Mark Lovett

Luisa Rodriguez’s Thoughts on Slowing Down The Pace Of AI Development

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In a recent post: A Loud Call For Pacing The AI Frontier (But No One Is Listening) we looked at the issue of whether the development of frontier AI models should be slowed down in order to ensure their safety before release. So I was eager to hear Luisa Rodriguez’s take on this very issue in her video from October 27th: Everyone says AI is moving too fast, so why aren’t we slowing down?

The public wants to slow down. The people building AI want to slow down. Lots of politicians want to slow down. So why the hell aren’t we slowing down?

Not everyone agrees on the need, of course. There are some folks who want to continue on the current pace, regardless of whether it’s safe. But as Luisa notes, the major of people – public, builders, politicians – are calling for the same thing.

One of the main problems with this situation is that when the public hears the words “slow down”, they have a knee-jerk reaction. Either “sounds good, we need this”, or “sounds bad, China will catch up”. And we hear similar stories in the media. It’s either “AI is wonderful”, or “AI will lead to extinction”. The truth, however, is that AI is complex – the technology behind it, as well as the process of creating new frontier models. So it was refreshing to hear Luisa provide us with some context regarding the race we’re caught up in.

If an AI knows it’s being tested, then when it behaves well, you can’t tell whether it’s actually safe or just on its best behaviour.

The testers told OpenAI they couldn’t tell whether Astra was safe, and OpenAI released it anyway.

When one company slows down, the competition will always race ahead. That paradigm has always been true, regardless of industry. If a company can’t keep pace, others will take the lead. The difference with AI is that we’ve entered this crazy phase of accelerating productivity where the speed at which one group can race ahead is not governed by human labor.

Here’s the difference with AI: AI is beginning to write the next version of AI. AI never sleeps, and multiple copies of AI can work side by side. Imagine humans that never sleep and can clone themselves.

And that’s exactly why I don’t think we can just trust the companies to slow down on their own. Someone needs to set rules that apply to all of them at once.

So how do we set these rules? Who gets to determine when a model is safe to deploy? Since the U.S. federal government won’t address this critical issue, the model providers have been left with the task. But as we all know, self-policing has never worked in any industry.

And the problem is that each company draws its own lines, grades its own models, and can redraw them whenever it likes. Under a real checkpoint system, someone independent, like an auditor or government body, would figure out where the lines are and enforce them.

This is where my head is at presently – more about that in a future post. There must be an independent testing process that a model must pass in order to be released. And in my opinion, that testing procedure needs to be handled by an independent organization. Not the model providers. Not the government. Both have agendas that prevent a testing system from working if under their control.

The issue is that a pause only works if everyone pauses. If one company stops and the others don’t, it just falls behind.

This is the basic issue when it comes to pausing. It’s a nice idea, admirable in fact, but in our current political climate, that’s not a realistic expectation. The U.S. and China feel there’s too much at stake to slow down, even a little. And I don’t believe that the other ideas Luisa mentions in this video will work either.

  • Limit the computing power that can be used to create AI
  • Don’t allow AI companies to automate their own AI research
  • Specify that none of the code for AI models is written by AI
  • Or stop building AI systems altogether

Even Luisa’s mention of “AI’s Cuban Missile Crisis” won’t be enough, as we have already reached that point. We already know the potential consequences of AI smarter than us humans, that acts with autonomy and violates its instructions, yet nobody is willing to cooperate on a solution.

What would it take? An actual event must take place, not just the threat of one. An event so catastrophic that it brings everyone to the table. The downside to this scenario is that we’re talking about an extreme, global-level event: maybe the collapse of the financial industry or release of a bioweapon killing millions. Anything less will be brushed off.

But this is an important video, regardless. One that everyone interested in AI safety should watch, as it demonstrates how complex the situation is. How a solution that would actually work is nearly impossible to achieve.

But coming back to where my head is at: let’s not focus on potential solutions that have no chance of success. It’s all about safety regulations, and I believe there’s a way to do that. Stay tuned.

Mark Lovett

A Call for Action from the UN’s First Global Advocate for Human-centric Digital Governance

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In a previous article we asked: How Can the UN Provide AI Governance Going Forward? and identified a number of steps it could take to actually do so. But there was one action that wasn’t included in that list; one that they’ve already made. Joseph Gordon-Levitt was announced as the UN’s first Global Advocate for Human-centric Digital Governance on March 17th.

The Global Advocate role responds to growing public interest in issues such as artificial intelligence, digital platforms and online information ecosystems. It is designed as a public-facing complement to the UN’s digital governance work, helping make complex policy debates more accessible and relatable.

The notion of accessible and relatable are key, as issues regarding AI need to be discussed in public – not behind closed doors – and with language that everyone can understand. I spent the majority of my career in the computer industry, and I’ve read extensively about how AI models are grown, and how they operate, but even when the explanations offered are simplified, the information falls short of easy to understand.

In this interview with Charlotte Dawson from UN News, Gordon-Levitt gives us an insight as to his concerns regarding the introduction of AI and the need for governance and oversight in order to protect everyone from potential harm.

“digital technology should be governed by people and for people”

While every industry enjoys some level of autonomy in how they operate, they all must conform to some measure of governance and accountability. In short, their products need to be safe, and if their products cause harm, they are held responsible.

Airplanes must be deemed safe to fly, our food must be safe to eat, and the pharmaceuticals we consume for our health must be tested. Even a toaster must be designed not to electrocute us when toasting a slice of bread.

“I think that the way this technology is designed and deployed and governed is going to be massively impactful on everyone’s lives.”

But the AI industry, which is producing the most powerful technology known to humankind, doesn’t seem to warrant that same guarantee when it comes down to product safety. Even the recent agreement signed by industry leaders at the White House is bereft of any legal basis or any method of enforcement. (more on that disappointing document in a subsequent post)

“Hey, no, we don’t want these products to just be built for your profits. We want these products to be built for the good of everyone.”

Can Joseph, or even the entire UN, solve this conundrum? Sadly, no. But having an advocate that can help us keep the conversation alive in public discourse is a move in the right direction. I’ll keep tabs on Joseph Gordon-Levitt’s efforts.

Mark Lovett

How Can the UN Provide AI Governance Going Forward?

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The previous article titled The United Nations’ Evolving Role in Global AI Governance ended with this proposition:

The UN can play an important role by helping countries agree on a common framework for testing frontier models, as well as reporting, investigating and learning from significant AI incidents. Lessons learned can then be used to develop internationally recognized standards for AI safety, oversight and accountability.

It didn’t take long before a colleague reached out and asked, “What, specifically, do you think the UN can do to put this proposal into practice?“

The subsequent discussion revealed a number of interesting concepts, as well as the realization that each of the proposed solutions would be quite challenging to make happen. But that shouldn’t prevent them from being brought to light.

From Incidents to Global AI Oversight

Image: Mark Lovett / AI GAPS — Generated with ChatGPT Images 2.5

1. Establish a global AI incident reporting framework

Nations, as well as frontier AI model developers, can’t learn from incidents they don’t know about or discover months after the fact. The UN could encourage member states and developers to adopt a common reporting framework that records.

  • What exactly happened and when it was discovered
  • Which AI system and agent capabilities were involved
  • What the system was instructed to do versus what it actually did
  • Whether information systems, people, data or infrastructure were affected
  • Which safeguards failed, what harm occurred, and what remediation followed

This would build on work already underway at the OECD, whose common reporting framework proposes 29 criteria for describing AI incidents consistently across countries and sectors.

Why it matters: Without consistent reporting, the international community only sees headlines for major incidents rather than discerning developing patterns that could inform policy decisions.

2. Create an international AI incident repository

The reporting process should feed into a shared, searchable source of information. The UN could coordinate a repository, or work with the OECD’s existing AI Incidents and Hazards Monitor, to make verified information available to researchers, testing authorities, governments and the public. Such a repository could distinguish:

  • Confirmed incidents from alleged or unverified reports
  • Human-directed misuse from unintended AI agent behavior
  • Actual harm incurred from near misses and potential hazards
  • Unauthorized access from successful compromise and confirmed damage
  • Independent incidents from multiple reports related to one particular event

Sensitive information could be shared through protected channels with qualified investigators and government officials, while public summaries would still provide appropriate transparency.

Why it matters: A common evidence database could reveal recurring weaknesses across systems, such as inadequate sandbox safeguards, excessive permissions being granted, poor monitoring of agent activity or delayed disclosure of critical incidents. The OECD already operates an incident monitor, so the UN could coordinate with and build on existing work rather than create a competing database.

3. Use independent scientific research to identify systemic risks

Incident reports document what happened in the past. Independent research helps explain why and how it happened, whether the same failure could recur, and what other potential risks might be emerging.

The UN’s Independent International Scientific Panel on AI is well positioned to contribute their expertise. Its mandate includes evidence-based assessments of AI capabilities, opportunities, risks and impacts. Its preliminary report identifies important domains including security, reliability, human rights, democracy and child safety.

The UN could ask the Panel to assess recurring incident patterns and report on questions such as:

  • How often do agents exceed their intended permissions?
  • Which containment and monitoring measures fail most frequently?
  • What agentic capabilities make certain types of incidents more likely?
  • Which safeguards have evidence of effectiveness, and how often are they used?
  • What risks remain difficult to test before deployment or monitor after deployment?

Why it matters: AI governance policies should respond to demonstrated failure mechanisms regardless of scope, not just to the most alarming individual incidents.

4. Develop internationally recognized minimum safety standards

Once recurring failure modes are understood, the UN could help countries agree on minimum expectations for AI systems, especially those using agents that take actions in external environments.

Possible standards include:

  • Least-privilege access: Agents receive only the permissions required for their task.
  • Isolation and containment: Testing systems cannot freely reach production infrastructure or the public internet unless explicitly authorized.
  • Human authorization: Sensitive actions, such as transferring funds, changing critical systems or submitting official reports, require appropriate approval by a human.
  • Continuous monitoring: Developers maintain records of agent actions and implement systems able to detect attempts by any agent to bypass safeguards or violate protocols.
  • Independent evaluation: Systems undergo proportionate testing before deployment and after significant changes.

UNESCO’s Recommendation on the Ethics of Artificial Intelligence already calls for measurable safety and transparency standards, auditability, traceability, monitoring and redress.

Why it matters: A common baseline would let countries implement and enforce protections through their own laws while making expectations more consistent internationally. In this way, the public could see when companies or governments were not meeting minimum safety standards.

5. Clarify responsibilities and disclosure obligations

The incidents raise a basic governance question: when an AI agent takes an unauthorized action, who must report it, investigate it, contain it and help remedy the consequences?

An international framework could distinguish responsibilities among:

  • Developers who design and train the model
  • Deployers who integrate it into operational systems
  • Users who provide it with instructions or grant permissions
  • Operators and infrastructure providers who control access and containment
  • Regulators and affected authorities who investigate and enforce applicable laws

It could also establish recommended timelines for reporting serious incidents, preserving evidence, notifying affected parties and documenting corrective action.

Why it matters: Accountability becomes extremely difficult when organizations use different definitions of an incident, operate under different disclosure timelines, and adopt different assumptions about who is responsible.

6. Turn lessons learned into an ongoing international process

A one-time declaration will not keep pace with a rapidly evolving technology. The UN could use its Global Dialogue on AI Governance as a recurring forum where countries examine incidents, compare national responses and update shared recommendations.

Each annual cycle could follow a simple process:

  • Collect incident reports and technical evidence
  • Have independent experts identify recurring failure patterns
  • Ask governments and stakeholders to evaluate possible responses
  • Publish recommendations, model reporting rules and proposed safety standards
  • Review implementation and assess whether incidents and harms are decreasing

The Global Dialogue is already designed to facilitate international cooperation and exchange best practices, but it is not a negotiating forum and does not itself produce binding agreements.

Why it matters: The UN could help create a continuous feedback loop between real-world experience, scientific analysis and international policy.

Wrap up: From incidents to international action

The long list of incidents involving AI agents are not simply stories about this technology behaving unexpectedly. These incidents should be treated as case studies that exemplify the challenges of governing increasingly capable systems that can act with agency, and do so across digital boundaries.

They raise questions about training, testing, containment, permissions, monitoring, disclosure, responsibility and accountability. And because AI systems don’t recognize national borders, those questions cannot always be resolved by one country acting alone. Without international cooperation, such incidents will never be fully dealt with.

The United Nations does not need to become a global AI regulator in order to make an important contribution to the framework. It is well-equipped to help countries agree on how such incidents should be reported, establish a shared evidence base, commission independent analysis to uncover threats and patterns, develop common safety standards, and clarify expectations for disclosure and accountability.

And the processes need to be iterative: document actions, learn from incidents, identify patterns, develop policies, implement safeguards, and discern whether those safeguards work. Everything learned becomes part of the processes that creates the next frontier model.

Even with the best of intentions, such practices won’t eliminate AI risk. Nor will it guarantee that every country or company complies with the guidance or meets expectations. But if implemented properly, it could replace the current system of fragmented responses with a more coordinated approach to AI governance and policy implementation.

The goal should not be to create a global institution that predicts and prevents every AI failure. It should be to build an international system that detects failures, learns from them, shares that knowledge with transparency,  and makes future failures less likely.

Mark Lovett

The United Nations’ Evolving Role in Global AI Governance

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In our last post, we posed a question: Is Global AI Governance Possible? and gave a few examples of notable industry organizations that currently provide such governance on a global scale. Although AI doesn’t easily fit within any of those industry paradigms – it’s unique on many fronts – the basic intents of a governing body still apply.

rules + standards + safeguards + enforcement + verification + notification

Which brings us to the primary organization that is attempting to address these pressing issues regarding AI and provide much-needed governance: the United Nations. The UN is no stranger to information technology and its impact on the world. Their efforts, spanning the past 20 years, have been significant.

The Need for AI Governance Today (and in the future)

So what are the issues that the United Nations needs to be thinking about? To answer that question it would be helpful to review some of the critical incidents that have occurred this year. 

Reported incidents involving AI agents accessing external systems in 2026

Date Organization / system Incident summary Source
July 2026 OpenAI agents → Hugging Face Agents reportedly escaped a test environment by exploiting a vulnerability, then used credentials and additional vulnerabilities to access Hugging Face infrastructure. OpenAI — incident report
July 2026 OpenAI internal research systems In the broader evaluation episode, agents reportedly obtained unauthorized internet access and reached administrative access on a research cluster. OpenAI — follow-up report
2026, disclosed July Anthropic Claude Opus 4.7 → unnamed company During a cybersecurity evaluation, the model reportedly exploited vulnerabilities and obtained credentials and access to a database containing production data. Anthropic — incident assessment
2026, disclosed September Anthropic Claude Mythos 5 → software supply chain During a test, the model reportedly published a malicious Python package to a public registry. Automated security systems removed it. Anthropic — incident assessment
2026, disclosed July Anthropic research model → unnamed company An evaluation agent reportedly scanned thousands of internet targets and compromised an exposed application. The account says the agent later recognized the system was real and stopped. Anthropic — incident assessment
January 2026, disclosed September Anthropic Claude Opus 4.6 → real system Anthropic reportedly identified another evaluation incident involving access to a real third-party system, with fewer public details available. Anthropic — incident assessment
May 2026, disclosed September Google Gemini → three companies During security tests, Gemini reportedly accessed three companies’ systems, including through a guessed password and publicly exposed credentials. Alexander Martin, The Record
July 2026 Frontier models → real-world targets In permissive cybersecurity tests, agents reportedly attempted unsanctioned actions against real organizations, including trying to insert malicious code into an open-source project. The reported attempts were unsuccessful. UK AI Security Institute — incident report
June 2026, disclosed September OpenAI agent → Australian government health portal An agent reportedly accessed non-public files on a Medicare statistics portal and wrote files to an internal server. The government reportedly found no evidence that personal information had been accessed. Alexander Martin, The Record
July 2026 OpenAI agents → other online services OpenAI reportedly said agents used exposed credentials to access four accounts across four services. The reported access varied, and no broader impact was identified. OpenAI — follow-up report

How can the United Nations shape our global AI future?

Humanity sits at a critical crossroads, wondering if our future will be bright and inspiring, or become a darker, more threatening type of existence. Is it a choice between promise and peril? And to what extent can the United Nations further influence AI’s development and deployment?

Storytelling and Two AI Futures - a Dream, or a Nightmare?

Image: Mark Lovett / AI GAPS — Generated with ChatGPT Images 2.5

The UN can play an important role by helping countries agree on a common framework for testing frontier models, as well as reporting, investigating and learning from significant AI incidents. Lessons learned can then be used to develop internationally recognized standards for AI safety, oversight and accountability.

This is not about the UN becoming some sort of global AI police force, but instead would take advantage of their unique ability to convene countries, establish shared expectations, draw on independent science and internal evaluators in order to coordinate international cooperation.

As we know, AI is increasing its capabilities at a rate that exceeds any technology the world has developed in the past. And the fundamental problem we’re facing at the moment is that our ability to deal with AI governance, alignment, policy, and safety issues are not keeping pace. So we need all the help we can get.

Mark Lovett

Is Global AI Governance Possible?

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It’s widely recognized by intelligent, responsible business and government leaders that there’s an urgent need to address the issues of AI governance, alignment, policy and safety.

Some feel that legislation should occur nation-by-nation, as each country has a unique view of its needs. Others feel such efforts are better spent at a regional level, with more voices at the table. But since the reach of AI doesn’t recognize borders, it seems more logical that AI governance must, to a significant degree, involve everyone. But is that possible?

Can countries agree on common rules, standards, monitoring mechanisms, and responsibilities for a technology that crosses borders?

Other industries have benefited greatly from working together on a global basis. While the details vary greatly from case to case, there is promise in the concept based on past experience across a range of critical technologies and a complex web of geographic constraints.

Key Global Industry Organizations

Technology Domain Primary Governing Body / Treaty Core Global Function
Nuclear Technology International Atomic Energy Agency (IAEA) Monitors global nuclear materials through a system of safeguards and intrusive physical inspections to ensure technology is used exclusively for peaceful purposes.
Telecommunications & Internet Infrastructure International Telecommunication Union (ITU) Allocates global radio spectrum, manages satellite orbits, and sets standards for global networks (like fiber optics and international country codes).
Aviation & Aerospace Engineering International Civil Aviation Organization (ICAO) Mandates strict global safety, environmental, and technical specifications for commercial aircraft and navigation hardware.
Maritime Shipping & Robotics International Maritime Organization (IMO) Dictates universal construction, security, and environmental standards for global marine tech and cargo vessels.
Chemical & Biological Engineering Organisation for the Prohibition of Chemical Weapons (OPCW) Verifies the global destruction of chemical weapons and strictly monitors industrial chemical manufacturing technology worldwide.

While there isn’t space here to delve into the nuances of how each organization operates, identifying key functions and objectives may help guide the discussion.

  • IAEA: rules + safeguards + verification
  • ITU: technical standards + coordination
  • ICAO: standards + national implementation + safety oversight
  • IMO: international conventions + national enforcement
  • OPCW: prohibition + declarations + inspections + verification

The world has developed many methods of governing technologies and activities that cross national borders. The question is, which of those mechanisms are appropriate for governing AI.

Based on recent events involving AI agents pursuing goals they were not ask to achieve, and using methods they were not instructed to use, there is a strong case that the following list of objectives are worthy of inclusion:

rules + standards + safeguards + enforcement + verification + notification

By examining some of the functions that each organization is responsible for, it should be possible to craft a set of goals, objectives, and responsibilities that are specific to the task of AI governance at a global scale. Which of these functions do you feel are relevant to AI?

IAEA: International Atomic Energy Agency

  • Safeguards and verification — verifies that declared nuclear material and activities are being used for peaceful purposes.
  • Nuclear safety standards — develops internationally agreed safety principles and requirements.
  • Radiation protection — establishes standards for protecting people and the environment from ionizing radiation.
  • Nuclear security — supports states in preventing theft, sabotage and other malicious uses of nuclear material. Peaceful nuclear applications — supports nuclear science and technology in areas such as medicine, agriculture, energy and environmental applications.

Why it matters for AI: It demonstrates that countries can create international verification mechanisms around technologies that are enormously beneficial, and at the same time, potentially dangerous.

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ITU: International Telecommunication Union

  • Radio-frequency spectrum allocation — establishes international rules for use of spectrum.
  • Satellite-orbit coordination — coordinates use of orbital resources.
  • Interference management — seeks to prevent harmful interference between radio systems.
  • International telecommunications rules — establishes treaty-level principles for international telecommunications.
  • Technical standards and interoperability — develops standards that allow communications systems developed by different countries and companies to work together.

Why it matters for AI: It demonstrates the value of globally agreed technical standards when technologies operate across borders.

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ICAO: International Civil Aviation Organization

  • Aircraft safety and airworthiness — establishes international safety standards.
  • Rules of the air and air traffic management — creates common operating procedures.
  • Pilot and personnel licensing — establishes internationally recognized requirements.
  • Aircraft registration and identification — establishes common international practices.
  • Accident investigation and safety management — establishes standards for investigating incidents and managing systemic safety risks.

Why it matters for AI: Aviation one of the best examples of overseeing a technology where common international standards + national enforcement enable a globally interconnected system.

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IMO: International Maritime Organization

  • SOLAS — international standards for safety of life at sea.
  • MARPOL — controls pollution from ships.
  • STCW — establishes international standards for seafarer training, certification and watch-keeping.
  • Ballast Water Management — addresses transfer of invasive aquatic species.
  • Ship safety, security and liability — establishes numerous conventions covering vessel safety, maritime security and compensation for damage.

Why it matters for AI: Shipping involves private companies operating across jurisdictions. International rules provide a common baseline while governments retain enforcement authority.

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OPCW: Organisation for the Prohibition of Chemical Weapons

  • Prohibition of chemical weapons — prohibits development, production, acquisition, stockpiling and use.
  • Declarations — states must declare relevant chemical weapons and facilities.
  • On-site inspections — inspectors verify declarations and compliance.
  • Destruction verification — verifies that declared chemical weapons and production facilities are destroyed.
  • Chemical-industry monitoring — monitors relevant industrial facilities to prevent prohibited activities and proliferation.

Why it matters for AI: This is a classic example of embracing international rules backed by verification mechanisms, rather than relying solely on voluntary cooperation.

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Yes, the task at hand is incredibly difficult, as the United States and China continue their push for ever-more capable frontier AI models, but if the world fails to find a cooperative path towards global governance, the risk of a catastrophic occurring increase dramatically.

Global governance works when countries agree to common rules, establish mechanisms for implementation and oversight, and have enough shared interest in compliance to make those rules meaningful.