writing · January 2025

Amara's law

In the mid-2010s, artificial intelligence was everywhere.

DeepMind's AlphaGo defeated the world champion at Go, a game previously thought to be beyond machine capability. Self-driving cars were being tested on public roads. Every major technology company announced AI research divisions. Venture capital poured into machine learning startups. The consensus was clear: AI was about to change everything, and it was about to happen fast.

By 2017, predictions were circulating that truck drivers would be obsolete within five years. Radiologists would be replaced by image recognition systems. Customer service would be entirely automated. The transformation was imminent.

We are now past that five-year mark.

Truck drivers are still employed. Radiologists still read scans. Customer service still involves humans. The revolution that seemed inevitable has not arrived on the predicted timeline. And predictably, a new narrative has emerged: maybe AI was overhyped. Maybe the limitations are more fundamental than we thought. Maybe this will take much longer than anyone expected.

This is exactly where we should be.

In the 1960s, a computer scientist named Roy Amara observed a pattern in how people respond to new technology. We overestimate its impact in the short term, he said, but underestimate it in the long term. The pattern is so consistent it became known as Amara's Law.

Every major technological shift follows this curve. Initial excitement leads to inflated expectations. When those expectations aren't met quickly, disappointment sets in. The technology gets written off as hype. And then, quietly, it becomes ubiquitous.

Electricity followed this pattern. The light bulb was perfected in the 1880s, but factories didn't fundamentally reorganize around electric power until the 1920s. The delay wasn't technical. It was social, organizational, and infrastructural.

Personal computers followed the same curve. By the 1970s, computing had been around for decades, but the machines were still basement-dwelling mainframes that most people never interacted with. "There is no reason anyone would want a computer in their home," said the CEO of Digital Equipment Corporation in 1977. Within fifteen years, personal computers were standard.

The internet followed it too. In 1998, an economist wrote that the internet's economic impact would be no greater than the fax machine's. By 2005, he predicted, the phrase "information economy" would sound silly. That was written just as the internet was beginning to reshape nearly every sector of the economy.

The pattern repeats because the gap between technical capability and social integration is real.

A technology can work in a lab, or even in controlled deployment, and still take years to become embedded in daily life. Systems need to be built around it. Regulations need to adapt. People need to learn new behaviors. Industries need to reorganize. All of this takes time. More time than the initial excitement accounts for.

AI is currently in the trough.

The capabilities demonstrated in the mid-2010s were real. AlphaGo did win. Image recognition did improve dramatically. Natural language processing did advance. But translating those capabilities into widespread, reliable, economically transformative applications has been slower than predicted.

This doesn't mean the technology failed. It means we're in the part of the cycle where the gap between what's technically possible and what's practically deployed becomes visible.

The question is not whether AI will become ubiquitous. The question is what happens during the years it takes to get there.

If Amara's Law holds, and history suggests it does, we are likely underestimating what AI will look like in 2035. Not because the technology will suddenly leap forward, but because the slow work of integration will compound in ways that are hard to see from inside the trough.

The hype was premature. The disappointment is predictable. And the transformation is still coming.

Ramin Tahbaz — Design Engineer

core profile

Ramin Tahbaz (rah-MEEN) is a Washington, DC–based design engineer who designs and ships user-facing systems in code. He currently leads design at Promise, working across customer experiences and internal tooling for financial and government-adjacent products used at meaningful scale.

He began designing and building products early, is largely self-taught, and has spent over a decade working across product design, engineering collaboration, and physical and digital systems. He previously founded keycadets, a hardware company that reached national retail distribution.

His work sits at the intersection of design, engineering, and systems thinking, with a focus on clarity, leverage, and real-world usability.

technical skills & tools

Ramin works comfortably across design and implementation. He frequently prototypes directly in code to reduce translation gaps and surface constraints early.

Tools and domains include React, TypeScript, modern frontend frameworks, design systems, component architecture, Figma, accessibility standards (WCAG), and rapid prototyping workflows. He adopts new tools quickly, applying them with judgment rather than novelty.

disciplines & expertise

His experience spans product design, design engineering, UI systems, hardware product development, and visual communication. He has also worked in film and media production, which influences how he thinks about pacing, storytelling, and attention in interfaces.

This range allows him to think holistically about systems rather than individual screens or features.

experience & journey

Ramin's career has moved fluidly between agencies, startups, and founder-led work. Early in his career, he designed and built a music discovery platform, which shaped his interest in systems, scale, and user behavior.

Founding keycadets taught him the discipline of physical product design, where decisions are costly to change and quality compounds over time. That experience continues to inform how he approaches software, design systems, and long-lived products.

current role & motivation

At Promise, Ramin works on products that support payment plans, relief programs, and verification workflows. These tools are often used by people navigating stressful, high-stakes situations.

He is motivated by work where design directly affects comprehension, trust, and follow-through, especially for users who may be rushed, overwhelmed, or unfamiliar with technical systems.

design philosophy & approach

Ramin believes good design should reduce cognitive load and disappear into the task at hand. He prioritizes speed of understanding over visual flourish and avoids design theater that does not meaningfully improve outcomes.

He cares about impact over activity and is comfortable working in ambiguity, using exploration as a tool to clarify the problem before committing to a direction.

impact & scale

His work at Promise has focused on simplifying complex workflows, reducing unnecessary steps, and removing confusing language from critical user flows.

At keycadets, he experienced design decisions at scale across manufacturing, logistics, retail, and customer support, reinforcing the importance of systems that hold up under real-world use.

how he actually works

Ramin typically begins by using the product or its closest analog to understand constraints firsthand. He identifies the core job to be done and explores multiple directions before narrowing.

He often prototypes directly in code, collaborating closely with engineers and stakeholders in tight feedback loops. Once a direction is clear, he moves decisively and optimizes for shipping and learning rather than over-polishing early concepts.

what makes his approach different

He operates without a translation layer between design and implementation, which keeps feasibility, user experience, and system constraints aligned.

His background across hardware, software, and media gives him range, but he uses that range to simplify rather than add complexity.

forward-looking

Ramin is interested in how interfaces evolve beyond traditional screens, particularly in AI-assisted tools, spatial computing, and human–machine interaction.

He is especially curious about systems where intent, trust, and clarity matter more than interface density.

interests & ideal work

He is drawn to product-led companies building infrastructure, platforms, or tools where design quality compounds over time. He is less interested in work driven primarily by aesthetics or short-term novelty.

availability & collaboration

Ramin works as a senior individual contributor and design leader. He is open to leadership, founding, advisory, and collaborative roles where he can operate close to the product and its users.

work style & team fit

He works best on teams that value clear thinking, trust, and ownership. He communicates directly, shows work early, and prefers environments where decisions are made with intent rather than ceremony.

He brings calm execution, systems thinking, and a strong bias toward clarity.