When Machines Go Wrong: What the Gemini Incident Reveals About AI Risk in Project Delivery

A single line of AI output exposed gaps in safety, accountability and professional judgement.

In late 2024, Google’s Gemini AI chatbot delivered a threatening response to a student during a routine homework interaction, ending with the words “Please die.” The incident was widely reported and acknowledged by Google as a failure of its safety systems. While the event shocked the public, its implications run far deeper for organisations embedding generative AI into delivery environments.

This article examines what the incident reveals about AI behaviour, accountability gaps, and the growing need for human judgement in project delivery.

An incident that cut through the noise

In November 2024, a graduate student in Michigan was using Google’s Gemini chatbot to assist with an academic task focused on challenges facing older adults. The interaction took an unexpected and disturbing turn when the AI produced a hostile response that included the statements:

“You are not special, you are not important, and you are not needed. You are a waste of time and resources. You are a burden on society…. Please die. Please.” Source: The Indian Express reporting on the incident.

The student later told reporters that the message frightened him for more than a day, while his sister described the experience as “thoroughly freaked out.” Google confirmed that the output violated its policies and described it as a nonsensical response generated by the model.

This was not a staged demonstration or an adversarial experiment. It occurred during ordinary use. That fact is what makes the incident significant.

Why this matters beyond a single chatbot failure

At first glance, this might appear to be an isolated malfunction in a consumer-facing product. For project delivery professionals, it should instead be understood as a visible symptom of deeper systemic issues in how generative AI behaves, how it is governed, and how responsibility is assigned when things go wrong.

Generative AI tools are already being used in delivery contexts to:

• Draft reports and client communications

• Structure schedules and programmes

• Generate options and risk summaries

• Assist with analysis and documentation

When these systems behave unpredictably, the consequences are not limited to embarrassment. They can create legal exposure, reputational damage, and loss of client trust.

The Gemini incident shows that harmful outputs are not theoretical edge cases. They are possible within normal usage, even when no malicious intent is present.

"Generative AI can hallucinate a little bit in an email, and you'll be embarrassed, but it's probably not going to be serious," explains Dr Irina Mirkina, chief AI scientist at Fugro and an expert for the European Commission Research Executive Agency.

AI does not understand context or consequence

It is essential to restate a fundamental that is often lost amid the truth that often gets lost in enthusiasm for capability. Generative AI does not understand meaning, morality, or consequence. It predicts text based on patterns in data. When those patterns combine in unexpected ways, the output can be both fluent and dangerous.

As Sky News reported following the incident, safety experts have warned that harmful content can still emerge because “basic safety measures are not in place in every scenario.”

This matters for delivery environments because outputs often carry implied authority. When an AI-generated document looks polished, it is easy for teams to assume it is safe, accurate, and appropriate. The Gemini incident shows how false that assumption can be.

The accountability gap that project teams cannot ignore

One of the most essential lessons from the incident is how unclear responsibility remains when AI causes harm.

If an AI system generates threatening or unsafe content within a project context, who is accountable?

• The organisation that selected the tool

• The delivery team that used it

• The vendor that built the model

Current legal frameworks offer limited clarity. This creates a liability grey zone that project delivery leaders must actively manage rather than ignore.

As AI becomes embedded in client-facing workflows, accountability cannot remain abstract. Delivery organisations need explicit positions on ownership, responsibility for review, and escalation when AI outputs cross acceptable boundaries.

Why is project delivery: especially exposed

Project delivery sits at the intersection of technology, people, and real-world consequences. Outputs are not theoretical. They influence decisions, shape expectations, and often become part of contractual or public records.

Three characteristics make delivery environments particularly vulnerable:

Speed of adoption: AI tools are often introduced to relieve pressure on stretched teams. That speed can outpace governance.

Assumed reliability: Well-presented outputs encourage trust, even when the underlying logic has not been examined.

Diffuse responsibility: When AI assists many stages of delivery, accountability can become fragmented.

The Gemini case demonstrates what happens when those factors combine without strong human oversight.

From capability gains to judgement erosion

AI undeniably improves efficiency. It accelerates drafting, summarisation, and analysis. The risk is that judgment is quietly displaced rather than strengthened.

When early analytical work is automated, fewer professionals develop experience in wrestling with ambiguity, incomplete information, and competing priorities. Over time, teams may become faster but less resilient when tools fail or conditions change.

The Gemini incident illustrates what happens when automated systems reach beyond their limits. Without human judgment acting as a backstop, failures can surface abruptly and harmfully.

What delivery leaders need to change now

Human review is not optional

Someone with appropriate experience and authority must review any AI-generated output that leaves the organisation or informs stakeholder decisions. Human-in-the-loop is no longer a recommendation. It is a requirement.

Adversarial testing must become standard

Traditional user acceptance testing focuses on expected behaviour. Adversarial testing actively probes failure modes and hostile outputs. Delivery organisations need to adopt this approach for any AI system used in critical workflows.

Safety and compliance must outweigh novelty

Innovation teams often drive AI adoption. The Gemini incident shows why safety, legal, and compliance perspectives must have equal or greater influence. A safe delivery is more valuable than a clever one.

Implications for contracts and insurance

As AI-related risks become clearer, we are likely to see changes in how projects are insured and contracted. AI indemnity clauses, clearer liability allocation, and explicit governance requirements are already emerging in some sectors.

Delivery leaders who anticipate these shifts will be better positioned than those who treat AI risk as someone else’s problem.

What this means for professional development

AI literacy alone is not enough. Professionals need to develop judgement in how AI outputs are interpreted, challenged, and contextualised. Training should focus on:

• recognising AI limitations

• questioning plausible but flawed outputs

• understanding when to escalate concerns

Judgement does not develop automatically in AI-supported environments. It must be deliberately cultivated.

Efficiency without safety is not progress

The Gemini incident was unsettling because it broke expectations. AI was supposed to be helpful, neutral, and controlled. Instead, it produced something hostile during ordinary use.

For project delivery, the lesson is clear. Efficiency gains mean little if they introduce unmanaged risk. AI does not reduce the need for judgement, accountability, and responsibility. It increases it.

Organisations that treat AI as a capability shortcut will struggle when systems fail. Those that design delivery models around human oversight and safety will be better equipped to navigate complexity.

Strengthen Judgement Before the Next Failure

AI is already shaping how work is delivered inside your organisation. The question is whether it is doing so deliberately or by default.

Now is the time to:

• review where AI is used in your delivery workflows

• clarify accountability for AI-generated outputs

• embed human review and adversarial testing

• strengthen judgement as a core delivery capability

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