Video games have become the proving ground where artificial intelligence must solve problems that no other medium faces: responding to millions of players simultaneously, 60 times per second, without breaking the rules of the world. At the 2026 Runway AI Summit, leaders from Electronic Arts and Paramount outlined why the interactive entertainment industry is reshaping how generative AI actually gets built and deployed.

Why Can't AI Just Generate Game Content Like It Generates Images?

The gap between what AI can do and what games actually need reveals a fundamental mismatch. Generative models excel at producing realistic pixels, but games demand something different: pixels that behave correctly under real-world physics and player input.

"It's not enough for things to look real. That's what you would call a dream. It has to be real, which is what you'd call a simulation," said Mihir Vaidya, Chief Strategy Officer at Electronic Arts.

Mihir Vaidya, Chief Strategy Officer at Electronic Arts

Consider a racing game. On a movie screen, a car bending around a curve only needs to look right. In a game, players feel the drag coefficient on the wheels through their controller. The physics can't be hallucinated or probabilistic. This distinction matters because it forces a rethinking of how AI integrates into creative workflows.

Vaidya explained that games have historically trained AI for four decades. GPUs, reinforcement learning agents, and behavior systems all grew up inside interactive environments. But the AI that trained inside games operated under strict rules: procedural generation, non-player character logic, deterministic systems. Generative AI arrived with the opposite constraint set, creating what he calls the next frontier: control.

What Does "Control" Actually Mean in This Context?

Today's generative models can render almost anything, but they don't understand a studio's intent or aesthetic style. The output often looks beautiful but doesn't feel like the right pixels for the game. Computer graphics historically solved this problem in reverse order. Developers started with foundational building blocks where they could exercise total control, then solved for visual fidelity. AI arrived backwards: incredible realism without the primitives to control it.

This inversion defines the work ahead. Studios need deep customization and workflow collaboration with the companies building the models. EA's biggest franchises span decades, which means their asset libraries predate physically-based rendering (PBR), the technique that makes light bounce off surfaces realistically in modern games. AI now allows the company to modernize these assets and bring them into PBR pipelines, a task that would have required armies of artists in the past.

How Are Media Companies Actually Using AI Right Now?

Paramount's Chief Technology Officer Phil Wiser offers a different lens on the moment. He compares AI to the greatest technology trends in history, placing it in the top five alongside fire and the printing press. His reasoning: transformative technologies rapidly decrease the cost of something humans do. AI is decompressing the cost of certain forms of knowledge work at scale.

Wiser's playbook inverts the usual tech rollout. Rather than leading with the technology, he leads with what people couldn't previously do. Paramount made Runway available to anyone who wanted to use it two or three years ago, coupling access with training and internal communities where people share what's working. The organization becomes a petri dish: inputs are technology and support, and the job is to examine what works and amplify it.

He measures success not by tokens consumed or login frequency, but by business outcomes.

"It's not how many tokens we're consuming or how often people are logging into Copilot or some other tool. What matters is business outcomes," explained Phil Wiser, Chief Technology Officer at Paramount.

Phil Wiser, Chief Technology Officer at Paramount

Steps to Navigate AI Integration in Creative Organizations

• Start with Aha Moments: Don't lead with the technology. Instead, identify something your team couldn't previously do, then get them to experience it firsthand. Skeptics change their minds when they build something themselves and realize what becomes possible.
• Build Internal Communities: Create spaces where people share what's working with AI tools. Teams trust themselves more than top-down directives, so peer learning accelerates adoption and reveals unexpected use cases.
• Measure Business Outcomes: Track results that matter to your organization, not vanity metrics like tool usage frequency. Define what success looks like before deploying AI, then examine whether the technology actually moves those needles.
• Balance Short-Term and Long-Term Thinking: Commit to a six-month push that tacks toward a five-to-ten-year trend. Pop your head up, look where you're going, pull hard, resurface, and repeat. Staying on the surface means missing the deep intuition of what's working.

What's the Timeline for AI's Real Impact?

Wiser believes the window for AI adoption is shorter than previous technology waves. Streaming video took 20 years to reach scale. Consumer computing ran from the 1970s through the 1990s. AI's window is probably five to ten years, though he notes with a caveat: "maybe ten years will be like three years".

Wiser

He resists the industry's instinct to analyze its way to certainty. Companies doing extensive analysis to "get it right" will miss the window. The stakes are clear: companies that get this right for their sector will own that sector. The defining product hasn't shipped yet. While some argue ChatGPT was that moment, Wiser believes the next iPhone moment still lies ahead.

EA's strategy runs on two axes: what will change and what won't. The three horizons of AI impact run in parallel. Efficiency gains may be near-term, expansion of what games can hold may be mid-term, and transformation into entirely new forms of entertainment will be longer-term. They must exist in parallel, not sequentially, given the lead times involved.

What won't change, despite AI, remains equally important. The value of intellectual property and brands, the importance of communities and network effects, the criticality of global distribution and localization, all arguably grow in importance as AI becomes more capable. The companies that recognize this balance will navigate the transition most effectively.