There is no best AI model. But there's something better

In recent years, we've come to expect a winner-takes-all environment in technology. Google won search, YouTube won video streaming, and LinkedIn won professional networking.

And, for some time, many thought AI models would follow that playbook. When ChatGPT emerged in November 2022, it quickly became synonymous with large language models (LLMs).

However, in 2026, ChatGPT is rethinking its approach while Google’s latest Gemini model is top of LMArena’s model board and everyone is talking about using Claude Code to build their own suite of apps. ChatGPT surely will be fine, but the point remains: there likely won’t be a clear de facto chatbot or agent that everyone uses. The same is true for video LLMs.

The latter topic was famously the subject of a viral Andreessen Horowitz essay: There is no God Tier video model: But there is something better.

That's especially true for Sora, Runway, Pika, Kling, Veo, and the dozen other video models competing for the top spot. While all of those companies may be focused on being the one true winner, there are several reasons why they won't be.

Ultimately, AI is not converging into a single universal model. It is fragmenting into highly specialized models, each strong in narrow domains and weak outside them.

Asking "Which AI is best?" is the wrong framing. The better question is: "How do multiple AI capabilities work together smoothly inside a production system?"

AI models are specializing, not converging

The different companies behind these models are focused on different things and building different ways to get there.

One main reason we're seeing so many options is the sheer number of video generation models, and that they're all beginning to fork toward different futures. Increasingly, different models are excelling at different things. Some are better at overall reasoning, others prioritize precision, speed, visuals, or consistency.

For example, Google's Veo 3.1 tends to excel at realistic actions and professional production. OpenAI's Sora 2 is more equipped to create farcical memes. Many have been using X's Grok for animation, and Kling offers lifelike human movement.

GenAI Image Editing Showdown demonstrated this by asking all of the popular creative models to put a full head of hair on a bald man, specifically George Costanza from Seinfeld.

While it was fun to see what hair the models would create - everything from a bouffant to a Beatles moptop, what was more interesting was the slight enhancements they did or did not make to the rest of the scene. Some models enhanced the background and others changed George’s expression.

The real advantage is the Orchestration layer

That there is no single dominant model doesn't mean creatives need to have six different tabs open. Unlocking value will require orchestration, the system that selects which model to use, knows when to use it, and governs how outputs are generated, validated, and routed.

Competitive advantage will come from orchestrating multiple models into controlled, automated systems that put control in the hands of the companies looking to build strong creative. The system that does this will create efficiency gains and new opportunities for those that embrace it.

Context matters more than ‘best’

Sometimes brands want a photorealistic image with their product in different scenes and scenarios. Sometimes they’re looking for something fun or outrageous, like Kalshi’s NBA Finals ad last year.

The "best model" is entirely context-dependent. Put another way, the "one model" enterprises want takes shape at the systems level, not at the model layer.

For most brands, a slightly weaker but predictable model is more valuable than a powerful but uncontrollable one. As more processes move toward automated orchestration, brands cannot risk using an uncontrolled specialist model that could fail badly with no way to correct it mid-flight.

The future of AI is system-led, workflow-integrated

Chasing and committing to the latest model requires constant rework and revalidation, and a lot of time that organizations do not have. They prefer knowing what they're doing, just works.

Real value happens when AI runs as a system, not a feature, and is embedded into the production pipeline. The systems that coordinate models intentionally will outperform any standalone "best" model.

The right systems encourage fully automated workflows, where humans set up the fundamentals and then step away from repetitive decision-making altogether, especially choosing models. The result? Dependable, compliant, and publishable outputs without constant human intervention. Value compounds when workflows are configured once and then run automatically.

Lasting value comes from systems, not from switching models

Content generation is becoming abundant, cheap, and widely accessible. Advertisers have a growing opportunity to use existing and emerging video generation models to drive their creative strategy. They can't afford to get dragged into debates about which model to trust with their entire content operation.

Enterprises looking for more controlled content generation that marries the freedom provided by AI with the control their brands demand will find it with the right orchestration solution.

Understanding how this works takes a simple question. Which is better: risking using the wrong model for a particular opportunity or trusting a system that chooses the right one for you without even needing to get involved? The companies that win won't be the ones who picked the right model. They'll be the ones who stopped asking the question.

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