Kasparov in 1997 were early, narrow proofs of concept. “For a long time, we were at this plateau,” he said. The explosion we’re witnessing now, he argues, is the result of a perfect storm: radical improvements in engineering, more powerful hardware, access to vast datasets, and unprecedented capital investment. “I’ve been working in this area for 30+ years. It’s really exciting to see it kind of finally break through. Stuff that I used to think was impossible is now a product.”
That breakthrough means AI has shifted from lab curiosity to consumer constant. It’s the large language model helping draft an email, the algorithm curating a news feed, the advanced driver-assistance system in a car. This democratization of interaction is what defines the current era. But with ubiquity comes responsibility, a point both professors emphasized. For Brooks, the real excitement isn’t in the capability itself, but in its application. “Being able to deploy these powerful technologies to solve real human problems is exciting to me.”
His hope for the field is deceptively simple yet profoundly challenging. “Use this to solve problems that really matter for our world today,” he said. “That sounds really obvious, but it’s super easy to forget and to instead focus on maybe efficiency, profits, or focus on making some business process faster at the expense of human comfort and not addressing human suffering.” This is the crux of the public’s apprehension. The fear isn’t of intelligent machines in the abstract; it’s that their intelligence will be harnessed primarily for extraction and optimization, rather than for elevation and equity.
Navigating this requires more than just technical literacy. It demands a robust conversation about values, governance, and intentionality. The “whole lot of unknowns” that people like Thomas Williams worry about aren’t just technical unknowns—they are societal ones. As this technology continues its exponential curve, the most critical development won’t be a new model architecture, but the frameworks we build to ensure its power aligns with a broader human benefit. The billboards might sell potential, but the real work happens in the harder, quieter spaces of law, ethics, and deliberate design.
- Artificial intelligence is woven into daily commutes
- Common sentiments range from optimism to fear
- Definition of AI is complex and evolving
- Historical milestones in AI development
- Technological responsibility is crucial
- The need for frameworks aligning AI with human benefits
| Aspect | Details |
|---|---|
| Definition of AI | Computer systems that can perform complex tasks normally done by human reasoning |
| Historical Milestone | 1956 Dartmouth workshop |
| Notable Event | IBM’s Deep Blue defeating Garry Kasparov in 1997 |
| Current Impact | Large language models and advanced algorithms |
| Main Concerns | AI’s potential impact on jobs and society |
| Future Focus | Ensuring AI serves broader human benefits |
