“I know what many of you are feeling about [AI]. I can hear you.” – Eric Schmidt – Former Google CEO in response to University of Arizona students boos and jeers

Public unease with artificial intelligence is no longer abstract speculation but an audible force shaping how a new generation encounters power, work, and technology. When a graduating class responds to an AI evangelist with boos rather than applause, it exposes not only scepticism about the technology but distrust of those who built and profit from it.¹ The tension is no longer simply over what AI can do; it is over who decides, who benefits, and who pays the cost as labour markets, information systems, and democratic processes are rewired around machine learning and large-scale automation.³

The University of Arizona commencement became an unexpected stage for this conflict. Former Google chief executive Eric Schmidt, long a prominent advocate of AI as a transformative general-purpose technology, referenced artificial intelligence and was met with jeers, groans and boos from students facing an uncertain labour market.^1,3 The discontent did not arise in a vacuum. Graduates have grown up through the global financial crisis, the platform era, and a pandemic that accelerated remote work and digital substitution; they have seen each wave of innovation framed as opportunity while also watching wages stagnate and housing, healthcare, and education costs rise. AI now appears as the next chapter in that story, and the students’ response reflects a belief that the chapter may again be written over their heads.

The substantive worry: AI, agency, and the future of work

The central fear animating the boos is less about science fiction-style superintelligence and more about immediate economic displacement. Generative AI systems are already capable of drafting text, generating images, writing code and performing customer support tasks that resemble the entry-level roles many graduates rely upon to begin their careers. Employers, investors and consultancies openly discuss headcount reductions, productivity gains and the reconfiguration of white-collar work through large language models and automation tools.^2,3 When this narrative is carried onto a graduation stage by someone deeply associated with the first wave of internet platforms, students hear not a promise but a warning.

Behind this is a broader question of agency. In the same speech, Schmidt argued that speaking of the future as if it is already decided means surrendering agency, insisting that the future is built in laboratories, dormitories, startups, classrooms and legislatures by people like the graduates.⁵ This framing invites students to see themselves as co-authors of AI’s trajectory rather than passive victims. Yet, when delivered by a figure who has already helped set much of the digital agenda, the message can feel like an evasion: if younger generations truly have agency, why was so much of the AI infrastructure – from data harvesting to surveillance advertising to the centralisation of cloud compute – designed without their input?

The graduates’ reaction reveals a clash between two understandings of agency. One is the innovation-centric view: individuals, by learning to use AI tools, founding companies or engaging with policymakers, can shape outcomes. The other is a structural view: when market power, capital and technical expertise are concentrated in a small set of firms and investors, individual “choices” are constrained within a narrow set of paths. Hearing that they retain agency while watching hiring freezes, restructuring and AI-driven “efficiencies” sweep through industries, many students understandably doubt how much real choice they will have over the terms of their working lives.³

Who is speaking, and why that matters

Reactions to technology are always coloured by who is doing the talking. Eric Schmidt is not just a technologist; he is a symbol of an era when Silicon Valley’s mantra was to “move fast and break things”, and when global platforms built vast fortunes by capturing user data and attention. Under his leadership, Google expanded aggressively, cementing the search and advertising business model that remains at the heart of many AI deployments today.¹ To a cohort that has wrestled with online misinformation, mental health impacts of social media and the erosion of local journalism, that history shapes how any reassurance about AI is received.

It is for this reason that the remark “I know what many of you are feeling about AI. I can hear you.” lands in two directions at once. On the surface, it signals empathy and acknowledgement, an attempt to de-escalate the tension in the stadium.¹ Yet for some listening, it may also sound like a rhetorical device to neutralise dissent rather than substantively address it. To say “I can hear you” while continuing broadly the same narrative of AI as inevitable progress risks reinforcing the suspicion that powerful actors are listening only long enough to continue speaking.

There is also a generational dimension. Many students grew up with the rhetoric that coding, STEM skills and adaptability would secure their future. Now, AI systems are being developed that partially automate coding itself, support or replace knowledge work, and extend surveillance capabilities at work. The messenger is someone who prospered under the previous digital regime, telling them they will have agency in the next one. The contrast between lived experience and elite reassurance is one driver of the boos.³

Factual context: a year of backlash and celebration

The graduation incident did not occur in isolation but against a backdrop of escalating debate over AI’s risks and benefits. In the months preceding the University of Arizona ceremony, governments convened AI safety summits, regulators proposed new rules for model transparency and data use, and multiple open letters from researchers and industry figures called for pauses or stronger oversight of frontier systems. At the same time, enterprises raced to embed AI into productivity suites, cloud platforms and consumer services, aiming to capture new markets and efficiencies.²

Within universities themselves, AI has become both tool and threat. Students use chatbots for drafting essays, debugging code and planning projects. Academics worry about plagiarism, the erosion of critical thinking and the devaluation of learning if assessments can be automated or short-circuited by text generators. Institutions wrestle with policy responses that balance innovation with academic integrity. In this environment, a high-profile AI advocate speaking at commencement enters a campus already saturated with contested experiences of the technology, from helpful assistance to opaque grading tools and proctoring systems that track gaze and keystrokes.

Business leaders are acutely aware of this ambivalence. Other technology executives giving graduation or public speeches have been similarly cautious, acknowledging concerns about job displacement and bias while encouraging graduates to see AI literacy as essential to their future.² The Arizona boos were widely reported in business and technology media as a signal that AI’s public-relations challenge is deepening, especially among the demographic most courted as a source of digital talent and consumption.^1,3

The strategic tension: inevitability versus contestability

Beneath the surface, there is a strategic tension between framing AI as an unstoppable wave and presenting it as a contested field of choices, standards and governance. Corporations pushing rapid deployment emphasise competitive pressures: if one company or country slows innovation, another will surge ahead. This narrative supports light-touch regulation and rewards early movers who can lock in data, compute capacity and market share. On the other hand, scholars, labour advocates and civil society groups argue that AI development is deeply shaped by legal rules, public investment, collective bargaining and social movements; far from being inevitable, its trajectory is malleable.

Schmidt’s line about the future being built in labs, dormitories, startups and legislatures implicitly endorses the second view: that the future is made, not preordained.⁵ Yet his career has been spent in organisations that benefited immensely from the first narrative, using claims of inevitability to resist or soften regulation, from data protection to antitrust. Graduates listening to his appeal may therefore perceive a strategic repositioning: AI is framed as something they can shape, but in practice the largest design decisions – such as whether models are open or closed, which languages and cultures are prioritised, and how training data is gathered – remain concentrated among a few major firms and research labs.

This tension matters because it affects how societies respond to AI. If people internalise the idea that AI is inevitable, they are more likely to accept job losses, privacy intrusions and centralised power as unavoidable side effects. If they see AI as contestable, they may demand stronger labour protections, public investment in alternative models, or democratic control over high-risk deployments. The boos at Arizona are an instance of the latter stance: a refusal to quietly accept the inevitability narrative, expressed in one of the few moments where graduates collectively encounter a high-profile architect of the digital economy.³

Labour, value and the invisible contributions behind AI

Another layer to the students’ response involves who is recognised as contributing to AI and who is left invisible. Modern AI systems rely on vast amounts of labelled data, content produced by millions of users, and the labour of human annotators who classify images, filter toxic content or rate chatbot responses. Much of this work takes place in precarious conditions, often in the global South, for modest pay and limited protections. Graduates entering a world where such labour underpins the tools they are told to embrace are increasingly aware of these inequalities through reporting and activism.

When a prominent figure declares “I can hear you”, students may be asking a different question: who hears the content moderators exposed to traumatic material, or the ghost workers whose evaluations train recommendation systems? When AI is framed primarily in terms of innovation and entrepreneurship, these forms of labour are marginalised. The backlash at ceremonies and in online debate reveals a growing insistence that any serious conversation about AI include the full supply chain of value creation and harm, not only the glamorous front-end applications or the high-level rhetoric about productivity and disruption.

Trust, legitimacy and the politics of listening

At a symbolic level, the exchange at Arizona is about trust. Large technology firms have repeatedly assured users, employees and regulators that they can be trusted to handle data responsibly and mitigate harms. Yet repeated scandals – from privacy breaches to algorithmic discrimination – have eroded that trust. When leaders from this ecosystem now take on quasi-statesman roles, addressing graduating classes about the future of democracy, work and knowledge, their legitimacy is contested.

To say “I can hear you” is an attempt to rebuild some degree of legitimacy by acknowledging discontent. But effective listening requires more than recognising emotional states; it demands concrete changes in governance, accountability and benefit-sharing. For AI, this might mean giving workers stronger rights around algorithmic management, supporting unions negotiating over automation, funding independent public research on AI impacts, and involving affected communities in determining where high-risk systems are deployed. Without visible shifts of this kind, reassurance can be read as condescension rather than solidarity.

Universities themselves are caught in this legitimacy problem. They partner with technology companies through research collaborations, recruitment pipelines and sponsorships. They also host critical scholarship on AI ethics, fairness and regulation. Students thus encounter both celebratory and critical narratives about AI within the same institution. The boos at commencement can be interpreted as a verdict on this dual role: a demand that universities align their institutional endorsements – including choice of speakers – with the critical perspectives students encounter in classrooms and lived experience.

Debates and objections: is the backlash short-sighted?

Not everyone sees the booing as justified. Some commentators argue that rejecting AI talk at graduation is short-sighted, given that AI skills and literacy are likely to be valuable for employability and civic participation. From this perspective, students should engage deeply with AI, shaping its ethical and societal parameters from within rather than resisting it from the sidelines.² They might point out that earlier generations expressed similar fears about computers, automation and the internet, yet those technologies also created new roles, industries and forms of expression.

There is also an objection that public backlash risks empowering actors who seek to halt AI research entirely or to use safety rhetoric to cement the dominance of incumbent firms. If fear leads to overly restrictive regulation focused solely on speculative existential risks, smaller players, open-source communities and public-interest research could be squeezed out, leaving only the largest corporations able to comply. In that scenario, some suggest, students’ legitimate concerns about concentrated power might inadvertently support further concentration.

However, defenders of the students counter that boos are not policy proposals but expressions of frustration at a policy landscape they did not design. Public dissent can coexist with nuanced engagement; indeed, it may be a prerequisite for moving beyond abstract optimism towards concrete, accountable arrangements. They note that the students did not demand a return to a pre-digital age; rather, they objected to being addressed by a powerful figure who appeared insufficiently responsive to the asymmetries in how AI’s benefits and harms are distributed.³

Why this moment matters

The significance of a brief exchange at a graduation ceremony lies in how it crystallises several converging dynamics. First, it captures the generational shift from early internet utopianism to a more sceptical, structurally informed view of technology. Graduates are not indifferent to AI; many are proficient users and aspiring builders. But they approach it with memories of earlier waves of disruption that did not deliver on their promises of broad-based prosperity.

Second, it highlights the growing expectation that those who have led major technology firms must address not only innovation narratives but also questions of justice, power and accountability. A simple reassurance that “I can hear you” is no longer sufficient when the stakes involve livelihoods, democratic resilience and the terms on which human and machine intelligence are integrated into everyday life. The audience wants more: concrete commitments, recognition of past harms, and a willingness to redistribute power over how AI is developed and governed.

Third, the incident demonstrates that AI’s social licence cannot be taken for granted. For years, AI was largely a technical matter, discussed in specialist communities. Now, as it touches education, creative work, medicine, law and public administration, its legitimacy depends on broad public consent. Graduation ceremonies, civic forums and workplace meetings become sites where that consent is negotiated – sometimes politely, sometimes through jeers.

Finally, the exchange underscores that listening is itself a political act. To hear the boos as irrational technophobia is to miss the rational core of concern about job precarity, surveillance and concentrated control. To hear them as a veto on AI development would be equally mistaken. The challenge for leaders, whether from industry, government or academia, is to treat such moments as opportunities to reframe AI not as destiny but as a contested, governable set of tools whose deployment reflects collective choices. For the graduates in Arizona, the boos were a way of asserting that they intend to be part of those choices – and that being “heard” means more than being briefly acknowledged before the script resumes.^1,3,5

 

References

1. “Former Google CEO Eric Schmidt booed by graduates at mention of AI” – https://www.bbc.com/news/articles/ce8pqd54qneo

2. Former Google CEO Eric Schmidt booed at University of Arizona … – 2026-05-18 – https://www.foxbusiness.com/politics/eric-schmidt-booed-ai-university-arizona-commencement

3. Sundar Pichai Says Graduates Booing AI Will Live With Tech’s Impact – 2026-05-24 – https://www.businessinsider.com/sundar-pichai-google-graduation-speech-stanford-ai-backlash-eric-schmidt

4. Arizona Students Boo Ex-Google CEO Eric Schmidt During … – 2026-05-16 – https://www.businessinsider.com/students-boo-eric-schmidt-google-ceo-ai-university-arizona-2026-5

5. Former Google CEO Eric Schmidt Booed At University of … – YouTube – 2026-05-17 – https://www.youtube.com/shorts/CA217AqFatE

6. Ex-Google CEO Gets Booed While Discussing AI in Commencement … – 2026-05-18 – https://www.youtube.com/watch?v=tNH43a1EI7s