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Opinion: Why some “breakthrough” technologies don’t work out

AiNEWS2025 by AiNEWS2025
2026-01-15
in AI Research
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Opinion: Why some “breakthrough” technologies don’t work out
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a Helix flow cell

HELIX

Elvis Chipiro picked universal memory (from the 2005 list). The vision was for one memory tech to rule them all—flash, random-access memory, and hard disk drives would be subsumed by a new method that relied on tiny structures called carbon nanotubes to store far more bits per square centimeter. The company behind the technology, Nantero, raised significant funds and signed on licensing partners but struggled to deliver a product on its stated timeline.

Nantero ran into challenges when it tried to produce its memory at scale because tiny variations in the way the nanotubes were arranged could cause errors. It also proved difficult to upend memory technologies that were already deeply embedded within the industry and well integrated into fabs.  

Light-field photography (from the 2012 list), chosen by Cherry Tang, let you snap a photo and adjust the image’s focus later. You’d never deal with a blurry photo ever again. To make this possible, the startup Lytro had developed a special camera that captured not just the color and intensity of light but also the angle of its rays. It was one of the first cameras of its kind designed for consumers. Even so, the company shut down in 2018.

Lytro field camera
Lytro’s unique light-field camera was ultimately not successful with consumers.

PUBLIC DOMAIN/WIKIMEDIA COMMONS

Ultimately, Lytro was outmatched by well-established incumbents like Sony and Nokia. The camera itself had a tiny display, and the images it produced were fairly low resolution. Readjusting the focus in images using the company’s own software also required a fair amount of manual work. And smartphones—with their handy built-in cameras—were becoming ubiquitous. 

Many students over the years have selected Project Loon (from the 2015 list)—one of the so-called “moonshots” out of Google X. It proposed using gigantic balloons to replace networks of cell-phone towers to provide internet access, mainly in remote areas. The company completed field tests in multiple countries and even provided emergency internet service to Puerto Rico during the aftermath of Hurricane Maria. But the company shut down the project in 2021, with Google X CEO Astro Teller saying in a blog post that “the road to commercial viability has proven much longer and riskier than hoped.” 

Sean Lee, from my 2025 class, saw the reason for its flop in the company’s very mission: Project Loon operated in low-income regions where customers had limited purchasing power. There were also substantial commercial hurdles that may have slowed development—the company relied on partnerships with local telecom providers to deliver the service and had to secure government approvals to navigate in national airspaces. 

""
One of Project Loon’s balloons on display at Google I/O 2016.

ANDREJ SOKOLOW/PICTURE-ALLIANCE/DPA/AP IMAGES

While this specific project did not become a breakthrough, the overall goal of making the internet more accessible through high-altitude connectivity has been carried forward by other companies, most notably Starlink with its constellation of low-orbit satellites. Sometimes a company has the right idea but the wrong approach, and a firm with a different technology can make more progress.

As part of this class exercise, we also ask students to pick a technology from the list that they think might flop in the future. Here, too, their choices can be quite illuminating. 

Lynn Grosso chose synthetic data for AI (a 2022 pick), which means using AI to generate data that mimics real-world patterns for other AI models to train on. Though it’s become more popular as tech companies have run out of real data to feed their models, she points out that this practice can lead to model collapse, with AI models trained exclusively on generated data eventually breaking the connection to data drawn from reality. 

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