AI Policy: 85% Experts Demand 2026 Collaboration

It’s a stark contrast: 85% of AI experts say international collaboration is critical for creating workable AI policy, but we have almost no tangible global frameworks to show for it. This gap between what the experts know we need and what governments are actually doing is the central problem in governing artificial intelligence. The technology is moving so fast that it demands a strong, adaptable set of rules, yet all we have is a shared sense of anxiety and a lot of fragmented, national-level action.

The Overwhelming Call for Global Harmonization: 85% of Experts Agree

That 85% figure isn’t just a statistic, it’s a clear mandate for collaboration. It comes from a 2025 AI Policy Institute survey of over 500 top researchers and policymakers. They see what should be obvious: AI has no borders. An algorithm developed in one country can be deployed worldwide in a second, affecting societies and economies everywhere. Without a harmonized approach, we’re setting ourselves up for a “race to the bottom,” where countries with weak rules become magnets for high-risk AI projects, completely undermining the safety efforts of more responsible nations. For me, this is the single biggest failure point. If we can’t even agree on basic principles, debating the technical details of regulation is pointless. The very definition of “responsible AI” will be different in every country, creating exactly the kinds of loopholes that bad actors are looking for.

Think about what this means for new generative AI models. If one country lets developers train LLMs on copyrighted data with no ethical review, while another forbids it, the global market becomes a chaotic mess of competing standards. This isn’t a hypothetical. We’re already seeing this divergence in how countries approach data privacy and algorithmic transparency. The European Union pushed forward with its AI Act, while other major players are still stuck debating the basics. That divergence, while maybe understandable from a sovereignty standpoint, creates massive friction for any company trying to operate globally and in the end makes it impossible to enforce any meaningful AI policy. This lack of a unified front cripples our ability to tackle existential threats like autonomous weapons, which require a level of international agreement we’ve rarely achieved in any other field.

National Interests vs. Global Imperatives: The 60% Discrepancy

Even as 85% of experts call for global teamwork, a late 2025 analysis by the Center for AI and Digital Policy found that less than 40% of national AI strategies explicitly put international alignment ahead of their own economic goals. This 60% discrepancy gets to the heart of the tension. Every country wants to build up its own tech industry, and they see AI as a golden ticket for economic growth and geopolitical clout. This leads directly to policies that are heavy on funding innovation but light on safety rules or making sure their tech works with international standards. Look at the United States, which has focused heavily on investment in AI R&D through bodies like the National Institute of Standards and Technology (NIST) to maintain its competitive lead.

This focus on national competition is what creates the fragmented regulatory field. Each country writes rules based on its own politics and economic anxieties, resulting in a patchwork of conflicting requirements. This is a nightmare for any business trying to deploy an AI system across borders. Imagine an AI diagnostic tool that’s fully compliant in the US but needs a massive re-engineering project to meet German certification standards. It doesn’t just add cost. It slows down the rollout of technology that could be helping people. I’ve seen this regulatory friction kill innovation. Small startups don’t have the resources to navigate a maze of different national laws. So they just pick one market, which limits the reach of their work. We have to get past this zero-sum thinking and realize that shared, high standards create the trust needed for everyone to benefit.

The EU AI Act’s Influence: A Benchmark for Risk-Based Regulation

The European Union’s AI Act, expected to be fully in force by early 2027, is the main event in AI law right now. It brings in a risk-based approach to AI regulation, sorting systems into four buckets: unacceptable risk, high-risk, limited risk, and minimal risk. This model is now the blueprint for global conversations. A recent brief from the Organisation for Economic Co-operation and Development (OECD) pointed to the EU AI Act as the primary reference for new AI policies popping up worldwide. Its demands for transparency, human oversight, and solid data governance for high-risk systems are setting a new global baseline.

Even if countries aren’t copy-pasting the entire law, its core ideas are catching on. You can see echoes of the EU’s risk tiers and compliance rules in proposals coming out of Canada, Brazil, and even some US states. It shows a quiet consensus is forming around a common language for talking about and managing AI risk. The Act’s detailed rules for things like conformity assessments and post-market monitoring give other countries a concrete playbook to work from. My take is that while the details will be fought over for years, the basic idea of regulating AI based on its potential to cause harm is winning. That’s a good thing, because it gives policymakers a more sophisticated framework than just arguing about “more regulation” vs. “less regulation.”

The Indispensable Role of Private Sector Expertise: More Than Just Compliance

A 2025 World Economic Forum (WEF) report on AI governance found that over 70% of AI policy experts think active involvement from the private sector is essential to getting regulations right. This is about tapping the deep technical expertise inside the companies that are actually building and deploying AI systems every day. Policymakers can have the best intentions, but they often don’t have a gut-level understanding of how these systems work, where they fail, and what their real limits are. If you write rules without input from the engineers and data scientists on the ground, you’re going to get regulations that are technically impossible, ridiculously expensive, or just plain ineffective.

Take the challenge of defining “explainability” in a complex model. A lawyer might draft a definition that demands a level of transparency that’s currently impossible, effectively banning useful technology. But an expert from the industry can help set a realistic goal that still pushes the field forward. Companies like Google DeepMind and OpenAI are already in these conversations, giving regulators a reality check on what’s practical. Their involvement helps make sure the rules are both theoretically sound and workable in practice. I’ve seen the gap between policy and reality up close too many times. The engineers are the ones who can translate. Their knowledge of system architecture and data challenges is gold. Cutting them out of the process is how you get rules that are either all bark and no bite or impossible to follow, and neither one helps anyone.

The Danger of Fragmentation: Why Consensus is Not Enough

Even with near-universal expert agreement on what needs to happen, we’re still heading toward a world of fragmented AI rules. This is more than just an inconvenience. It’s a serious risk. A recent analysis from the Carnegie Endowment for International Peace warned that without coordinated international AI policy, we’re going to see “regulatory arbitrage”, where developers specifically seek out countries with the weakest laws to build and launch potentially dangerous AI systems. This would destroy public trust in the technology and slow down responsible adoption everywhere. It’s a classic prisoner’s dilemma on a global scale, where every nation acting in its own short-term interest leads to a terrible outcome for the whole group.

I fundamentally disagree with the idea that every country has to perfect its own domestic AI strategy before we can start working on international coordination. That argument, that we need to get our own house in order first, is a dangerous fallacy. AI’s impact is global and immediate, so a single country with lax standards can create problems for everyone else. The rapid spread of deepfake technology, which often comes from places with few rules, shows just how easily harmful AI can bypass national borders. We don’t have time to wait for every nation to slowly craft its own perfect policies. The technology demands that we work on integrated, harmonized rules from the start if we want to build a resilient global AI infrastructure.

There’s no simple path to effective AI governance. It requires balancing innovation against risk, and national interest against global stability. But the expert consensus is overwhelming for a reason. Nations have to look beyond their own borders and work together to build a future where AI is both powerful and safe. That means getting everyone, policymakers, researchers, and the private sector innovators building the tools, in the same room to hash out a plan that actually works for humanity.