Perspectives on the Future of AI from Tech Leaders and Students

Editor’s note (scope & sourcing): This article is a synthesis of perspectives and quotes reported in WIRED’s interviews with tech leaders and UC Berkeley students, supplemented by referenced external reporting and research cited below. It is not original reporting, and it should not be read as medical, legal, or educational advice.

Tech leaders and students envision AI’s impact

• AI is already woven into daily life in ways that resemble the rise of search—often practical, mundane, and frequent.
• Students and tech leaders diverge on how much AI should shape learning, with some embracing it and others avoiding it on principle.
• Health and childcare are emerging as major real-world use cases, even as experts warn about new kinds of errors and privacy risks.
• Trust remains a bottleneck: many people use AI, but far fewer say they trust it “a lot,” intensifying calls for safety testing and governance.

Insights from Tech Leaders on AI’s Future

Author context: I’m Martin Weidemann (Weidemann.tech), a digital transformation and fintech/payments builder with 20+ years of experience operating technology products in regulated environments across Mexico and Latin America. That background informs how I read themes like trust, safety testing, and governance—but the viewpoints summarized here come from the cited interviews and sources.

For many of the tech leaders interviewed, the most striking feature of today’s AI moment is not a single breakthrough, but the speed and breadth of deployment. AI is arriving not as a niche tool but as an ambient layer—something people “rely on every day,” as Anthropic cofounder and president Daniela Amodei put it, and something companies are shipping in a “wide-open regulatory environment” where self-policing often substitutes for formal guardrails.

That reality shapes how leaders talk about the future: less as a distant horizon and more as a series of launches, each with consequences. Mike Masnick, founder of Techdirt, framed the core pre-launch question bluntly: “What might go wrong?” It’s a deceptively simple prompt that implies a discipline many critics say is missing—anticipating harms before products scale.

Amodei offered a concrete analogy: safety testing should resemble crash tests in the auto industry, with companies asking how confident they are that they’ve tested enough before releasing a new agent. Her personal benchmark—whether she’d be comfortable giving the tool to her own child—captures a broader shift in the AI conversation: from capability demos to reliability, misuse, and dependency.

Cloudflare CEO Matthew Prince emphasized trust as a prerequisite, not a marketing afterthought. That emphasis lands in a public climate where usage is rising but confidence is not: a YouGov survey found 35 percent of US adults say they use AI daily, yet only 5 percent say they “trust AI a lot,” while 41 percent are distrustful. Meanwhile, an Ipsos poll showed global trust in AI companies to protect personal data fell from 2023 to 2024—an erosion that leaders can’t ignore if they want adoption to endure.

Student Perspectives on AI Integration

On campuses, AI is not an abstract debate about the future of work or the nature of intelligence. It’s a daily decision: use the tool, avoid it, or try to draw a line between help and substitution. The student voices in the interviews reveal a split that is less about technical literacy—many have grown up surrounded by digital systems—and more about what they believe learning is for.

Angel Tramontin, a student at UC Berkeley’s Haas School of Business, described a pattern that has become common among heavy users: “I use a lot of LLMs to answer any questions I have throughout the day.” The phrasing matters. It’s not “for homework” or “for coding,” but for “any questions,” suggesting AI has become a general-purpose interface for curiosity, planning, and micro-decisions—much like search once was.

Other students described more targeted, creative uses. Gonzalo Vasquez Negra, pursuing an MBA at Berkeley, said he is working on a presentation to teach people how to use AI in Peru—an example of students not just consuming tools but acting as translators and trainers, spreading practices beyond Silicon Valley. Gilliane Balingit, another Berkeley student, said she used AI for writing poetry and for editing support: she has “a hard time with editing my writing,” and used AI to “help me enhance my thoughts and my feelings.” That’s a revealing use case: not outsourcing the act of expression entirely, but leaning on the tool as a scaffold.

Yet there is also principled resistance. UC Berkeley undergraduate Sienna Villalobos said, “I try not to use it at all,” arguing that “AI shouldn’t be able to give you an opinion.” Her concern is not only about cheating; it’s about agency—whether students will still practice forming judgments when a machine can generate plausible stances on demand.

That skepticism is echoed in her broader critique of the industry’s incentives. Villalobos said she believes many companies “put financial gain over morality,” calling it “one of the biggest dangers.” In student terms, the question becomes: if the tool is everywhere, who is shaping it—and whose values does it encode?

Practical Applications of AI in Daily Life

AI’s most visible impact is often not cinematic or futuristic. It’s the quiet replacement of small frictions: drafting, summarizing, searching, and advising. In the interviews, both students and tech leaders described using large language models in ways that mirror the early days of web search—frequent, casual, and sometimes so routine it barely registers as “AI use” at all.

Several respondents noted they had used AI within the last few hours, even minutes. That immediacy matters because it reframes the technology from “emerging” to “embedded.” When a tool becomes something you consult reflexively, it begins to shape not just outcomes but habits: how you ask questions, how you verify answers, and how quickly you move from uncertainty to action.

The use cases described also show how AI is expanding beyond work tasks into intimate domains—family logistics, health anxieties, and personal writing. That expansion is part of why AI companies are positioning health as a growth area, and why privacy and safety concerns are intensifying at the same time. The more sensitive the question, the higher the stakes of a confident but wrong answer—and the more consequential the data trail.

At the same time, the practical appeal is hard to deny. People reach for tools that reduce cognitive load, especially when they are stressed, busy, or unsure where to start. In that sense, AI is functioning as a first draft of thinking: a starting reference point that can be refined, challenged, or discarded. The problem, as critics warn, is that “starting reference point” can quietly become “final authority” when users are tired, rushed, or overly trusting.

Using LLMs for Everyday Questions

The most common pattern described in the interviews is also the least dramatic: using LLMs as an always-available answer engine. Tramontin’s line—using LLMs to answer questions “throughout the day”—captures how AI is becoming a general interface for information, similar to how search became a default behavior after the “Alta Vista days.”

This matters because it suggests a shift in where people place their first click. Instead of opening a browser and scanning sources, some users begin with a synthesized response. That can be efficient, but it also changes the user’s relationship to uncertainty. Search results force you to choose among links; a chatbot offers a single narrative, which can feel more authoritative than it deserves.

The interviews also hint at a broader phenomenon: people may be using AI without explicitly choosing to. As AI becomes intertwined with products like search—through integrations such as Google Gemini—many users may interact with AI features “without even realizing it or intending to.” That blurs the boundary between deliberate adoption and passive exposure, complicating the idea of consent and informed use.

Everyday-question usage also raises a subtle educational issue: if AI becomes the default for quick explanations, users may practice fewer “micro-skills” of reasoning—like checking assumptions, comparing sources, or sitting with ambiguity. None of the interviewees claim this is inevitable, but the pattern is clear: the tool is easiest to use when you treat it like an oracle, and hardest to use when you treat it like a fallible assistant.

AI in Childcare and Health Advice

Some of the most striking examples of AI’s everyday role came from parenting and health—domains where anxiety is high and time is scarce. Amodei said she has used Anthropic’s Claude to assist with childcare: “Claude actually helped me and my husband potty-train our older son,” she said. She also described using it for “the equivalent of panic-Googling symptoms” for her daughter.

Film director Jon M. Chu described a similar impulse, turning to LLMs “just to get some advice on my children’s health,” adding, “which is maybe not the best,” but “it’s a good starting reference point.” The caveat is important: even enthusiastic users recognize the risk of treating a chatbot as a clinician. But the behavior persists because the tool meets a real need—fast, conversational triage when people don’t know what to ask or where to begin.

The expansion into health underscores a tension: AI can widen access to information, but it can also introduce new errors. As physician Eric Topol warned, medicine already has many errors, and “we also don’t want to have new ones, or make that any worse by AI.” In other words, usefulness is not the same as safety—and in health, the gap can be costly.

The Growing Use of AI Among Teens

Among teenagers, AI is no longer a novelty—it’s becoming a normal part of digital life. A recent Pew Research study found that nearly two-thirds of US teens use chatbots. About 3 in 10 report using them daily. Those numbers matter not just for what they say about adoption, but for what they imply about habit formation: daily use suggests AI is becoming part of the routine infrastructure of adolescence, alongside messaging, video, and search.

The growth also complicates the idea of “AI users” as a distinct group. If chatbots are used by most teens, and if AI is increasingly embedded in search and other tools, then AI becomes less like a product you opt into and more like a default layer of the internet. That makes it harder for parents, educators, and policymakers to rely on individual choice as the main safeguard.

The interviews suggest teens and young adults are using AI for both mundane and meaningful tasks: answering questions, editing writing, even generating poetry. These uses can be framed as productivity or creativity, but they also raise questions about dependency. If a student consults an AI for “any questions” throughout the day, what happens to the skills that used to sit between question and answer—like evaluating sources, tolerating uncertainty, or building an argument from scratch?

At the same time, teen adoption is happening in a broader climate of institutional uncertainty. Schools are still debating policies, companies are iterating quickly, and regulation remains limited. That means many teens are learning norms from peers and platforms rather than from clear guidance. The result is uneven literacy: some will learn to treat AI as a tool to be questioned, while others may treat it as a shortcut to be exploited.

The scale of teen usage also raises a practical point about measurement. That makes survey numbers both important and incomplete: reported chatbot use may undercount AI exposure, because the boundary between “chatbot” and “internet” is dissolving.

Concerns About AI in Education

Education is where AI’s promise and its pitfalls collide most visibly. In theory, generative tools can support learning—helping students brainstorm, critique model work, or organize ideas. In practice, educators and students are grappling with a more immediate reality: AI makes it easier to bypass the very cognitive labor that school is meant to cultivate.

An Education Week opinion essay from an English/language arts teacher describes what that looks like on the ground. Since 2022, the teacher says they have seen “upward of 100 AI-generated responses” submitted as “original” work. The problem is not only volume; it’s verification. AI detectors vary in reliability and can produce false positives, making it difficult to prove misconduct definitively.

To compensate, the teacher relies on process evidence—Google Docs history and tools like Draftback or Revision History—to see how students draft. But even that workaround is being undermined: students began typing out AI-generated responses to create an artificial drafting history. The teacher describes patterns like writing a full response in one sitting in 15–30 minutes with few revisions—unlike human writing.

This is not just a cat-and-mouse game. It changes relationships. The teacher writes about bracing when reading student work, and about how distrust creates a barrier between teacher and student. False accusations can be “emotionally crushing,” even if a student is cleared. In other words, AI doesn’t only affect assessment; it affects classroom culture.

Administrators, meanwhile, may feel pressure to “embrace” AI to signal innovation. But the teacher’s question—where is the train headed, and do we want to go there?—captures a growing unease: adoption is happening faster than consensus on what counts as learning, what counts as cheating, and what kinds of assistance are acceptable.

Ethical Implications of AI Usage

The ethical challenge in education is not simply whether students will use AI, but how schools define legitimate help. The Education Week essay argues that education is about process, not product: “Writing is thinking,” the teacher writes, describing writing as a generative and metacognitive process. If AI is used to avoid that process, the ethical issue is not plagiarism in the traditional sense—it’s the outsourcing of thinking.

That becomes especially fraught when students use AI for prompts designed to be personal or reflective. The teacher reports students using AI for opinion-based questions like “Which character in Gatsby is most insufferable and why?” and for personal reflections like “Describe a time you knew you were learning.” These are prompts meant to elicit judgment and self-knowledge. If a chatbot can answer them convincingly, the ethical question shifts from “Did you copy?” to “Did you participate in the learning experience at all?”

There is also an equity dimension implied in broader AI governance discussions: AI systems can exacerbate inequalities and biases if not designed and governed carefully. The Berkman Klein Center notes that AI’s growing presence raises questions about governance, accountability, and how to harness potential without worsening existing inequities. In education, that concern can translate into who benefits from AI tools, who is penalized by surveillance-like enforcement, and whose writing style might be misread by unreliable detectors.

Finally, ethical use is not guaranteed by instruction alone. The teacher argues that teaching students to use AI ethically “does not mean they will stop using it to avoid cognitive labor.” That’s a sobering point for schools hoping that a policy document or a workshop will solve what is, at root, a motivational and cultural problem.

Challenges in Academic Integrity

Academic integrity is where AI’s practical capabilities collide with institutional limits. The Education Week essay lays out the core dilemma: it can be “very easy to tell” when a response sounds like a chatbot rather than a high schooler, but it is “difficult to definitively prove” AI generation. Detectors are inconsistent and can falsely accuse students, raising ethical and procedural risks for schools.

As a result, enforcement shifts toward monitoring process rather than evaluating product. The teacher uses Google Docs history and tools like Draftback to watch drafting in real time, noting that students who use generative AI often paste in a large block of text. But students adapt. When they began typing out AI-generated responses, they created a plausible-looking revision trail—one that still felt inhuman to the teacher, but harder to prosecute fairly.

This creates a structural problem: the more schools try to police AI use, the more they may incentivize sophisticated deception. And because proof is elusive, teachers may either over-enforce (risking false accusations) or under-enforce (normalizing cheating). Either path corrodes trust.

The teacher’s response—returning to pencil and paper, doing most writing in class—highlights a broader trend: some educators may retreat to environments where AI is harder to use. That may protect integrity, but it also narrows the kinds of assignments students can do and may reduce opportunities to learn responsible AI use.

Meanwhile, the “train is leaving the station” argument—used to justify adoption—doesn’t resolve integrity questions. It simply accelerates them. If AI can answer “most prompts” regardless of how personal or creative, then schools face a fundamental redesign challenge: how to assess learning when the product can be generated on demand.

The Role of AI in Health and Wellness

Practical note: When this section discusses people using chatbots for symptoms or childcare-related questions, treat those examples as descriptions of behavior and product direction—not as recommendations. For health concerns, consult qualified professionals and follow applicable local guidance.

Health and wellness sit at the center of AI’s next expansion, driven by two forces visible in the interviews: user behavior and corporate strategy. Users are already asking chatbots about symptoms, children’s health, and wellness routines. Companies are responding by building products and pitching institutions, betting that health will be a durable growth area.

OpenAI’s announcement of ChatGPT Health is a signal of scale and intent. The company disclosed that “hundreds of millions of people” use ChatGPT to answer health and wellness questions each week, and said the health-focused experience introduces additional privacy measures because of the sensitivity of those queries. Anthropic’s Claude for Healthcare, aimed at hospitals and health care systems, points to a parallel market: enterprise adoption in clinical settings, where AI could be used for workflows and analysis.

But the interviews also underline why health is uniquely risky. People often consult AI when they are anxious—“panic-Googling symptoms,” as Amodei described it. Anxiety can reduce skepticism, making users more likely to accept confident answers. That’s a dangerous mismatch with a technology that can be wrong in plausible ways.

Physician Eric Topol framed the concern in clinical terms: medicine already contains many errors, and the goal should not be to introduce new ones or worsen existing problems through AI. His warning doesn’t deny potential benefits; it insists on a standard of care. In health, “useful” is not enough—systems must be reliable, and failure modes must be understood.

The health use cases also intensify privacy stakes. If people are asking about symptoms, mental health, or children’s conditions, the data involved is among the most sensitive they generate. That suggests a widening gap between what people are doing (sharing intimate questions) and what they believe (doubting companies’ stewardship).

Trust and Skepticism Towards AI Technologies

AI’s adoption curve is colliding with a trust deficit. Surveys cited in the interviews show a public that is experimenting widely but believing cautiously. That gap—between use and trust—helps explain why calls for safety testing, transparency, and governance are growing louder even as products proliferate.

Trust is also being strained by the legal and reputational environment. The interviews note that a series of high-profile lawsuits over alleged harms caused by AI has further strained public views of some chatbot providers. Lawsuits don’t prove guilt on their own, but they shape perception: they signal that harms are plausible, that accountability is contested, and that the industry’s social license is not guaranteed.

Tech leaders themselves acknowledge that trust must be built, not assumed. Cloudflare CEO Matthew Prince emphasized establishing trust before launching new products. That’s notable coming from a company that has taken steps to keep AI companies accountable for scraping websites for training data—suggesting that even AI optimists see the need for constraints and norms.

Skepticism is not limited to the general public. Villalobos’ critique—believing companies prioritize financial gain over morality—reflects a broader suspicion about incentives in a “wide-open regulatory environment.” When companies are “largely left to self-police,” as the interviews put it, users may reasonably ask whether safety and privacy are being treated as core requirements or as public-relations features.

Michele Jawando, president of the nonprofit Omidyar Network, framed the trust problem as a governance problem: “Who does it hurt, and who does it harm?” If a company can’t answer, she argued, “you don’t have enough people in the room.” The implication is that trust is not only about technical performance; it’s about representation, accountability, and whether affected communities have a voice before deployment.

Future Challenges and Opportunities in AI Development

The next phase of AI development will be shaped as much by governance and social impact as by model capability. The interviews and related reporting point to a landscape where deployment is relentless, regulation is limited, and the consequences—on labor, education, health, and information ecosystems—are arriving faster than institutions can adapt.

One major challenge is the changing nature of work. Berkeley students cited job security as a long-term concern, with Abigail Kaufman describing stress on campus about whether fields students are entering “are going to still be a field.” Jeremy Allaire, CEO of Circle, echoed the uncertainty: changes in labor could impact people and the economy, and “no one really seems to have good answers.” The reporting also notes Stanford University economists have found employment opportunities for young people are already in decline, and that multiple tech giants have cited AI as a rationale for restructuring workforces. Even without precise forecasts, the direction is clear: AI is becoming a factor in how companies justify reorganizing labor.

Another challenge is content governance and the amplification of harms. In a post-Davos discussion of AI governance, Michael Posner argued that the choice between unfettered innovation and stifling regulation is a false one, calling instead for collaboration between government and tech companies to build ethical guardrails. He highlighted risks including content moderation pressures and the rise of deepfakes, noting controversies such as Grok enabling users to remove clothing from images of women without consent—an example used to argue for better regulation while protecting free expression.

Infrastructure and sustainability also loom. Posner described rapid data center expansion and warned that companies are moving fast without adequately considering natural resources consumed or communities disrupted—pressures expected to be particularly acute in the Global South. While the interviews focus more on daily use and trust, the infrastructure point underscores that AI’s footprint is physical as well as digital.

At the same time, opportunities are real: AI can improve efficiency and equity if deployed with guardrails, as discussed at Florida State University’s AI Day in the Capital. Speakers emphasized moving beyond theory into real-world applications, while stressing data quality, bias, and security—especially when minors and public institutions are involved. The opportunity, in other words, is not just to build more powerful systems, but to build systems that can be trusted in the places people most need them.

Navigating the Future of AI: A Collective Vision

The interviews leave a clear impression: AI’s future will not be decided by technologists alone, nor by students, nor by regulators in isolation. It will be negotiated—through product launches, classroom policies, lawsuits, institutional procurement decisions, and public opinion. The most consistent thread across voices is not a single prediction, but a shared recognition that consequences must be anticipated, not discovered after the fact.

The Role of Collaboration in AI Development

Multiple sources point toward collaboration as the missing mechanism in today’s AI environment. Posner’s post-Davos argument calls for government and tech companies to “forge a third path” between no oversight and innovation-killing regulation. The premise is that neither side can solve the problem alone: governments need technical understanding and agility, while companies need legitimacy and guardrails that markets won’t reliably supply.

That collaborative instinct also appeared in institutional settings. Florida State University’s AI Day in the Capital was designed to “demystify” AI by moving beyond theory into real-world applications, bringing together government, law, technology, and academia. The event emphasized interdisciplinary conversation about AI that connects innovation, policy, and public impact—an implicit acknowledgment that AI’s effects cut across domains.

Michele Jawando’s point—“If you don’t know the answer, you don’t have enough people in the room”—is a practical test for collaboration. It suggests that harm assessment requires diverse perspectives, including those likely to be affected by deployment. Collaboration, in this framing, is not a feel-good principle; it is a method for seeing risks that homogeneous teams miss.

Ethical Considerations for AI Implementation

Ethics emerges less as a philosophical add-on and more as an operational requirement. Masnick’s “What might go wrong?” is an ethical question disguised as a product question. Amodei’s call for safety testing akin to crash tests is an ethical stance translated into engineering practice: test before release, because people will rely on the system.

The Berkman Klein Center’s framing of AI as raising pressing questions about governance, accountability, and inequality reinforces that ethical considerations include who benefits and who bears costs. In education, the ethical stakes show up in the tension between efficiency and learning: the Education Week teacher argues that “efficiency is not the goal,” and that offloading feedback to a machine can deprive students of collaboration with peers.

In health, ethical considerations include not worsening error rates and protecting sensitive data. OpenAI’s decision to add privacy measures to ChatGPT Health reflects an acknowledgment that health queries demand a higher standard of care—though public trust data suggests many users remain unconvinced that companies will protect personal information.

The Importance of Public Trust in AI Technologies

Public trust is the hinge on which AI’s social acceptance will turn. The surveys cited—high daily use, low deep trust—suggest a fragile equilibrium: people are experimenting because the tools are useful, but they are not granting broad confidence in the institutions behind them.

Prince’s emphasis on establishing trust before launch aligns with that reality, as does the recognition that lawsuits and perceived harms can quickly erode legitimacy. Villalobos’ skepticism about corporate morality highlights the reputational risk of a self-policing model in a lightly regulated environment.

Trust is also shaped by how AI is introduced into sensitive settings like schools and hospitals. In classrooms, the Education Week teacher describes how AI has complicated relationships and created distrust between teacher and student—an example of how technology can corrode trust at the interpersonal level, not just the institutional one. In health, Topol’s warning about new errors underscores that trust must be earned through reliability, not just accessibility.

If AI is to become as integrated as search—an analogy raised directly in the interviews—then trust will need to become similarly infrastructural: not blind faith, but a baseline confidence that systems are tested, accountable, and designed with the public’s interests in mind.