Connect with us

AI Research

Gen AI Will Accelerate the Innovation Adoption Cycle

Published

on


For many Americans, saving money at the grocery store was once a Sunday evening ritual. Out came the scissors and spreadsheets to clip coupons from the Sunday papers stuffed with dozens and dozens of circulars. And a week’s worth of shopping excursions were planned to get the best deals.

Couponing started with Coca-Cola’s hand-written free drink offers in 1887, and the early adopters were working-class and Great Depression-era families who needed to stretch their dollars. After the Great Depression, consumers continued their habit of saving money through coupon clipping, and brands continued to oblige.

It’s also a skill that even made some people pretty famous.  Super Couponer J’aime Kirlew built a whole cable show around her couponing pursuits. She offered her followers tips on how to save a bundle fifty cents and a dollar at a time. And she created converts with her claim of once getting $2,000 worth of groceries for $100. It’s hard work, she said, sometimes requiring six hours of clipping and organizing. But the savings were real, and it made her a role model for super savers with the same ambitions.

Try explaining any of that to a Gen Z.

Of course, they love deals just as much. But why, they ask, would anyone spend six hours cutting and organizing paper rectangles when browser extensions auto-apply codes at checkout?

And why plan elaborate store routes when price comparison happens instantly in-app? Why stockpile products in basements when alerts for flash sales and same-day deliveries eliminate the hassle and need to have room to store a dozen rolls of aluminum foil? To a Gen Z, physical coupons seem as antiquated as printing MapQuest directions.

But people, including Gen Zs, still want to save money. Still hunt for deals. Still seek the satisfaction of paying less, getting a deal. That behavior hasn’t changed.

What’s changed is the technology that supports how they save money on the products they want to buy. Coupon distribution has plummeted from 330 billion in 2010 to just 50 billion last year. People want to save money, but brands and stores have found better ways to do it and still drive sales. Even Kirlew herself has moved on. She recently described saving $225 at a store that was closing and feeling like she was “couponing” without scanning a single piece of paper. The ritual of saving has transformed from a manual weekend hobby into invisible algorithms that do all of the work in the background.

That’s the story of innovation: behavior endures, and the tools that power it transform radically. And the early adopters of that transformation have typically followed a generational cycle.

Until now.

The Great Misconception About Gen Z

We often describe Gen Z as fundamentally different. The truth is they really aren’t. They work, shop, save, pay, travel, split bills, watch movies, go out to eat and strive to stay healthy like every person their age across the generations that came before them.

They didn’t start the digital revolution. That credit goes to millennials. It was they who made the leap from analog to digital, from desktops to laptops to smartphones. They moved from CDs to MP3s, landlines to smartphones, desktop banking to mobile apps. Millennials were digital natives-in-training who normalized mobile as a digital companion.

Gen Z came of age after that shift had already happened. The first-born Gen Zers were eleven when the App Store launched on the iPhone. They weren’t literally born with a smartphone in hand, but they came of age with those devices squarely in their palms.

For them, the phone isn’t an accessory or digital companion. It’s the control center for life. They don’t “go online” because they live there, on those devices. According to PYMNTS Intelligence, Gen Z completes more than 425 digital activities per month, 34% more than every other generation. They’ve collapsed the distinction between physical and digital entirely.

What they do isn’t new. What’s new is how they go about their day-to-day. Just as Kirlew’s paper coupons were replaced by browser extensions that auto-apply codes, every generation’s “go-to” tools simply reflect the best technology available at the time. What remains constant across millennia are the underlying human behaviors that define the daily grind.

How Innovation Used to Work (and Why That’s About to Change)

For decades, innovation more or less followed a predictable relay race. Baby Boomers were the first mass adopters of credit cards in the 1970s. Gen X, and younger baby boomers, normalized ATMs. Online shopping took off in the 2000s. Millennials took us from analog to digital across every category. Gen Z made mobile the center of everything.

The pattern was always the same: invention, adoption, integration, invisibility. One generation would break new ground. The next would scale it. The one after wouldn’t think of it as new, it was just part of the landscape.

FinTechs understood this cycle and built for it. In the 2010s, they designed mobile-first financial tools for a generation that never had to unlearn kludgey analog habits.  They created apps that turned phones into wallets and budget managers.  Gen Z demanded seamless, friction-free experiences.  FinTechs delivered tools that turned banking into swiping and shopping into streaming.

But that innovation wasn’t only the property of Gen Z. The feedback loop created a flywheel that lifted every generation. The Gen Xers and the Boomers haven’t just been dragged kicking and screaming into the digital world. Rather, they’ve been systematically developing their digital fluency in earnest since the start of this decade through everyday necessity and convenience.

Parents and grandparents didn’t ask for peer-to-peer apps. But they use them now because the youngest generation showed them it was easier and better.

The comfort with digital interfaces now exists across generations. A lot of credit goes to Apple, which created devices that are easy for everyone to use — compared to PCs/Windows, which really weren’t. Everyone needs IT support for computers, yet hardly anyone does for mobile devices or most of what we do online.

Once behavior crosses the generational chasm, it becomes routine. Why go back?

But as they say, that was then.

AI Changes Everything: The End of Generational Innovation Cycles

Gen AI and Agentic AI are breaking the traditional innovation adoption pattern. They don’t rely on generational adoption because they will be embedded inside the apps, systems and platforms people already use.

Gen AI and agents will improve the ways we engage with existing apps by creating intelligent, invisible flows. New AI-native apps will be developed that will offer new ways to complete complex tasks. Activities that used to require multiple apps and domain-specific knowledge will collapse into a single prompt.

Intent will take a fast track to execution without all the friction in the middle.

In investing: “Round up my purchases and invest in a low-risk fund unless I’m spending more than usual.” AI will monitor, decide and execute based on context.

In education: “Explain this algebra concept with visuals and examples from last week’s lesson.” AI personalizes curriculum in real time based on individual learning patterns.

In shopping: “I’m trying to find the best dog bed for a Border Collie, recommendations on several brain puzzles to keep her mentally stimulated and the best training tips from the top breeders in the world.” AI will find and buy the bed and the puzzles and produce summarized training tips in less than a minute.

The infrastructure is already here: APIs, data centers, tokens, real-time rails, digital identity, risk engines, cloud platforms. AI will connect the dots and make the system intelligent. It doesn’t create new rails because it doesn’t need to. It just moves people and apps across existing ones faster, smarter and with less effort.

Some of this is already taking shape, though these are early days.

Voice Becomes the Great Equalizer

In the next chapter of this story, the user interface may not always be a screen. But it will always be a sentence, a written or spoken prompt. Voice will become the most powerful interface of this distributed connected economy — not because it’s new, but because it removes the need to learn how to use something new to get a better outcome. When the system understands what you mean, you don’t need to know how to ask perfectly.

In this future, AI becomes the operating system, voice becomes the interface and the mobile device as we know it becomes more optional than essential. OpenAI and others are already designing AI-native devices that don’t look like phones, don’t behave like apps and don’t sit behind screens. They live in your environment, listen, adapt and respond.

You’ll say what you need, and AI will orchestrate the response across systems. Typing becomes a backup plan. Navigation across apps becomes unnecessary. The entire system becomes anticipatory, ambient, context-aware.

AI will work equally well for everyone because it eliminates the learning curve that has traditionally created generational adoption patterns. An 80-year-old can say, “Help me manage my medications and remind me when to take them” just as easily as a 20-year-old can ask for investment advice or travel planning.

From Generational Shift to Universal Access

AI will change the rhythm and the pace of innovation and how quickly new experiences are adopted. For the first time, innovation won’t trickle down from the young. It has the potential to touch everyone because it will become part of everything that is already a native digital or mobile experience.

Gen Z will demand it because they expect technology to anticipate their needs. Millennials will embrace it because they’re comfortable with digital solutions that solve real problems. Gen X will welcome the simplicity because they value efficiency over complexity. Boomers will adopt it because it removes friction rather than adding it. Seniors, who are living longer and more active lives, will find that it makes access to important things easy and intuitive, and preserves their independence.

The traditional innovation relay race, where young early adopters pull older generations forward over time, will be upended.

The New Innovation Economy

This shift has important implications for how businesses think about innovation, adoption and market penetration. The traditional model of targeting young early adopters and waiting for generational pull-through becomes obsolete when innovation is embedded and access becomes voice-activated.

Companies that understand this will build for universal access from day one, rather than assuming that to the youth go all of the innovation spoils. They’ll focus on solving universal human needs like health, wealth, convenience and connection rather than generation-specific preferences. They’ll design for conversation rather than navigation, for orchestration rather than operation.

The winners will be those who recognize that digital fluency already exists across generations, and the missing piece (intuitive, intelligent interfaces) is finally here and getting better every day.

Everyone gets to be an early adopter from day one.

The future of innovation isn’t generational. It’s conversational.

Just ask.

 



Source link

AI Research

On-demand webinar: Artificial intelligence – Next gen tech, next gen risks? : Clyde & Co

Published

on


Artificial intelligence is an umbrella term for technologies that simulate human intelligence. It is one of the greatest sources of systemic risk that insurers now face. It acts as a multiplier of existing exposures and a source of new liabilities, with the potential to cause catastrophic mass loss events.

In this webinar, we delve into the systemic risks of artificial intelligence, including privacy, security, and legal challenges that insurers must navigate.

Our speakers were joined by Dr. Matthew Bonner, Senior Fire Engineer and Research Lead at Trigon Fire Safety, and Rishi Baviskar, Cyber Risk Consultant at Allianz, for a discussion on the systemic risks of artificial intelligence – including privacy, security, and legal challenges that insurers must navigate.

Key topics include:

  • Privacy violations
  • Security threats, weaponisation and adversarial manipulation
  • The threat of ‘uncontrollable AI’
  • Sentient AI and the concept of legal personality
  • And more!

Watch the recording



Source link

Continue Reading

AI Research

Scientists create biological ‘artificial intelligence’ system

Published

on


Credit: Pixabay/CC0 Public Domain

Australian scientists have successfully developed a research system that uses ‘biological artificial intelligence’ to design and evolve molecules with new or improved functions directly in mammal cells. The researchers said this system provides a powerful new tool that will help scientists develop more specific and effective research tools or gene therapies.

Named PROTEUS (PROTein Evolution Using Selection) the system harnesses ‘directed evolution’, a lab technique that mimics the natural power of evolution. However, rather than taking years or decades, this method accelerates cycles of evolution and natural selection, allowing them to create molecules with new functions in weeks.

This could have a direct impact on finding new, more effective medicines. For example, this system can be applied to improve gene editing technology like CRISPR to improve its effectiveness.

“This means PROTEUS can be used to generate new molecules that are highly tuned to function in our bodies, and we can use it to make new medicine that would be otherwise difficult or impossible to make with current technologies.” says co-senior author Professor Greg Neely, Head of the Dr. John and Anne Chong Lab for Functional Genomics at the University of Sydney.

“What is new about our work is that directed evolution primarily work in , whereas PROTEUS can evolve molecules in .”

PROTEUS can be given a problem with uncertain solution like when a user feeds in prompts for an artificial intelligence platform. For example the problem can be how to efficiently turn off a human disease gene inside our body.

PROTEUS then uses directed evolution to explore millions of possible sequences that have yet to exist naturally and finds molecules with properties that are highly adapted to solve the problem. This means PROTEUS can help find a solution that would normally take a human researcher years to solve if at all.

The researchers reported they used PROTEUS to develop improved versions of proteins that can be more easily regulated by drugs, and nanobodies (mini versions of antibodies) that can detect DNA damage, an important process that drives cancer. However, they said PROTEUS isn’t limited to this and can be used to enhance the function of most proteins and molecules.

The findings were reported in Nature Communications, with the research performed at the Charles Perkins Centre, the University of Sydney with collaborators from the Centenary Institute.

Unlocking molecular machine learning

The original development of directed evolution, performed first in bacteria, was recognized by the 2018 Noble Prize in Chemistry.

“The invention of directed evolution changed the trajectory of biochemistry. Now, with PROTEUS, we can program a mammalian cell with a genetic problem we aren’t sure how to solve. Letting our system run continuously means we can check in regularly to understand just how the system is solving our genetic challenge,” said lead researcher Dr. Christopher Denes from the Charles Perkins Centre and School of Life and Environmental Sciences

The biggest challenge Dr. Denes and the team faced was how to make sure the mammalian cell could withstand the multiple cycles of and mutations and remain stable, without the system “cheating” and coming up with a trivial solution that doesn’t answer the intended question.

They found the key was using chimeric virus-like particles, a design consisting of taking the outside shell of one virus and combining it with the genes of another virus, which blocked the system from cheating.

The design used parts of two significantly different virus families creating the best of both worlds. The resulting system allowed the cells to process many different possible solutions in parallel, with improved solutions winning and becoming more dominant while incorrect solutions instead disappear.

“PROTEUS is stable, robust and has been validated by independent labs. We welcome other labs to adopt this technique. By applying PROTEUS, we hope to empower the development of a new generation of enzymes, molecular tools and therapeutics,” Dr. Denes said.

“We made this system open source for the , and we are excited to see what people use it for, our goals will be to enhance gene-editing technologies, or to fine tune mRNA medicines for more potent and specific effects,” Professor Neely said.

More information:
Alexander J. Cole et al, A chimeric viral platform for directed evolution in mammalian cells, Nature Communications (2025). DOI: 10.1038/s41467-025-59438-2

Citation:
Scientists create biological ‘artificial intelligence’ system (2025, July 8)
retrieved 8 July 2025
from https://medicalxpress.com/news/2025-07-scientists-biological-artificial-intelligence.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.





Source link

Continue Reading

AI Research

CWRU joins national AI labor study backed by $1.6M grant

Published

on

By


Research aims to guide decision-makers on real-world effects of artificial intelligence on American workers

Case Western Reserve University economics professor Mark Schweitzer has joined a new, multi-university research collaboration examining the impact of artificial intelligence (AI) on workers and the labor market—an urgent area of inquiry as AI adoption accelerates across industries.

Mark Schweitzer

The $1.6 million project is supported by the Alfred P. Sloan Foundation and led by Carnegie Mellon University’s Block Center for Technology and Society and MIT’s FutureTech. Researchers from eight academic institutions—including the University of Pittsburgh, Northeastern University, the University of Virginia and the California Policy Lab—are contributing their expertise, along with collaborators from the U.S. Chamber of Commerce Foundation.

“This is an important opportunity to bring rigorous, data-driven insights to some of the most pressing economic questions of our time,” said Schweitzer, whose research at Case Western Reserve and the Federal Reserve Bank of Cleveland focuses on labor markets and regional economics. “By pooling knowledge across institutions, we can better understand where AI is helping workers—and where it’s leaving them behind.”

During the next two years, the team will work to improve labor-market data and produce both academic research and policy-relevant reports, he said. The goal is to support research-driven decision-making by employers, labor organizations and government.

More information on the Block Center’s AI and Work initiative.


For more information, contact Colin McEwen at colin.mcewen@case.edu.



Source link

Continue Reading

Trending