The Aperture of Utility
Why Some Technologies Expand While Others Narrow
I've been attending the World Economic Forum in Davos this week - an event with an eclectic agenda, an energetic community, and a unique opportunity to learn. Last year, enthusiasm around AI was palpable, and I expected it might naturally wane over time.
**I could not have been more wrong. **
This year, AI dominated every conversation, with even more energy and depth than before.
One of the most intriguing aspects of emerging technologies is how they evolve as we learn more about them. Some technologies, like AI, seem to reveal an ever-expanding array of use cases the deeper we explore. Others, like blockchain or 3D printing, generate early excitement only to see their scope of practical application narrow over time.
This dynamic—the way a technology’s “aperture of utility” widens or narrows—shapes the trajectory of innovation, adoption, and long-term impact. In this post, I’ll explore how this concept applies to AI and other technologies, highlighting why some continue to surprise us with their versatility while others settle into a narrower range of applications.
Defining the Aperture of Utility
The “aperture of utility” describes the breadth of credible use cases for a given technology. In its early stages, a technology may have a narrow aperture, either because it’s immature or poorly understood. Over time, one of two things happens:
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The aperture expands as new advancements or insights enable a broader range of applications.
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The aperture contracts as technical, economic, or practical limitations reveal themselves.
A widening aperture reinforces excitement, investment, and experimentation, creating a self-reinforcing cycle of innovation. Conversely, a contracting aperture tends to temper expectations, often leading to slower adoption or disillusionment.
Expanding Apertures: AI, Cloud Computing, and the Internet
Generative AI: Generative AI has evolved from a content creation tool to a reasoning partner, autonomous agent, and decision-support system. Advances in multistep reasoning, agents, fine-tuning, and collaboration are driving this transformation.
Generative AI is moving beyond static outputs, evolving into a collaborator capable of reasoning through complex problems. It employs techniques like chain-of-thought prompting to break down logical steps, integrates external tools like APIs for richer analysis, and enables agents that can plan, adapt, and execute tasks independently. These advancements allow generative AI to amplify human decision-making and reshape entire workflows.
What sets generative AI apart is its ability to evolve from a tool into a cognitive partner. Advances in reasoning, autonomous problem-solving, and fine-tuning are transforming industries, unlocking value across domains, and redefining the boundaries of what AI can achieve.
What’s most remarkable is that each breakthrough compounds upon the last, unlocking new applications in ways that feel both inevitable and surprising. As these models grow in reasoning power, their aperture of utility will continue to expand—reshaping industries and redefining what we consider possible in AI.
Cloud Computing: Cloud computing’s aperture also expanded rapidly. Initially seen as a way to rent remote servers, cloud services now underpin innovations in serverless computing, machine learning, IoT, and edge computing. By reducing the costs and complexity of infrastructure, cloud platforms unlocked use cases no one anticipated at their inception.
The Internet: The Internet is perhaps the quintessential example of expanding utility. Starting as a tool for research and communication, it evolved into the backbone of e-commerce, entertainment, remote work, and more. Its success lies in its open, adaptable infrastructure, which encourages continuous reinvention.
Lessons from Contracting Apertures
Initial enthusiasm for new technologies often gives way to more focused applications as limitations become apparent. Blockchain, once seen as a universal disruptor, has found meaningful yet constrained applications in cryptocurrencies and DeFi. Similarly, 3D printing excels in prototyping and custom manufacturing but has not achieved the mass-production revolution many envisioned.
Time Dimension of Aperture Changes
The process of aperture expansion or contraction follows distinct temporal patterns. The telegraph's aperture expanded rapidly in its first decade but plateaued within 30 years as its limitations became clear. Similarly, Segway's initial promise of revolutionizing personal transportation contracted sharply within just a few years of launch, ultimately finding only niche applications in tourism and security patrols. Blockchain ledgers in supply chain are promising. 3d printing continues to find new applications in aerospace and medical fields.
Some technologies undergo multiple cycles of expansion and contraction. Virtual worlds surged with Second Life in the early 2000s, contracted sharply, and then resurged nearly two decades later with the metaverse. This pattern highlights how timing and technological readiness often determine whether a technology can sustain its utility.
The timeline of aperture changes often follows a revealing pattern: technologies that maintain expanding apertures typically show practical utility within their first 2-3 years. Those that fail to demonstrate concrete value in this window frequently experience rapid contraction.
This \"utility window\" appears particularly critical in enterprise technology—if businesses can't find practical applications within initial pilots, broader adoption rarely follows. Understanding this utility window is crucial, but timing alone doesn't tell the whole story. What underlying factors determine whether a technology can demonstrate value quickly and sustain its expansion? Or conversely, what causes some initially promising technologies to contract despite early enthusiasm?
Why Do Apertures Expand or Contract?
Several key factors shape a technology's trajectory during and after this critical utility window, determining whether it can unlock new opportunities or hits fundamental limits.
Technological Maturity: Expanding technologies often gain from breakthroughs that lower costs, enhance usability, or unlock new possibilities. For instance, AI’s expansion is rooted in innovations like transformer architectures (e.g., GPT), which enable scaling to unprecedented capabilities. These technical leaps broaden not just performance but the scope of potential applications.
In contrast, technologies with slower progress may stagnate. Blockchain, for instance, faces persistent scalability challenges that limit its adoption for high-throughput applications like payments or supply chain tracking. Without breakthroughs in performance or cost-efficiency, its aperture remains constrained.
Ecosystem Support: The success of a technology is rarely determined in isolation—it depends on the ecosystem of tools, standards, and partnerships surrounding it. AI, for instance, thrives because of open-source frameworks (e.g., PyTorch, TensorFlow) and accessible platforms (e.g., AWS, Hugging Face, and Google Cloud). These ecosystems reduce barriers to entry and empower a broad range of developers to experiment and build, accelerating the discovery of new use cases.
Conversely, technologies with fragmented or immature ecosystems often struggle to scale. In 3D printing, for example, inconsistent standards for materials and software interoperability have slowed broader adoption, keeping the aperture focused on niche applications rather than mass production.
Economic Fit: A technology’s ability to align with real-world economic needs often determines the breadth of its utility. Expanding technologies tend to have scalable, cost-efficient deployment models that allow adoption across industries. For example, cloud computing thrives because it eliminates the need for upfront infrastructure investments, making it universally appealing to businesses of all sizes.
On the other hand, technologies that fail to offer a compelling value proposition see their apertures contract. Blockchain voting systems, for instance, face significant barriers: they’re often costlier, slower, and more complex than existing centralized alternatives, undermining their practical appeal.
Public and Market Perception: Expanding technologies tend to align with clear, practical needs, while those with misaligned or overhyped promises risk disillusionment. AI has maintained its momentum because it consistently delivers tangible value—whether in automating mundane tasks, improving customer experiences, or accelerating scientific research.
In contrast, technologies like VR/AR have faced a perception gap. While their promise of immersive experiences is compelling, the practical reality—bulky hardware, high costs, and limited killer apps—has tempered widespread adoption. The aperture narrows when hype outpaces practical utility.
Implications of the Aperture of Utility
Understanding whether a technology’s aperture is expanding or contracting has practical implications for businesses, investors, and researchers:
Adoption Strategies: For expanding technologies like generative AI, success depends on experimentation and exploring diverse use cases—whether in healthcare, finance, or education. By contrast, contracting technologies like 3D printing thrive when focused on niches like prototyping or custom medical devices.
R&D Investment: For expanding technologies like AI, broad, long-term research efforts can uncover new applications and extend their momentum. Contracting technologies, like 3D printing, benefit from focused investments in proven applications that deepen their value in specific niches.
Hype Management: Understanding the aperture’s trajectory helps leaders set realistic expectations—embracing AI’s expansive potential while focusing blockchain efforts on its most viable applications.
Closing thoughts
The aperture of utility offers a lens for understanding how technologies evolve and why some thrive while others falter. AI’s aperture is still opening, with every breakthrough revealing new possibilities across industries. But sustaining this momentum will require not only innovation but responsibility—ensuring these tools solve real-world problems while remaining aligned with human needs.
The technologies that succeed are not just the most powerful but the most adaptable, accessible, and useful. For AI, the aperture is still expanding—and the opportunities it unlocks may only grow from here.