AI labs are using crowdsourced contest data to infuse human creativity into smarter, more adaptable … More models.

Marcus Spiske

Artificial Intelligence (AI) is evolving at a breakneck pace, fueled by large language models (LLMs) that devour vast amounts of text and code from across the internet. Yet, as AI Labs search for the next competitive edge, they’ve begun to realize something remarkable: the most transformative insights come directly from the nuanced, creative, and often untapped depths of human problem-solving.

Rather than relying solely on curated repositories or scraping the internet at scale, forward-looking organizations are turning to crowdsourcing platforms such as Wazoku’s Data as a Service (DaaS), Codewars (part of Andela), and Topcoder. These platforms host historical contest data—real human-generated solutions to real problems—and provide a treasure trove of “in-the-wild” problem statements and solutions. This data captures the very essence of human intelligence at work: creativity, collaboration, and iterative improvement.

Why Crowdsourced Data Matters

AI models depend on high-quality training data. If the only information we feed them is static or generalized, we risk producing static, generalized models. Instead, when LLMs learn from dynamic human interactions, through solutions crafted by global teams of expert solvers, they can capture the diversity of thought and resourcefulness that only humans can deliver.

Simon Hill, CEO of Wazoku, has seen this phenomenon firsthand:

“As we get to the next evolution of language models, the data we need to start bringing in to train the machine better is just data that’s harder to find. Whether it’s tacit data that isn’t yet codified or analog data that we don’t even know exists, that’s where you need a globally diverse but highly skilled network of people. That is exactly where crowdsourcing plays.”

What Hill points to is a fundamental shift: AI Labs aren’t just mining readily available digital text; they are seeking expert, experience-based, and context-rich data.

The Power of Human Iteration

The Codewars platform, part of Andela, exemplifies how contests can surface world-class coding solutions. It invites developers of all skill levels to solve programming challenges in multiple ways, and then upvotes the most effective approaches.

Carrol Chang of Andela, which oversees Codewars, puts it succinctly:

“The ironic thing about synthetic intelligence is that the best kind is built on the best human intelligence, and that’s what the Codewars platform was built to draw out. People come to Codewars because they want to improve their mastery of coding. Our slogan is mastery through challenge. You have human coders answering the same coding challenge in hundreds of different ways, and the community votes up the best answers… all of that is a gold mine.”

Chang’s insight underscores that these solutions aren’t just theoretical examples—they’re dynamic, tested, and vetted by other practitioners. This style of iterative improvement mimics how real innovation occurs in the wild: through trial, feedback, and refinement. Traditional training sets—like static text or curated code repos—lack that valuable, iterative community input.

Surfacing Hidden Insights

One of the most significant hurdles in AI is finding data that represents a breadth of perspectives, cultures, and creative approaches. According to Hill, “most of the world’s data is still locked inside human minds.” Even if extensive text archives exist, not all best practices or novel ideas make it into neatly formatted repositories. Often, it’s only through competition or collaboration that these nuanced insights emerge.

That’s where platforms like Wazoku’s DaaS come in. By connecting organizations with diverse, global solvers, Wazoku can unearth latent information. AI Labs can then harness that data to train models capable of performing specialized tasks, from designing innovative products to improving coding efficiency.

How Crowdsourced Data Stacks Up Against RLHF

Increasingly, developers of large language models turn to Reinforcement Learning from Human Feedback (RLHF) to fine-tune and align their models. Yet crowdsourced data can sometimes be even more valuable than RLHF, because it originates from broader, more organic interactions rather than feedback from a smaller, curated group of annotators. This broader lens on real-world problem-solving captures a richness of creativity, debate, and iteration that helps models learn more versatile and context-sensitive solutions.

The Future of Contest Data

While historical datasets are already proving invaluable, Tony Jefts of Topcoder highlights the growing value of future contest data:

“Every new challenge that goes live generates a set of fresh ideas, codes, and approaches, and these ‘in-the-moment’ solutions are just as important, if not more than, the archived solutions,” Jefts explains. “It’s a continuous process of problem-solving, and each competition becomes another building block for training more adaptable, future-proof AI models.”

In other words, the competitions themselves—not just the archived solutions—will produce new training sets that will be just as (if not more) important than what’s been gathered so far.

The continuous loop of “challenge → diverse solutions → refined data → updated LLM” fosters a positive feedback cycle that keeps AI relevant and continuously improving. It’s not just about unlocking old data; it’s about co-creating new data that captures the evolving frontier of human knowledge.

A Human-Machine Symbiosis

We often think of AI and humans in opposition—one replacing the other. But as these crowdsourcing platforms prove, the most significant AI models may be the ones that most effectively leverage human creativity. In this hybrid model, humans provide the experimentation, variety, and real-world insights; the AI aggregates these solutions, learns patterns, and refines capabilities.

“Human plus machine is where the real value is,” Hill reminds us. “Why not use the human to accelerate the training of the algorithm and achieve better outcomes for everyone involved?”

Strategic Considerations for Organizations

Identify the Right Platforms
Not all crowdsourcing communities are the same. Organizations should look for platforms with documented, long-running contests and high-quality feedback loops.

Look Beyond the Code
While code-focused platforms like Codewars and Topcoder are invaluable, innovative ideas spring from many domains—design, product development, and more. Wazoku’s DaaS model, for instance, is broad in scope, capturing a spectrum of problem-solving data.

Incentivize Community Building
The best solutions emerge when participants feel motivated to collaborate, critique, and refine each other’s work. Recognizing top contributors and fostering a sense of purpose encourages higher-quality outputs.

Future-Proof with Ongoing Data Generation
Tony Jefts’s focus on the value of future contest data reminds us that AI training is not a one-and-done affair. Regularly updated competitions and fresh crowdsourced insights will keep LLMs dynamic and relevant.

Cultivate Ethical and Transparent Practices
As organizations source data for AI development, it’s crucial to communicate how that data will be used and protect participants’ intellectual property. Transparency builds trust and ultimately yields more and better data.

Crowdsourced data from historically rich contest platforms offers AI Labs a potent way to train more adaptive, creative, and reliable LLMs. By capturing the dynamic and diverse nature of human problem-solving, these platforms provide a steady stream of high-impact insights that traditional data sources simply can’t replicate. Crowdsourced platforms such as Codewars, Topcoder, and Wazoku’s DaaS are poised to fuel the next wave of AI innovation, reminding us that, ultimately, people remain the most crucial input in the AI equation.