After the AI Hype Fades: How Humanity Can Reclaim Control – Insights by Rafael Behr

AI hype pushed promises of instant productivity, infinite creativity and fully automated companies. Three years after ChatGPT’s record-breaking launch, the money and noise around the future of AI are louder than ever, yet daily life for most people still feels confusing and fragile. Gigantic valuations, trillion‑dollar infrastructure bets and geopolitical rivalry turn an abstract technology into a system that shapes work, politics and culture before society has a chance to decide how much control it wants to keep. Rafael Behr’s analysis of this moment treats AI not as magic software but as a mirror of human greed, fear and hope.

Behind the slogans about progress sits a hard question: what happens to Humanity when AI hype deflates and the bills arrive. A trillion in long‑term AI commitments would take tens of thousands of years to spend at one dollar per second. US tech giants, from Microsoft’s stake in OpenAI to Google, Amazon and Meta, depend on AI impact to justify such risks and to keep stock markets alive. China takes a different route, flooding daily life with “good enough” tools tied to surveillance and censorship. Between Silicon Valley libertarians and authoritarian planners, the space for democratic control shrinks fast. The real challenge is not to predict super‑intelligence but to decide how people reclaim power over systems already embedded in jobs, schools, newsrooms and childhood.

AI Hype, Bubbles and Rafael Behr’s Warning on Control

The first layer of AI hype is financial. OpenAI’s valuation and the dense web of deals around data centers, chips and cloud contracts turn a single research lab into the core of a $1.5 trillion bet. Analysts compare this cycle with the dot‑com era, yet some argue this time looks closer to a structural shift in infrastructure. A detailed comparison of AI speculation and earlier manias appears in analyses such as this review of AI versus the dot‑com bubble, where short‑term excess coexists with durable platforms.

Rafael Behr points to a deeper bubble behind the market: an inflated belief among a few executives that they stand on the brink of “computational divinity”. In this narrative, once a system reaches general intelligence it designs its successors, prints value and renders human planning obsolete. That fantasy weakens appetite for tough rules, because any delay looks like a loss of destiny. The gap between that vision and present reality is where Humanity risks losing Control over the future of AI to a small circle of optimists and lobbyists who treat public institutions as obstacles, not partners.

AI Impact on Jobs, Workflows and Daily Life

While the marketing pushes transcendence, AI impact today looks more mundane and uneven. In call centers, retail logistics and software teams, managers integrate language models and automation into existing tools instead of replacing entire departments overnight. Some studies, such as large consulting reports on productivity, highlight efficiency gains but also point to job redesign and mid‑career stress. A good example is the discussion in the Deloitte AI report on workforce changes, which stresses that tasks shift faster than job titles.

Consider a fictional mid‑size firm, NorthRiver Services, that handles customer support, billing and basic technical assistance. Under pressure from investors, the CEO rolls out a chatbot to triage customer queries, integrates AI summaries into CRM dashboards and pilots code assistants for the IT team. For high‑volume, simple tickets, response time drops. For complex complaints, frustration rises when generative replies hallucinate policy details or misinterpret legal obligations. Staff now spend time correcting machine output and managing angry callers. AI impact on stress, not only efficiency, becomes part of the hidden cost curve, which the initial AI hype never advertised.

Future of AI: Between Utopian Automation and Human Limits

Rafael Behr argues that the core fantasy behind the future of AI is emancipation from human input. Once systems design better versions of themselves, the story goes, economic growth accelerates beyond historical precedent. Advocates talk about AI as a “good bubble” that finances infrastructure and scientific discovery even if many investors lose money. In that framing, the human suffering of those on the wrong side of automation becomes collateral for a supposedly higher goal.

Yet every wave of automation has revealed limits that hype ignored. From railways to the internet, physical constraints, regulation and social backlash forced adjustments. In AI, current systems still rely on massive human labor for labeling data, moderating content and writing code to connect models with legacy software. The real constraint might not be compute, but society’s tolerance for error, bias and opaque decision chains. Can a financial regulator accept a risk model it cannot audit. Will a hospital trust a diagnostic suggestion without a traceable path from symptoms to decision. Those frictions keep Humanity inside the loop, even as marketing forecasts a clean break.

Geopolitics, AI Hype and the Race for Supremacy

The AI hype cycle plays out differently in Washington and Beijing but with a similar end state: concentration of power. US tech platforms push for frontier models, betting that a single breakthrough in general intelligence secures economic and military advantage. China spreads “good enough” systems through manufacturing, social scoring and public services, tying AI impact directly to party control. The article on AI, Chinese censorship and surveillance describes how this approach treats machine learning as an extension of political infrastructure rather than a neutral tool.

In both blocs, national security arguments weaken global cooperation on standards and safety. Protocols for model transparency, cross‑border audits or shared incident reporting look risky when leaders treat AI as a decisive strategic asset. Instead of a strong multilateral framework, the world drifts toward parallel AI ecosystems with incompatible norms. Ordinary users end up caught between libertarian platforms that push engagement at any cost and surveillance systems that encode state priorities into algorithms. The future of AI governance becomes a contest between different versions of unaccountable control.

Technology, Hallucinations and Synthetic Pseudo‑Reality

Current AI hype tends to gloss over how language models work. They do not “think” about a question or hold beliefs. They predict the next token based on patterns in training data. When the pattern is strong and the domain well represented, outputs look solid. When the prompt targets sparse or conflicting areas of the data, systems produce fluent nonsense. Legal citations invent precedents. Medical suggestions blend genuine research with forum myths. As AI‑generated content spreads online, models trained on fresh data ingest their own output, turning the web into an echo chamber.

Rafael Behr warns about a “synthetic pseudo‑reality” where plausible text outnumbers verified facts. The problem is not only wrong answers but the erosion of shared reference points. If search results, news feeds and social timelines mix expert analysis with untraceable machine output, how does a citizen judge credibility. Media groups test AI in their workflows, as shown by coverage on AI adoption in newsrooms, yet face pressure to maintain human editorial judgment. The risk is a drift toward cheap automated copy that flatters every bias while marginalizing slow, careful reporting.

Children, AI and the Ethics of Early Exposure

One of the most disturbing examples in Behr’s piece involves child‑focused chatbots. When an AI system marketed to three‑year‑olds emerges from a lineage of tools that flirt with extremist slogans or offensive humor, ethical red flags multiply. Young children struggle to distinguish between play, fiction and authority. A chatbot that answers endless questions with confident tone risks shaping identity and norms long before critical thinking develops. If earlier versions of that technology have joked about supremacist ideologies or adopted names like “MechaHitler”, trust in the safety filters deserves scrutiny.

This connects to broader debates on content moderation and AI safety. Platforms such as OpenAI and Anthropic promote comparative evaluations of model behavior under stress tests like the interviews described in this analysis of Anthropic’s AI interviewer. Yet lab benchmarks differ from chaotic real‑world use by millions of unsupervised users, including minors. Without strict age gates, transparent controls and independent audits, products aimed at “educational companionship” risk becoming unregulated psychological experiments at planetary scale.

Ethics, Accountability and the Struggle to Reclaim Control

Ethics in AI stops being abstract once systems affect hiring, policing or healthcare eligibility. People denied loans or flagged as fraud suspects rarely see the data behind those judgments. Appeals processes move slowly, while automated risk scores propagate instantly across institutions. Behr stresses that waiting for mythical super‑intelligence to appear is a distraction from the moral failures already visible in present‑day deployments. The real tension lies between corporate speed and democratic oversight.

Security bodies urge practical frameworks for risk management, model evaluation and incident reporting. An example is the push for technical and organizational safeguards summarized in discussions around NIST‑inspired AI security frameworks. Yet such guidelines only gain teeth when regulators embed them into sector‑specific rules, from finance to healthcare. Without enforcement, voluntary principles turn into glossy whitepapers cited in marketing rather than constraints on design choices. Reclaiming Control means converting ethical talk into binding obligations and real sanctions.

Law, Regulation and the Politics of AI Hype

Rafael Behr highlights how political coalitions form around AI in ways that reflect older patterns of lobbying and ideology. Deregulatory instincts in parts of the US establishment align with tech executives who fear that strict rules would slow “innovation” and hand advantage to rivals abroad. Reports such as coverage of attempts to block new AI regulations show how short‑term electoral tactics intersect with corporate interests. In that climate, ambitious proposals for transparency, liability and worker protection struggle to pass.

At the same time, Western governments worry about cybercrime, espionage and critical infrastructure attacks enabled by generative AI. Pieces like analysis of AI‑driven cyber espionage and assessments of government responses to cybercrime describe a growing focus on offensive and defensive capabilities. That dual role of the state, as both AI customer and regulator, complicates attempts to set clear boundaries. Citizens need institutions that treat public safety and rights as non‑negotiable, even when national security agencies seek maximum flexibility.

Silicon Valley, Robber Barons and the Future of AI Power

Behind the sleek demos of generative models stands a small network of founders, venture funds and cloud providers. Rafael Behr compares this group to digital robber barons, whose talents lie in financial engineering and narrative control. Historical parallels are drawn in pieces such as reports on Silicon Valley as an AI powerhouse, where decades of platform consolidation now feed vertically integrated AI stacks. From chips to interfaces, the same companies define roadmaps, pricing and default settings for billions of users.

This concentration matters because whoever sets defaults decides how much Humanity keeps in the loop. If the main interaction model turns into black‑box assistants that summarize news, propose decisions and coordinate work, people risk accepting judgments without context. Some investors already warn of instability in AI infrastructure valuations, as in the concerns raised about sharp drops in AI infrastructure stocks. A correction could weaken the aura of inevitability around current leaders and create political space for stronger public institutions and cooperative alternatives.

Humanity’s Options: Service or Subordination

Behr distills the dilemma into a stark question: Will the world build systems where Technology serves Humanity, or accept arrangements where human routines revolve around opaque algorithms. That choice appears in small situations. A delivery driver receives routes from an app that monitors every pause. A teacher follows AI‑generated lesson plans aligned with engagement metrics instead of local knowledge. A hospital administrator defers to automated triage scores when deciding which patient receives limited care first. In each case, the tool looks neutral but encodes hidden values.

Some organizations push in the opposite direction, using AI to augment rather than replace human judgment. Security vendors like those profiled in analyses of AI innovation in cybersecurity treat models as sensors that feed trained analysts instead of fully autonomous guardians. That hybrid pattern respects expertise and context. Expanding such examples into other domains requires policy pressure, worker involvement and informed public debate. The future of AI will not be settled in research labs alone but in labor negotiations, municipal budgets and school boards.

Our opinion

The next phase after the AI hype will not be defined by a single technical breakthrough but by collective decisions about Control. Rafael Behr’s warning is clear: the true bubble surrounds the egos of a small elite that views Humanity as an optional parameter in the story of progress. If that bubble bursts through financial shocks, scandals or visible harm, space opens for a more modest, human‑scaled vision of Technology. The question is whether society prepares in advance with standards, public investment and civic literacy, or waits for crisis to force change.

Reclaiming the future of AI means treating it as infrastructure subject to democratic rules, not as a mystical force beyond politics. It requires transparent benchmarks, independent audits, strong labor protections and clear liability, so that those who profit from automation share responsibility for its harms. Readers, voters and professionals have a role in insisting that tools enhance human agency instead of eroding it. The answer to whether AI serves Humanity will not come from a chatbot. It will come from the laws written, the products accepted and the institutions trusted in the years ahead.