The value of learning the culture of tech

Editor’s note: This is an edited version of an article titled “An invisible, moving target: Studying the culture of tech."

The year was 1834 and Thomas Davenport threw back the curtains to reveal his invention: a battery-powered electric motor. The townsfolk went nuts. “Look at this beautiful thing!” they shouted. Fifteen silent minutes passed and they asked, “What do we do with it?”

While there are plenty of people who admire engines as standalone objects, most of us are more interested in how that technology is used. The motor is ubiquitous. We don’t spend time thinking about the big motors that power water treatment plants and tornado sirens or the itty-bitty ones that power our laptop fans and moderately-spooky Teddy Ruxpin dolls. Now let us consider the current fervor around AI technology.

Right this minute, technology is experiencing its biggest paradigm shift in the last 20 years. With OpenAI turning the entire tech world on its head, we are at an interesting crossroads. Will AI become a reliable personal assistant? Will it be seen as a supplemental employee? Will it impact workplace roles and hiring needs? How will AI evolve as it transitions from a proof-of-concept to a capitalist asset? Will AI achieve world domination? AI’s growing impact and its potential for wide-scale societal change is undeniable and difficult to predict.

The gravity of studying the culture of tech

A million users in five days. That’s how fast ChatGPT grew. It took Facebook around 300 days to hit the same landmark. When we say that disruption, by definition, is exceedingly rare, this viral rate of adoption is what we’re talking about. Our current technological landscape is entering a massive shift and this time it's not limited to Silicon Valley, the military or NASA. This is about how we live our lives and do our jobs. It involves society, ethics, data privacy and our relationships with products and brands.

The integration of AI into the fabric of our lives and culture is a development as significant as the home computer or the internet, and some people are not even aware of the massive changes that are coming.

The thing about technology is that it needs to continually change and evolve. This inherent nature of constantly evolving technology landscapes is part of what makes it so difficult to research. The ever-changing language, ethics, culture, consumer sentiment and short lifespan of some tech innovations are just a few of the strands that make up the twisted knot that is tech culture. 

The pitfalls of tech research

As the technology industry continues to rapidly evolve, conducting market research in this field becomes increasingly challenging. Below are some of the common pitfalls that arise in exploring and adapting to changes in the culture of technology.

Underestimating the impact and demand from older generations

Older generations are often overlooked in the development of technologies even though they are increasingly using technology in their daily lives. It's essential to understand how older generations use technology to fulfill their unique needs and how this process may differ from younger generations.

Ignoring latent needs

Product teams often fall prey to the trap of assuming what consumers want from technology instead of asking what they need – and their assumptions can be driven by existing features or roadmaps rather than actual behaviors. Conducting qualitative research to observe and identify the latent needs of consumers can help create products that genuinely solve problems and improve the user experience.

Not allowing users agency and control 

Today, people are becoming more aware of how tech works and are wanting more control over it. The days of technology being a black box are waning, and consumers are becoming much more savvy about the role that algorithms play. Failing to prioritize agency and control can leave the door wide open for competitors who won’t overlook this key consumer need.

Not exploring how tech will fit into users' lives and routines

The echo chamber of technology can be real. It's common for development teams to want to champion their users and design as if they are a user themselves – but the reality is that we are not our users. Teams that understand this and take the time to truly observe and explore how technology really fits into users' daily routines will always have the leg up when it comes to getting ahead of the curve with technology adoption. 

Allowing the product roadmap to cloud judgment

Technology teams live and die by their product roadmaps, feeling the pressure and demand from stakeholders to meet product and innovation cycles or to deliver on iterations and upgrades to products and services. This tunnel vision can blind us from the daily realities of consumer mental models, needs and desires and result in wasted development hours that are neither desirable nor marketable.

Ignoring or overlooking data and ethical concerns

This can be a hard conversation to have, but it’s an important one. Think about how many tech-related ethical touchstones we’ve lived through together. Remember the Patriot Act and the widespread concerns about privacy that it raised? Or when that U2 album was distributed to every iPhone and iPod? How about the numerous scandals around planned obsolescence? GPS trackers being used by stalkers? When companies fail to ask questions about the ethics and data ethics of tech, their consumers will ask the questions for them, and often, infer their own answers. No brand wants to be remembered as a case study in ethics violations; once those associations are made, they are not easily broken. Our perspective: Tracking trends in data ethics should become the next regular brand tracker. Every company should be conducting ongoing research to keep a regular pulse on the fears, opinions and beliefs around data ethics. 

Using jargon or ineffective language 

The term “AI” is becoming increasingly meaningless and can be a barrier to understanding and communication. Even the term “chat” is currently evolving. The longstanding definition of “informal conversation” inherited a tech-skew in the 90s, with the popularity surge of internet chat rooms. 

Now, the term ChatGPT is experiencing something similar to genericide, where journalists point to “tools like ChatGPT” to describe a vast array of AI language processing tools. It's essential to constantly reframe jargon and language to ensure that research is accessible, understandable and produces meaningful insights from studies.

Overreacting to red herrings

It can be tempting in the fast-moving world of tech to react to every development, competitor product or trending topic; however, many of these prove to be red herrings, ideas that look like the answer to a question, but ultimately distract from your goal. Research must strike a balance between being up to date and cautious in its approach.

Forgetting context clues

Technology doesn’t exist in a vacuum; it exists within culture and society. Immersive research allows us to understand the full context: how and when technology is being used, by who and for what reason. It’s important to remember that at times, the technology is the foundation on which multiple uses are built. Like the electric motor, just because you can’t see it doesn’t mean it’s not functioning.

Studying the adoption curve

The adoption curve is a foundational framework and a useful graphic representation of where tech is going. It's common for teams to fall into the trap of only studying the cutting-edge segments, but during moments of seismic change, it's critical to follow the conversation across the entire adoption curve. The adoption curve for conversational AI has been triggered, and as a result, a tidal wave of change will continue to carry forward. It’s important for us to understand the new weather patterns that will emerge for all consumers because of the change. 

Tech must be effective

For every success in technology, there are hundreds (or thousands) of failures. What do these failures have in common? They all overlook the fundamental logic behind consumer technology: It should be designed to be harnessed by users by answering a latent need. Rather than telling consumers what they need, tech companies should be developing products that fulfill needs and add real value to daily life through clear use cases.