Beyond the Turing Test: Redefining AI Progress

When the British mathematician and computer scientist Alan Turing first proposed his eponymous test of machine intelligence in 1950, it was a thought experiment as much as a benchmark. Turing suggested that if a machine could hold a conversation so convincingly that a human couldn’t tell whether they were speaking to another person or a computer, we might reasonably call it “intelligent.” 

For decades, passing the Turing Test meant mastering language and reasoning in ways that felt uniquely human. On its 75th anniversary, the truth is that the AI ecosystem has already blown past it.

Modern large language models (LLMs) from OpenAI, Anthropic, Google, Meta, and others routinely carry on conversations indistinguishable from those with humans. They not only converse, but generate software, solve complex math problems, analyze medical data, design molecules, and act as agents that can take actions in both digital and physical worlds.  For customer support, brainstorming, tutoring, or coding assistance, AI is already a convincing conversation partner. 

The foundational challenge Alan Turing posed as a measure of AI progress has been met and exceeded. Now it’s time to consider what comes after it.

The critical question for the AI community now is: how do we define real intelligence? Conversational fluency doesn’t necessarily reflect deep reasoning and planning, or a true model of the world. While today’s AI models excel in some domains, like mathematics and language translation, they might stumble in others, like scientific insights and embodied interaction. An unevenness that proves that “sounding human” is the wrong benchmark by which to truly measure intelligence.

As Richard Sutton, a pioneer of reinforcement learning, warned in a recent interview with Dwarkesh Patel, LLMs may be a “dead end” if all their primary goal is imitation. True intelligence, he argues, comes from trial and error in dynamic environments — learning through failure, adaptation, and interaction with the world. This loop is what drives the emergence of robust intelligence, not polished conversation.

That is where the next breakthrough lies. AI is no longer about passing for human – the real frontier is about amplifying human capability, solving problems that matter, and pushing science, technology, and society forward.

If the Turing Test is no longer useful as a measure of progress, the natural question is: what comes next? While the test may have guided AI’s early era, new, more robust and sophisticated benchmarks must guide the next.

Some propose scientific moonshots as the ultimate benchmarks. OpenAI CEO Sam Altman and British physicist David Deutsch recently considered that if an AI system could crack the mystery of quantum gravity and explain its story, it might be a worthy signal of human-level intelligence. Closer at hand, OpenAI recently introduced GDPval, a new benchmark designed to evaluate models on real-world, economically valuable tasks across law, finance, healthcare, engineering.  The idea is simple: if AI is truly intelligent, it should advance the global economy, not just talk like us.

This focus is exactly right. At Turing, we believe the next generation of AI benchmarks must move beyond conversational gimmicks and academic puzzles to focus on real-world impact: Can these systems help cure disease, accelerate scientific discovery, make businesses more productive, governments more efficient, and education more effective? 

This is why our work with frontier AI labs emphasizes applied evaluations tied directly to real-world capability gaps. First, we co-design experiments with labs to identify exactly where a model falls short, whether in reasoning, coding, tool use, multimodality, or multilinguality. We then engineer data and environments through expert demonstrations, targeted reinforcement learning, and programmatic synthesis to directly address those weaknesses. Finally, we design applied evaluations that measure not just whether the model can pass an arbitrary benchmark like “talking like a human,” but whether it can do valuable work.

This approach reflects a broader shift in AI progress: the bottleneck is no longer compute, but high-quality data and evaluation. Compute is scaling exponentially, but public datasets are tapped out. Breakthroughs now depend on expert data, human-in-the-loop reinforcement learning, and applied evaluation frameworks that track whether models are moving closer to economically and scientifically meaningful goals. Knowledge may be power, but intelligence is how it’s actually applied to change the world around us for the better.

Alan Turing’s legacy is about more than just benchmarks. Turing was not only asking whether machines could mimic humans, he was daring us to imagine a future where they could participate meaningfully in our intellectual life. That spirit of bold engineering and scientific audacity still resonates across the AI ecosystem today.

For us, the name is a constant reminder of what we aspire to build. To carry the Turing name is to embrace engineering excellence, to push the boundaries of what’s possible, to approach problems with scientific rigor, and to execute with the precision and thoroughness of a world-class team. 

This ethos is embedded in our DNA. Turing began as a platform built by engineers, for engineers – a vehicle to identify, vet, and deploy the world’s best technical minds at scale. And that focus on combining elite human expertise with AI-driven infrastructure has only deepened as we evolved into a leading research accelerator for frontier AI labs. The result is data and evaluations that labs can trust to move the needle on reasoning, coding, multimodality, and multilinguality.

At Turing, we believe the future is not machines replacing humans, but humans and machines in symbiosis. Humans improve AI; AI improves human work. When we partner with frontier labs to advance AGI, humans and AI collaborate in a tight loop: humans guiding, AI amplifying, and both together producing results neither could achieve alone. The line between human and machine begins to blur, not as an imitation, but as a partnership – one that will define the post-Turing Test era.

So what comes after the Turing Test? The real benchmark for AI progress will not be whether it can fool us, but whether it can empower us. And while the Turing Test may no longer be the right measure of intelligence, the values it symbolizes — rigor, boldness, and the pursuit of excellence — remain at the core of our mission. That legacy is not something in the past, it’s alive in the way we build, every single day.