Standing at the AI Chasm's Edge: Video Gaming's Next Challenge

The Technology Adoption Journey

For those unfamiliar with the concept, the technology adoption curve shows how innovations spread through a market: innovators embrace new technology first, followed by early adopters, then the early majority, the late majority, and finally, laggards. Between early adopters and the early majority lies a chasm—a gap where many promising technologies fail because the needs and expectations of these groups differ dramatically.

It's worth noting that machine learning has crossed this chasm in gaming. The growth of the ML Summit at GDC, the widespread acceptance of ML in LiveOps, and acquisition and retention stand as evidence of this transition. However, ML never significantly impacted production processes themselves.

Looking Forward: The Crossing Ahead

Crossing the chasm will be the most challenging part. Success will require complete solutions (not just technology), proven use cases, and standardized governance frameworks.

On the other side of this chasm is a healthier, more productive industry reinvigorated by new technologies that increase our ability to delight players profitably. The shift from theoretical possibility to practical implementation we've seen at GDC this year suggests we're prepared for this crossing, but it will require collaboration, patience, and persistence.

The gaming industry has always been at the forefront of technology adoption. If we work together—sharing success stories, developing standards, and focusing on pragmatic solutions—we can make it to the other side.

The Value of Experimentation and Incremental Innovation: Navigating the Path to AI-Driven Innovation in Game Development 

As game makers, we're no strangers to innovation. But with the rapid evolution of AI technology, it's easy to get caught up in the excitement of endless tech assessments and lose sight of what truly matters: solving real-world problems to drive value creation. 

Balancing Incremental and Radical Innovation 

When it comes to AI, we often think about radical innovation - the kind of game-changing breakthroughs that can transform our industry. But incremental innovation is just as important and often brings more immediate “better/cheaper/faster” opportunities. Paired with effective experimentation, incremental innovation starts from existing knowledge and technologies to create practical, near-term solutions. It's about making small, incremental improvements that can add up to big impacts over time. 

The Power of Incremental Innovation 

Incremental innovation is not just about making small changes; it's about creating a culture of continuous improvement. By focusing on incremental innovation, we can: 

Increase our chances of project success by tackling smaller, more manageable problems
Pragmatically create solutions that are more likely to be adopted into production
Support and enhance the work of skilled professionals, rather than trying to replace them