Beyond the Hype: CTO Argues AI Can Augment, Not Replace, Human Creativity

A CTO argues generative artificial intelligence (AI) enhances creativity by lowering barriers and shifting human focus to higher-level tasks.

Lowering Barriers to Creativity with AI

Fueled by fears that AI will eventually stifle creativity, doomsday predictions are nothing new, as Phillipe Wassibauer, chief technology officer (CTO) at Crunchdao, has asserted. Wassibauer, however, argues that even some of the most successful technological solutions have faced similar resistance before ultimately proving to be tools that enhances human creativity.

Making the case for generative AI, Wassibauer told Bitcoin.com News that instead of stifling human innovation, the technology is proving to be “a more powerful tool that lowers barriers to creativity.” He points to how anyone can easily use AI to produce high-quality videos with well-crafted prompts, supporting this assertion.

Furthermore, rather than degrading human creativity as some critics point out. This, he argues, shows that “generative AI enhances, not replaces, the creative process.”

Regarding low trust in AI, the Crunchdao CTO identified privacy fears and job loss concerns as some of the key contributing factors. Though not a “cure-all,” the CTO argued that decentralization may be the step that aligns AI with values like fairness and autonomy, which can build trust.

In other written answer shared with Bitcoin.com News, Wassibauer addressed AI risks and how current engineers can help reduce these. He also gave his perspective on regulatory steps taken so far. Below are Wassibauer’s answers to the questions sent.

Bitcoin.com News (BCN): According to a report by KPMG, three in five people are wary of trusting artificial intelligence (AI) with 67% of people reporting low to moderate acceptance of the technology. Do you believe that the advent of decentralized technologies and other associated innovations could help enhance users’ trust in AI? In your view, why is there such a deep trust issue in the first place?

Phillipe Wassibauer (PW): AI’s low acceptance is driven by factors like misunderstanding, privacy fears, inaccuracy, rapid development, and job loss concerns. Decentralization can help by improving privacy with user-controlled data, increasing transparency through auditable systems, and reducing centralized control. While not a cure-all, it’s a step toward aligning AI with values like fairness and autonomy, which can rebuild trust.

BCN: Are there any AI-related trends or innovations that you think are being overlooked or underappreciated? How do you think these trends or innovations could be leveraged to drive growth or improvement in the data analytics space?

PW: AI agents are set to become a major trend, especially in blockchain ecosystems. These systems are tailor-made for bots—data is accessible, systems are composable, and interactions are seamless. As the finance sector moves onto blockchain, the potential for AI agents to leverage this data will grow, driving smarter automation, optimization, and innovation in analytics and decision-making.

BCN: Can you briefly discuss any challenges or obstacles you faced when trying to transition traditional systems to decentralized frameworks, and how you overcame these challenges?

PW: Crafting network effects with tokenomics: In traditional apps, adoption hinges on building a great product and finding adoption. In Web3, tokenomics are key to driving network effects. It’s critical to design them thoughtfully to reward early adopters and align incentives among participants for growth and collaboration.

Deciding on decentralization levels: A fully decentralized protocol is the end goal, but going fully decentralized too early can slow product development and decision-making. Finding the right balance for the initial protocol iteration is challenging but essential for long-term success.

Regulatory compliance: Operating in a nascent field means navigating unclear regulations, which takes significant time and effort. Building compliant products while staying agile is a constant challenge.

BCN: In the past few years, several AI platforms have emerged where internet users can apply prompts in simple languages to achieve results. Many believe the emergence of such solutions is taking away the creativity and intuitiveness associated with humankind. What is your reaction to this assertion? Do you believe in striking a balance between human creativity and AI capabilities, or do you think humanity is on an irreversible path toward AI dominance?

PW: This fear arises with every new technology—books, computers, the internet—you name it. Yet, each of these has ultimately increased human creativity. Generative AI is no different in my opinion.

It’s simply a more powerful tool that lowers barriers to creativity. For instance, you can now produce high-quality videos with well-crafted prompts that previously required a big budget and extensive effort.

Human creativity isn’t being lost; it’s shifting. Instead of focusing on operational tasks, we’re moving toward ideation, direction, and planning. Generative AI enhances, not replaces, the creative process.

BCN: How do you balance the potential benefits of AI-driven automation with the need to protect jobs and ensure that AI systems are transparent and accountable?

PW: AI systems rely heavily on human input, and it makes sense for such systems to reward the creators of the data they learn from. At CrunchDAO, we’re building a system that embodies this principle. As it matures and becomes more autonomous, we ensure that IP stays with the creators. Whenever their models are used, they earn royalties, creating a potential for passive income.

Additionally, we plan to use system-generated revenue for token buybacks and burns, offering further upside to network participants. This approach not only aligns incentives but also ensures transparency and accountability. I expect similar models to emerge across other decentralized systems.

BCN: You recently joined Crunchdao as the company’s CTO, bringing over 20 years of leadership in engineering and product development. As CTO, what AI-related initiatives or projects are you most excited to explore or develop in the near future? Also, can you shed light on the future of decentralized compute in Crunchdao’s roadmap and how it integrates with AI/ML?

PW: I’m particularly excited about the real-time prediction systems we’re scaling up next year. These systems process real-time data streams to generate predictions, starting with mid-market price predictions. The next use case will likely focus on improving on-chain systems, creating immediate and actionable value for decentralized ecosystems.

What excites me even more is how these systems evolve. They can be continuously tuned, with new models added and outputs aggregated through composable methods. Multiple actors contribute to optimizing predictions, ensuring the best ideas rise to the top. This creates a transparent and open system where anyone can participate, and those contributing to value creation are rewarded consistently.

As for the decentralized compute, it’s central to CrunchDAO’s roadmap. It aligns with our vision of a democratic and scalable predictive modeling ecosystem, enabling real-time AI/ML capabilities while ensuring efficiency, fairness, and inclusivity in how predictions and insights are generated.

BCN: Crunchdao claims to have over 6,000 data scientists and 600 PhD level individuals that develop alpha-generating insights through its collective intelligence network. Why such a high number of experts, what exactly do they do, and how does the platform manage operations within its network?

PW: Currently, our data scientists and PhDs compete in high-level challenges on topics like predicting mid-market prices, causality analysis, cancer prediction, and portfolio management, among others. Companies and foundations approach us to test and challenge their internal methodologies, often resulting in the development of new and more effective methods. These challenges are structured as tournaments and our decentralized approach has repeatedly outperformed traditional, internal models.

But this is just the beginning. We’re building a decentralised network where participants can contribute models and predictions, evolving the platform into a protocol-driven, and innovative predictive modeling ecosystem. This approach fosters collaboration, incentivizes creativity, and ensures continuous improvement, creating a system far more dynamic and effective than centralized alternatives.

BCN: Like every innovation, AI comes with risks especially in the current infant stages of its evolution. Data handling and developmental risks lie in the hands of software engineers and data analysts. How much trust do you have in the current generation of AI/ML engineers to deliver solutions with minimal risks to humankind?

PW: There’s no inherent risk in machine learning itself, especially in cases like ours, where it’s about finding predictions by analyzing data. When AI is used by individuals or small-scale teams, I’m not too concerned. It’s just another tool to enhance creativity or improve processes. This is not to say that this will not be used for the wrong outcomes here, but it isn’t a risk to humankind.

The real risks emerge when AI is wielded by nation-states or large entities. These players have the resources to use AI at scale, potentially for surveillance, manipulation, or autonomous weapon systems. The issue isn’t the technology but the intent behind its use.

BCN: What role do you think AI should play in informing product development decisions, and how did you incorporate AI-driven insights into your previous roles?

PW: AI is already shaping product development through analytics tools that assist teams in deriving insights. For example, at Dune, we built AI systems that help create or fix SQL queries and generate visualizations, making decision-making more streamlined.

At CrunchDAO, we take this further by envisioning a web of models designed to tackle different problems. These models are rewarded and boosted based on their utility and impact, allowing the ecosystem to self-optimize over time.

This aligns with the future I foresee—AI agents continuously monitoring data, learning patterns, and proactively generating ideas or proposals, driving efficiency and innovation in decision-making.

BCN: The potential risks associated with AI machines have informed existing regulations in the sector. Governments and institutions have repeatedly highlighted the possibility of AI malfunctions or unintended consequences if not properly managed. In your opinion, are these fears justified?

PW: It’s too early to tell how justified these fears are. AI is still in its infancy, and while there will inevitably be misuse or unintended consequences, I don’t foresee major issues when civilians use the technology. Yes, there will be wrong use cases—such as misinformation or scams — but the technology itself often provides tools to counterbalance these risks, like AI-driven detection systems for fraud or disinformation.

What concerns me more is when AI is weaponized or controlled at the state level or by large entities. The risks here like autonomous weapons, surveillance, or large-scale manipulation are far greater. If only big entities or states were to have control over this technology, it would be particularly alarming, as it could concentrate power and create significant imbalances.

BCN: Do you think the regulatory steps taken so far are properly crafted, or are there areas you think should be adjusted to achieve a balanced ecosystem without stifling innovation?

PW: There are proposed laws, but not many are active yet. Overall, having regulatory clarity is a positive step, as it provides guidelines for developers and businesses. However, there’s a real danger of stifling innovation if regulations become too restrictive or fail to adapt to the rapid evolution of AI technology. This is especially the case here as we are at the very beginning of the AI boom and it is unclear what will happen in the coming years, both from the innovation side and regulation.

From what I understand, the balance lies in crafting regulations that address critical concerns—like bias, privacy, and accountability—without creating unnecessary barriers for startups and innovators. Engaging industry experts and iterative policymaking could help, which seems to be happening currently.

BCN: How do you see AI and machine learning technologies evolving in the next 2-5 years? Any more insights from you about AI/ML systems and the possibilities they offer in the fast-evolving ecosystem of decentralized solutions?

PW: I’m not an expert on LLM development and training, but I suspect we might see a plateau in advancements there, as larger models become exponentially more expensive to compute, and acquiring novel data for training grows costlier. For example, the unit economics of OpenAI don’t currently look sustainable when factoring in these challenges.

That said, the existing and upcoming models are already incredibly powerful, which is why we’re seeing widespread integration. As the technology matures and understanding deepens, I expect a period of innovation where new approaches and applications will flourish. In particular, I’m excited about more systems moving onto blockchain. The potential for AI agents to leverage blockchain data—accessible, composable, and seamlessly interactive—is enormous, driving smarter automation, optimization, and innovation in analytics and decision-making.

CrunchDAO is uniquely positioned to lead in this space, building the infrastructure to support and shape these emerging trends, ensuring that decentralized solutions play a pivotal role in this next phase of AI/ML evolution.

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