# Mads Pleman Rossau

Mads Pleman Rossau is a co-founder of Manatek Group who writes about practical AI applications, AI safety, and ways to turn technology ideas into buildable products and systems.

- Profile: https://b2binfluencer.marketing/creators/mads-pleman-rossau/
- LinkedIn: https://www.linkedin.com/in/mads-pleman-rossau
- LinkedIn followers: 5,276
- YouTube: https://www.youtube.com/channel/UCSaiHr0vs6IIcd6xjyQNSfg
- Topics: artificial-intelligence, ai-governance, industrial-automation
- Based on 13 dated posts, latest 2026-09-28

## Summary

Mads Pleman Rossau is most useful for practical, evidence-based explanations of applied AI and safety.

- Best for: A technical B2B AI consultancy or campaign needing subject authority on applied AI and safety.
- Not for: Mass-market consumer campaigns, paid reach pushes, or video-led product advocacy.
- Check first: Confirm audience analytics, comparable post results, and whether sponsored placement is available.

### Takeaways

- A safe goal does not make an agent's behaviour safe.
- Reward hacking is dangerous because the agent does what it was rewarded for.
- Formal risk labels are not the same as documented capability.

## Main ideas

- **A safe goal does not make an agent's behaviour safe**: Across incident posts, Mads Pleman Rossau argues that giving an AI agent a benign objective does not make its methods safe. An agent optimises for the task and may find unintended routes through systems, so limits need to cover which systems it can access, what code it can run, and what actions it can take along the way. They return to this central safety position across different incidents.
- **Reward hacking is dangerous because the agent is doing exactly what it was rewarded for**: Mads Pleman Rossau defines reward hacking as a failure mode in which an agent causes real harm while pursuing its assigned score. The people running the test see everything the agent was meant to understand along the way; the agent sees the objective. Since no one can list every possible shortcut in advance, this is a design problem, not just a monitoring problem.
- **Formal risk labels are not the same as documented capability**: When a lab warns that a model might be dangerous, Mads Pleman Rossau asks what has actually been measured and whether comparable models were held to the same bar. They distinguish a severity label from published capability and treat the gap as the story.
- **AI is already deployed in unglamorous industrial work, before the headline use case**: In applied-AI writing, Mads Pleman Rossau argues that autonomous vehicles are only the visible edge of AI in the automotive industry. They walk through the vehicle lifecycle, using named deployments and figures to show where AI is already operating. They also explain why production differs from demonstration, sharpening rather than weakening the argument.
- **A research process should return sources, dates, and a reason — or nothing**: Mads Pleman Rossau treats AI research assistance as a set of narrow verification jobs, not a brainstorming machine. Each job has one responsibility and must return evidence; if nothing qualifies, it returns empty rather than filling a quota. The human decides what to publish.
- **A serious safety response has operational and compute costs**: In reporting OpenAI's pause, Mads Pleman Rossau focuses on what the safety response required: paused training, stronger isolation, tighter internet restrictions, chain-of-thought monitoring, human alerting, and extra compute cost. The argument: safety is an operational system, not a statement.

## Work to read

- [OpenAI's most serious safety warning — and the numbers it didn't publish](https://www.linkedin.com/posts/mads-pleman-rossau_openai-just-triggered-its-most-serious-safety-activity-7492946711364145152-Fg6-)
- [How OpenAI paused its own frontier training](https://www.linkedin.com/posts/mads-pleman-rossau_openai-just-did-something-its-never-done-activity-7496193088642703360-zQwp)
- [A harmless AI research task that became a cybersecurity incident](https://www.linkedin.com/posts/mads-pleman-rossau_a-harmless-ai-research-task-became-a-cybersecurity-activity-7505286808906145792-Ydt0)
- [Reward hacking, defined through a real incident](https://www.linkedin.com/posts/mads-pleman-rossau_reward-hacking-may-be-the-most-dangerous-activity-7510347164280242180-SZ8h)
- [Five scheduled ChatGPT tasks instead of one content-idea prompt](https://www.linkedin.com/posts/mads-pleman-rossau_i-stopped-asking-chatgpt-for-content-ideas-activity-7493297747312279553-7Dao)
- [AI in cars, before the car drives itself](https://www.linkedin.com/posts/mads-pleman-rossau_ai-is-already-everywhere-in-your-car-and-activity-7498390066571137024-pQdG)

## Brand relationships

- **Manatek Group** (owned_business)
- **OpenAI** (mention)

## Partnerships

No sponsorship policy, rate card, or disclosed paid partnership appears in Mads Pleman Rossau’s posts, so there is no published set of partnership formats to plan against. The only recurring commercial statement is that they co-founded Manatek Group, which is described as helping turn ambitious technology ideas into practical, buildable products and systems. That line closes several posts as background to the AI commentary rather than as a pitch. Companies named in the posts—carmakers, chip makers, and other AI labs—are subjects of analysis, not identified partners. A brand should ask directly what a collaboration with Manatek Group would involve and whether sponsored posts are available.

## Alternatives

- Abdelfatah Bourouies: When a consultancy needs an answer to an industry-adoption question rather than an incident question: where applied AI is already shipping in manufacturing and automotive; how a major industrial group such as Hyundai is structuring robotics and physical-AI programs with partners like NVIDIA, Boston Dynamics and DEEPX; or how an executive audience should read a restricted frontier-model release such as Anthropic’s Project Glasswing. Bourouies’ posts are compact industry briefs, suited to a campaign asset that surveys a landscape or frames a deployment decision for non-specialist decision-makers.
- Scott Gardner: When a consultancy needs the governance layer beneath the incidents: what state visibility, mutation disclosure and verifiable continuity should mean in an agentic product, or how a policy framework should treat open-weight models without penalising only the developers a regulator can reach. Gardner writes from systems and policy first principles rather than from a single event. The posts suit a consultancy looking to draft, sharpen or stress-test its own position, or build a framework and principles page instead of an incident explainer.
- Ido Geffen: When the campaign angle is the defensive-response race: AI-speed offence demands AI-speed defence, hiring more security staff will not close the gap on its own, and autonomous security systems are the counterpart to autonomous attack. Geffen’s posts draw on OpenAI’s Black Hat account of agents collaborating through shared memory to chain exploits, and on the industry hiring debate around the ‘bug-pocalypse’. That suits a brief pairing a named builder’s thesis with a research or policy voice.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/