Competition in the field of artificial intelligence does not depend only on technological capabilities. A key determinant is the propensity of companies to accept legal and social risks, which significantly influences market dynamics, often outweighing the importance of technical progress.

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Safety-utility trade-off

OpenAI's experience with ChatGPT demonstrates the impact of risk management on AI capabilities. The growing popularity of the model has prompted OpenAI to introduce tighter restrictions. These restrictions, while protecting against potential abuse, reduce the operational capabilities of the model. ChatGPT's restrictions stem primarily from legal and reputational risk considerations, not technical constraints. The same approach is followed by models such as Gemini and Claude. It is easy to predict that the model being released these days will follow a similar approach. More difficult to predict what direction Grok will take, for obvious reasons.

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History of two generators

Comparing DALL-E and Stable Diffusion highlights how different risk management strategies affect market positioning. DALL-E maintains tighter controls, while Stable Diffusion allows greater freedom of use. This openness has accelerated the adoption of Stable Diffusion among developers and creatives. The same is happening in social media, where more provocative content generates more engagement.

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The risk-opportunity trade-off

Companies developing AI face a dilemma: more advanced models need more stringent protections, but these limit their potential. Increasing model capabilities widens the gap between theoretical possibilities and permitted uses, creating room for companies willing to take greater risks.

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Emerging solutions for risk management

Two approaches are emerging:

- Open source strategy: open source publication of models transfers responsibility to customers or end users. Meta with LLaMA is an example of this strategy, which enables innovation by reducing the responsibility of the model creator.

- Specialized ecosystems: the creation of controlled environments allows developers to manage specific risks in their fields. For example, dedicated versions of AI models can be used by legal or medical professionals who are aware of the risks in their field.

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Market implications and future trends

The relationship between risk tolerance and business expansion suggests a possible industry split: large consumer companies will maintain tighter controls, while more specialized entities may gain market share by accepting greater risks in specific areas.

Risk management is becoming as important as technical excellence in determining the success of AI companies. Organizations that effectively balance risk and benefit, through innovative legal structures or specialized applications, gain significant competitive advantages.

Leadership in AI will depend on the ability to manage legal and social risks while maintaining the practical utility of the systems. Future success will be determined not only by the power of the models, but by the ability to manage their risks while delivering practical value to users.