Can Profit-Oriented AI Companies Be the Custodians of the AI Ecosystem?

AI governance just entered a new phase.

On September 29, 2026, a group of leading AI and technology companies — Anthropic, Google, Meta, Nvidia, OpenAI and xAI among them — signed a voluntary White House AI accord on the safe development and deployment of advanced AI. The commitments cover internal controls, monitoring, external auditing and board-level oversight. It's a clear bet on industry-led safeguards over a sweeping new regulatory regime.

That is a meaningful step. Companies building increasingly capable systems are acknowledging, in writing, that technical progress needs guardrails, monitoring and accountability attached to it.

But it opens a bigger question than corporate self-regulation alone:

Can organizations whose core duty is to create shareholder value also act as custodians of an ecosystem whose stakes reach far beyond their own businesses?

This is not an argument against AI companies sitting at the governance table. Their technical expertise is irreplaceable. The real question is whether taking part in governance and being custodian of the wider ecosystem are actually the same job.

01 Self-regulation has a real role to play

The case against corporate self-regulation should not be overstated. Frontier AI labs hold technical knowledge that governments, universities, and most other institutions simply do not have. They are often the first to see emerging capabilities — and emerging risks — up close.

That gives them something genuine to contribute:

• Safety research
• Technical testing and monitoring
• Risk assessment
• Incident response
• Cybersecurity
• Operational know-how

There is precedent for this working. At the 2024 AI Seoul Summit, sixteen AI companies signed on to Frontier AI Safety Commitments covering risk thresholds, internal accountability and public transparency, and more companies have joined the list since. Voluntary industry action can clearly be a real part of the governance picture.

So the question is not whether companies should have a hand in governing their own activities — they should. The harder question is whether one company governing itself can also stand in as stewardship for an ecosystem full of interests that extend well past that company.

02 The structural limitation

A commercial AI company owes real, legitimate duties to its shareholders, investors, customers, employees, partners and its own competitive future. The broader AI ecosystem holds a lot more than that: competitors, startups, researchers, universities, governments, professional bodies, infrastructure providers, investors, everyday users, and civil society all have a stake in it.

Those interests often line up. Sometimes they don't. That's not a sign anyone is acting in bad faith — it's just what happens when institutions play different roles. A company managing the risks of its own product is operating in a different frame entirely from an institution tasked with protecting the health of the whole ecosystem.

Which gets at the core distinction:

Corporate governance manages one participant's responsibilities. Ecosystem custodianship is about the environment every participant operates in.

The distinction matters because a true custodian sometimes has to weigh questions that cut against its own members, competitors, or commercial partners — something a company governing itself was never built to do.

03 What the new accord actually shows us

The September 2026 White House accord is a good case study here. The signing companies committed to internal governance measures and outside auditing, with extra oversight layered on to strengthen safety practices around advanced AI.

The fact that it's voluntary is itself telling. Voluntary commitments can set shared expectations, push better corporate behavior, create transparency, encourage technical cooperation, and build a track record that later shapes real standards or legislation. They can also evolve — the Seoul-process commitments explicitly left room for approaches to shift as the science matured.

But self-regulation runs into the same wall every time: the company is still the one deciding how its own commitments get implemented inside its own walls. That's a reasonable setup for corporate governance. It gets a lot more complicated the moment the question shifts from:

How should this company govern itself?

to:

How should the wider AI ecosystem be protected?

The second question drags in interests that no single company can fully speak for.

04 The concentration problem

This matters more because a fairly small number of companies now sit in strategically central positions across nearly the entire AI value chain — foundation models, cloud computing, chips, data centers, applications, enterprise tools, investment, talent, and distribution, often all at once.

The U.S. Federal Trade Commission's review of major cloud-AI partnerships is a useful illustration of why that's worth watching. The FTC flagged arrangements built on heavy investment, revenue-sharing, cloud commitments, consultation or control rights, and access to sensitive technical and business information — and raised concerns about what that could mean for access to compute, access to engineering talent, switching costs and competition more broadly.

None of that means major AI companies should be shut out of governance. It makes a narrower point:

Being commercially central and being institutionally neutral are not the same thing.

The more strategically important an organization becomes, the more carefully its commercial position needs to be kept separate from any governance role it holds.

05 The representation gap

The AI ecosystem is a lot bigger than the handful of companies building frontier models. Think about how different the priorities look from a startup trying to afford compute, a university chasing research access, a professional body writing standards, an investor hunting for opportunities, a government building national capability, or a regular consumer worried about safety and privacy.

Every one of those groups needs a voice in shaping AI's future. Major AI companies deserve a voice too. But there's a real gap between having a loud voice and speaking for the ecosystem as a whole — and that gap is exactly where the custodianship problem lives.

A commercial player can fairly push for its own interests. A custodian has a different job: keeping the framework intact so that participants with conflicting interests can still coexist, interact, and build. That's why the question of who should hold the custodial role can't just be answered by asking who has the most advanced technology.

06 Why AI associations deserve a closer look

This is where digging into AI associations opened up a new angle for me. Finding organizations like UKAI turned out to matter more than I would originally have given it credit for in the Country + AI Platform research.

An AI association is built around a community of organizations and stakeholders, not around a single product. That structure lets it do things a company can't easily do: industry representation, policy engagement, knowledge-sharing, networking, skills development, and building bridges between business, government and academia.

That doesn't automatically make an association a good custodian — its membership, mandate and institutional self-interest come with their own blind spots. But its basic orientation sits closer to representing the ecosystem than a company whose whole purpose is to compete within it.

Which makes AI associations worth a lot more research attention going forward, and suggests the right custodians might already exist inside these ecosystems, rather than needing to be invented from scratch.

Toward distributed custodianship

The alternative to corporate custodianship does not have to be one new institution replacing another. A more resilient model could spread the responsibility across several participants at once: AI companies contributing technical expertise, government providing public authority, universities contributing research, associations providing ecosystem representation, professional bodies offering specialist perspective, infrastructure providers sharing technical knowledge, and an independent governance structure holding it all together with oversight.

That setup would not erase competing interests. It would just build mechanisms to balance them instead of pretending they don't exist. Which leads to a broader way of thinking about custodianship:

The custodian might end up being less an organization and more a governance architecture.

That kind of architecture would let commercial companies participate fully without putting any single one of them in charge of representing everyone else.

Government is not the automatic fallback either

There's an equally important warning on the other side of this. If corporate self-regulation has limits, handing everything to government is not automatically the fix.

Government genuinely belongs in areas like public safety, national security, competition and consumer protection. But too much government control brings its own risks: bureaucracy, political dependency, slower decisions, over-centralization, innovation bottlenecks, or turning an ecosystem gateway into a state-controlled checkpoint.

The goal is not swapping corporate control for government control. It's building balanced ecosystem governance — government setting legal and public-interest boundaries, while leaving the ecosystem itself room to innovate and let institutions participate.

USAi.gov: a potential building block for government AI coordination

One particularly interesting finding from this research is USAi.gov, a U.S. government AI platform operated by the General Services Administration. Its current role is primarily that of a shared service, giving federal agencies access to multiple AI models through a common platform while providing centralized model evaluation, API management, usage monitoring and other governance capabilities. Similar platforms exist in some other countries.

The significance of the model could, however, extend beyond technology deployment. A government-wide AI entity with an appropriately defined mandate could potentially become a coordinating layer through which agencies develop more coherent approaches to AI policy, standards, procurement, risk management and regulation — reducing the chance of fragmented or conflicting approaches emerging independently across government. Such a role would not necessarily require the entity to replace the legal authority of individual agencies; rather, it could provide a mechanism for coordination and consistency while allowing agencies to retain their statutory responsibilities.

From an AI Ecosystem Custodian perspective, an institution of this kind could become an important public-sector component of the wider custodianship ecosystem — linking government policy and regulatory institutions with the broader national AI ecosystem. USAi's present mandate should not be confused with this broader proposition, but its shared-service architecture offers an interesting example of how government could build institutional capacity for coordinated AI governance.

Participation should not require custodianship

This distinction points to a practical way forward. Major AI companies should not be pushed out of governance just because they are commercial entities — their technical knowledge is too valuable to lose. They should be able to advise, help shape standards, contribute safety research, provide technical evidence, build voluntary commitments, and help spot emerging risks.

But none of that requires exclusive custodianship. An institution can carry a powerful voice without holding the authority to define everyone else's interests for them. If anything, separating the two roles probably strengthens corporate participation — it makes everyone's lane clearer.

Conclusion: self-regulation isn't ecosystem custodianship

The real question here was never just:

Can AI companies be trusted?

That's too broad to be useful. Plenty of AI companies are investing seriously in safety, security and responsible development, and voluntary commitments can genuinely produce useful safeguards that feed into broader governance over time. The Seoul process and others already show industry commitments can set real safety norms and build transparency.

The sharper question is:

Can a commercial AI company pursue its own competitive interests while also acting as neutral custodian of an ecosystem that includes its competitors, customers, regulators, researchers and the public?

That is a question about institutional design, not about the character of any particular company.

The argument here is not that commercial AI companies should be excluded from governance. It is that corporate participation and ecosystem custodianship are two different things, and should be treated that way. The companies building frontier AI deserve a strong seat at the table — their expertise is indispensable. But custodianship needs a broader mandate, one built to hold competing commercial, public, research, professional and societal interests at once.

That is why the search for the right AI Ecosystem Custodians matters more every year. The answer might not be a corporation. It might not be government either. It might not even be a single organization at all.

It may turn out to be a distributed governance architecture, with different institutions each bringing their own expertise and accountability to the table.

That distinction may become one of the defining institutional questions as AI stops being a collection of technologies and becomes a deeply interconnected global ecosystem in its own right.