Designing Intelligence: Reimagining Creativity and Ethics in the Age of AI

by Elley Cheng

At the recent NYCxDESIGN Talk featuring Will Hall, I had the honor of joining the conversation with a presentation titled “Designing Intelligence,” where I explored how artificial intelligence is reshaping the relationship between creativity, ethics, and economic power.

AI as Collaborator—or Competitor?

Historically, design has been human-centered: understand user

s, design for people. But now, with generative tools like Midjourney, ChatGPT, and Sora, we are co-creating with systems trained on vast datasets—datasets built from human work.

On one hand, this is thrilling. AI can accelerate workflows, spark ideas, and democratize access to tools that were once gatekept. But there’s a tradeoff: these same systems also threaten to displace creative workers and undercut the value of originality by flooding markets with synthetic content.

The line between inspiration and imitation has never been blurrier.

Intellectual Property Rights: A Double-Edged Sword

Intellectual property (IP) rights have traditionally served as a framework to protect creators and incentivize innovation. In theory, copyright allows authors to retain control over how their work is used and to be compensated accordingly.

But in practice, especially in the AI era, IP law has significant blind spots.

The Pros:

• IP can provide legal recourse when artists are exploited or their work is copied wholesale.

• It reinforces the idea that creative labor has economic value.

• It sets boundaries that can guide ethical AI development.

  
  

The Cons:

• IP protections often favor those with legal resources—typically companies, not individual creators.

• Style, technique, and genre—hallmarks of creative identity—are often not protected under current law.

• It can be difficult, if not impossible, for an artist to trace how their work was used to train an AI model, let alone take action.

  
  

The result? A creative economy where the rules are outdated, and artists are vulnerable.

The Industry’s Current Trajectory: Centralized Value, Dispersed Risk

Using Porter’s Five Forces to evaluate today’s generative AI landscape reveals a high-growth but structurally unstable market.

There is a high threat of new entrants and intense competitive rivalry, fueled by billions in venture capital and rapid innovation. Since ChatGPT’s launch in late 2022, over 2,000 generative AI tools have entered the market. Low development costs, open-source models, and widespread interest contribute to this influx.

Buyer power remains moderate. While users are fragmented, switching costs are low and alternative tools are abundant, driving competition on speed, features, and price.

Despite AI’s disruptive impact, the threat of substitution is low—the alternative is returning to slower, manual methods. This makes adoption attractive, even when outputs are imperfect.

However, supplier power is especially weak. Creative professionals—whose work often underpins these models—have little visibility or leverage. Even with intellectual property rights, current legal frameworks offer limited protection or compensation.

These dynamics mirror those in industries like restaurants or fast fashion, where fragmented competition, weak supplier power, and undifferentiated offerings lead to low margins, high failure rates, and unsustainable pressure to spend on marketing and R&D. Over time, such conditions often lead to consolidation, burnout, or market exits—with long-term risks for cultural diversity and creator sustainability.

Changing Course: Toward a More Balanced Generative AI Economy

While the current generative AI market favors speed and scale, its future is still being written. By shaping the underlying economic and legal dynamics, we can steer the industry toward long-term sustainability—benefiting creators, companies, users, and society.

Legal and regulatory frameworks play a key role. Strengthening enforcement of intellectual property rights—while modernizing them—can introduce necessary friction into the system. This encourages companies to act thoughtfully, differentiating their AI tools through higher-quality, licensed training data rather than indiscriminate scraping. Clear IP boundaries can raise the bar for market entry, shifting the race from “move fast and break things” to “move smart to move fast.”

At the same time, creative professionals can increase their bargaining power by organizing into data collectives. Just as image licensing agencies aggregated high-quality stock photography for enterprise buyers, curated training datasets—legally cleared and stylistically rich—could become premium assets in the AI economy.

Consumer behavior is another crucial factor. Re-educating users to value high-quality, ethical AI outputs can drive demand for responsibly built tools. While this may require a shift away from free, low-value content, it opens the door to generative AI products that provide real utility and economic impact. With millions of AI-generated images created daily, the environmental and creative cost of unchecked content generation is also a growing concern.

In a healthier market, AI companies would have the space to invest in new jobs, better tools, and ethical growth. Creators would be empowered to earn a sustainable living. And users would benefit from tools that generate real value—creatively, economically, and environmentally.

As enshrined in the U.S. Constitution, the purpose of intellectual property is to “promote the progress of science and useful arts.” If we realign incentives around that principle, we can advance both technology and human creativity in ways that serve future generations.

Designing Intelligence: For the Many, Not the Few

To "design intelligence" is not just to build smarter tools. It is to design an ecosystem—economic, cultural, and ethical—that allow all participants to thrive. That includes artists, engineers, companies, and yes, even machines.

If we value intelligence, we must value the minds behind it.

If we celebrate generative art, we must honor the artists.

If we believe in innovation, we must build it with accountability.

The future of intelligence is not artificial. It’s collaborative. It’s designed.