The Era of AI Super Intelligence We are witnessing artificial intelligence approach the frontier of super intelligence. Elon Musk already speaks of AI “finding new physics” and generating inventions. But what does that actually mean for users, markets and governments? Today, the newest AI models are often released to the public while still incomplete, sometimes prematurely, usually with safety mechanisms in place. These constraints are primarily designed to avoid obvious copyright violations or political disruption. But with the AGI threshold now within reach, the strategic calculus is shifting. Once artificial intelligence surpasses a certain capability level, it will no longer be just an incredible tool for automation and augmentation. It will be capable of replacing not just individual workers but entire teams, entire departments, perhaps even entire companies and improve itself (AGI). In such a landscape, releasing these models directly to the open market would be economically irrational. Giving everyone access to the most advanced AI would only empower competitors and undercut the very companies building them. Enter “The Era of the Golden Goose“ If you own a goose that lays golden eggs, why on earth would you sell it? That metaphor now applies to AGI. The companies leading the AI race are realizing this. The most powerful models will likely remain internal. These systems will be used to accelerate invention, optimize internal operations, and even found entirely new companies. Only lower-tier models, what we might call assistant-class AI, will be released to the public. These tools will still enhance productivity, but they will not rival the capabilities of true AGI. This is not just speculation. It is already starting to happen. Just look at Tesla: selling a car brings in a fixed profit, but operating that car as part of a robotaxi fleet could pay for itself in under three years. The return on investment from operating rather than selling is undeniable. The same logic applies to AI. And as this economic shift unfolds, governments are introducing policies that appear to do more harm than good. The European Union’s latest regulatory effort is a perfect example. Under the new EU AI Act, providers of general-purpose AI models are now compelled to submit public summaries of their training data using a mandatory template. This move is marketed as a push for transparency and accountability, but it is more accurately described as another bureaucratic overreach. Stifling Competition by Regulation Let me follow up on my last article “EU’s Latest AI Bureaucratic Overreach: Mandatory Templates for AI-Model Training Data Disclosure” “EU’s Latest AI Bureaucratic Overreach: Mandatory Templates for AI-Model Training Data Disclosure” Let’s look at what this actually entails. The so-called “template” spans thirteen dense pages. It demands disclosures about training datasets, along with justifications for any protected or secret content. The assumption here is that policymakers can strike a balance between openness and trade secrecy, but in practice, this becomes a lose-lose proposition. First, the administrative burden is massive. Who exactly is going to read and verify thousands of such disclosures? Are we imagining an EU task force of AI-literate bureaucrats combing through technical annexes? That seems unlikely. More plausibly, these forms will gather dust in obscure servers, while companies drown in regulatory friction. Second, the policy forces companies to expose valuable proprietary data. Firms spend millions compiling and curating training sets that reflect strategic insight. Now, they are being asked to unwrap those assets for regulators and, indirectly, for competitors. In a world where AGI becomes an internal differentiator, this is not just burdensome. It is self-defeating. Third, the idea that regulators can reliably assess the truthfulness or completeness of these submissions is optimistic at best. Verifying data provenance requires deep technical knowledge, massive resources, and consistent enforcement. Without those, the process becomes little more than a checkbox exercise, undermining its own credibility. All of this reinforces the core argument. The most powerful models will not be released. The EU will push for control through regulation, but the economic incentive to keep AGI private is stronger. Regulation that assumes all models must be public, explainable and symmetrical ignores the emerging reality: access to top-tier AI is becoming a strategic asset. Two tiers of AI will soon define the landscape. The first tier, internal and privately operated super intelligence, will drive value creation at a scale we have never seen. The second tier, publicly available but restricted in power, will help users and small companies adapt to a new technological order. This bifurcation is not just likely. It is inevitable. When the goose lays golden eggs, no one sells the goose.

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