We have always been captivated by what artificial intelligence can do. Every year, we see new innovations, and just like that came Sora.

When OpenAI introduced Sora 2 in late 2025, people got impressed by the features that stood out from other AI tools. Sora was not generating static images or short text responses. It was creating scenes, movement, continuity, visual narratives, and appeared to simulate reality itself. A simple prompt could produce cinematic sequences with realistic environments, consistent characters and storytelling elements that would have seemed impossible just a few years earlier. Still, the shutdown was announced not because the technology failed but because the economics may have. For every impressive AI demonstration, compute costs. Most users never see this side of artificial intelligence. When someone generates a video, asks a chatbot a question, or creates an image, it feels almost instant. But behind the scenes, massive data centres are working continuously, thousands of GPUs process huge amounts of information, electricity is consumed and servers are maintained.

For text generation, those costs can often be managed effectively. But for high-quality video generation, it is different. Videos are among the most computationally demanding forms of AI output. As AI video quality improves, the computational requirements increase as well, and this creates a difficult challenge.

The Enterprise Advantage

One important shift in artificial intelligence today is the growing emphasis on enterprise applications. This shift is not happening because creativity is unimportant or because consumer AI has failed. It is happening because enterprise environments give clearer pathways between AI capabilities and measurable financial outcomes. Let’s consider two scenarios:

Consumer AI:

A user generates an entertaining video. The result and experience is impressive. But the direct economic value may be difficult to measure.

Enterprise AI:

A company automates customer support with the help of an AI agent. Response times improve, customer satisfaction increases along with revenue.

Here, one generates excitement and the other generates measurable business outcomes. This is why many AI companies are investing more on agentic AI systems and workflow automation. If an AI system saves millions of dollars annually, paying for that system becomes an easy decision. The economics align and this leads to acceleration in adoption.

The Evolution from Creativity to Productivity

The first major wave of AI adoption was moved by creativity. We experienced AI as a tool that expanded creative possibilities. This phase was essential as it captured attention and introduced millions of people to the potential of artificial intelligence.

But every technology progresses beyond creativity and moves to the productivity phase after some time. This is the time when organizations begin asking if this reduces operating costs or improves efficiency. And answers to it determine long-term adoption. Artificial intelligence is entering that phase now.

AI is reducing the cost of many activities like pricing decisions, market analysis, customer behaviour and strategies formation. By cutting costs, AI is in turn reducing the cost of decision-making itself. When decision-making becomes cheaper, organizations are able to make more decisions, gaining more competitive advantages.

This shows that the companies benefiting most from AI may not be the ones creating the most content but the ones making better decisions faster than their competitors.

Lessons from Sora

If reports about Sora's challenges are accurate, the lesson is not that AI video generation failed. In many ways, Sora may have succeeded too well. It showed the world what artificial intelligence is capable of when technological ambition is pushed to its limits. But technology has never been written by capability alone.

Every major innovation eventually faces a moment when excitement meets economics. For example, Electric vehicles redefined transportation, but their scaling production proved just as important as building the technology itself. Artificial intelligence is now going towards a similar crossroads. And Sora represents one of the clearest examples of this shift.

Working in health tech, I see that a product can be technically brilliant and commercially difficult at the same time, as innovation, adoption, infrastructure, regulation, and economics are connected. The winning technologies will be those that can find balance among all five. Sora may be remembered not as a failure but as an important experiment that showed us where the industry's economic boundaries currently exist. Sora extends far beyond video generation and reveals a maturing AI industry, one that is beginning to think less about what is technically possible and more about what is commercially possible.

What are your thoughts on this?