Perceptions of age in contemporary tech

In her book, Technically Wrong, Wachter-Boettcher (2017) writes that tech is an insular, mono-culture where insiders bond over a shared belief in their own brilliance. She points out that tech culture is rather homogenous in terms of age, ethnicity, and gender; it is young and predominantly populated by men of Caucasian or Asian origin. However, it is also essential to acknowledge that tech today is the result of many influences, of which hacking is perhaps the most known. In an age of surveillance capitalism (see Zuboff, 2019), the leftist and countercultural origins of Silicon Valley and the personal computer are quickly forgotten. Turner (2006) argues that tech culture brings together two legacies: The Cold War military-industrial research culture and American counterculture. Tech is where industry and high science's technological and intellectual output meets Eastern religions, LSD mysticism, and the 1960s back-to-the-land movement. Certain aspects of these origins, such as freedom of information, individual empowerment, and realising the future through code, have been picked up by right-wing politicians (see Turner, 2006), coupling the capitalist logic of profit with an entrepreneurship and startup culture prevalent in tech today.

With its hippie and hacker history, teenage rebellion, and anti-authoritarianism, tech culture is oriented towards youth. Early hackers – populating the computer labs of established universities and later the garages of middle-class suburbia – were most often young prankster men. They showed a general dissatisfaction with the adult world, expressing teenage angst often rooted in science fiction's dystopian vision. It was about young people being comfortable with technology that could intimidate their elders (Thomas, 2002). Indeed, hacking could be seen as a space in which youth – particularly boys – could demonstrate mastery and autonomy and challenge parental and societal authority. The hacker's mastery of technology was often used for pranks at the expense of the adult world. The expression to do things “for the lulz” (derived from LOL – i.e., to laugh out loud) has a youthful rebellious feeling. Thomas (2002) thus considers such pranks as guerrilla warfare waged by boys against the adult world as well as an exercise in control.

The exclusive nature of access to university computer labs in the 1960s and 1970s undergirded popular depictions of programmers as nerds and computer geniuses (Thomas, 2002). Ensmenger (2015) connects programmers working together in dark spaces in front of computers – underdogs – with meritocracy ideals in which programming skills are favoured over titles and looks. That programmers are underdogs and tech culture is based on merits alone can be questioned, given the importance of male networks often founded in elite universities (Wachter-Boettcher, 2017). The hacker, and the later entrepreneur, are based on mostly white, male, Anglo-American networks (Turner, 2006).

In the 1980s, when more and more people had their own personal computers, the mostly male university culture of hacking was transformed into a more suburban youth culture (Thomas, 2002). Turner (2006) talks here about a group of predominantly white men who saw themselves as part of a creative, independent elite, bringing the world back into balance, combatting bureaucracy and authorities with information technology. This resonates in contemporary startup values, emphasising taking risks and believing in yourself (Svensson, forthcoming).

Today, tech is marinated in entrepreneurship enthusiasm with slogans such as “invent the future” and “move fast and break things”. This is particularly evident in large tech companies with services and food possibilities geared towards creating an atmosphere of play, loyalty, and flat hierarchies. Kunda (2006), in his ethnography of a tech company (researched in the 1980s), describes managers’ attempts to generate enthusiasm and strong affiliation to the company through an enforced informality, openness, and flexibility. This is about combining paternal care with an open, informal, yet achievement-oriented culture, mixing the playful with the semi-serious. Using images of belonging to a family, tech corporations underline that there should be no contradiction between personal and corporate goals (Kunda, 2006). This allure of offering opportunities for professional development, exciting work, relative autonomy, and fun, as well as a kind of formalised prescription of informality and semi-serious playfulness, still resonates in tech. No wonder tech is full of stories about young people devoting their whole lives to their companies and living their entire lives in the office. “It didn’t look like a business whatsoever – it looked like a bunch of kids in their mid-twenties, you know, screwing around”, as Ayers (in Fisher, 2018: 279) described Google's early days. This kind of youthful entrepreneurialism has also spilt over to the rest of society and influences how identities are constructed today (see Bröckling, 2016).

Tech culture is thus geared towards youth. This makes perceptions of age and ageism particularly important to study in contemporary tech industry. But what is meant with ageism? Ageism refers to discrimination based on age, which is typically negative and against older people (Ayalon & Tesch-Römer, 2018). It builds on stereotypes: social constructs or shared ideas about the characteristics of a particular social category. Stereotypes are cognitive structures widely used in social interaction. Individuals are often judged based on their assigned category, which may lead to discrimination – for example, sexism, racism, or ageism (Sangrador, 1996) – and which disregards all diversities in such wide collectives.

Ageism is thus a particular form of discrimination in which individuals are dealt with and governed according to age-based stereotypes: what people should be doing, experiencing, or feeling depending on their age. Stereotypes, which often work unconsciously, contribute to discrimination by ignoring individuals’ real habits, interests, and values (Ayalon & Tesch-Römer, 2018). Beyond stereotypes, ageing is influenced by the process of growing old, which is shaped by life trajectories, and the cultural assumptions, expectations, and beliefs about ageing and about being old that shape human ageing (Ayalon & Tesch-Römer, 2018). Thus, age stereotypes shape both the images that individuals have of themselves and the images that society has of different life stages and age-appropriate behaviour (Lagacé et al., 2015). These can be positive or negative, subtle or explicit (Ayalon & Tesch-Römer, 2018), and may be directed at people of any age. However, the most common form is negative ageism towards the oldest individuals. It is argued that ageism is the most invisible form of discrimination (Ayalon & Tesch-Römer, 2018) and that it could be more pervasive than sexism and racism (Officer & de la Fuente-Núñez, 2018), as it is rooted in stereotypes that are widely accepted and little questioned in society.

Age stereotypes also govern relationships in the workplace (McCann, 2012). With life expectancy around 80 in developed countries and retirement age between 62 and 67, older workers are most often identified as those aged 55 and over (McCann, 2012). Similarly, young workers are considered to be under 35, and middle-aged workers are somewhere in between. While young workers may be viewed as immature and in need of experience, older workers are perceived as less motivated, less enthusiastic, and more reluctant to learn new things. Such prejudices are based on the idea that physical and cognitive decline comes with ageing (McCann, 2012). Ageism is partly due to these widely accepted age stereotypes and partly due to the way in which contemporary Western welfare societies, through their retirement plans, have ousted older people from positions of power (McCann, 2012). After retirement age, people are conceived of as a burden to society. In this sense, ageism implies de-prioritisation, disregard, and even exclusion of older people (AGE Platform Europe, 2016). Age stereotypes thus govern relationships between individuals, society, and institutions, as well as products and services.

Concerning the use of digital technologies, a widespread idea is that older people are less interested in digital technologies (Durick et al., 2013). A digital divide reinforces this. Although older people consistently have lower technology adoption rates than the general public, this group is actually more digitally connected than ever (Eurostat, 2019). In addition, the global population is ageing. Thus, every year there are more older people and an increasing amount of them are using digital technologies. Older digital users may have different interests than younger users because personal values and interests change over the lifetime (Neugarten, 1996). Personal communication patterns and media use evolve as we grow older (Ling et. al., 2012). However, with tech culture geared towards youth, teenagers and young users are most often the imagined users when platforms and services are engineered and designed, risking the marginalisation of older users. It is therefore important to study perceptions of age and ageism among tech workers.

Ageism in tech is not new. In Fisher's (2018) book on the history of Silicon Valley, there are numerous examples of how youth-oriented tech culture is. This resonates decades later in our empirical material. In general, our interviewees described “older programmers” as those above 35 years old and young programmers as under 30. In their descriptions, our interview participants identified themselves and their co-workers as either young or old, leaving no space for middle-aged tech workers. This was illustrated in an interview with a young programmer of Chinese origin when asked if there were any older people in his team. He answered no by referring to “older” as those over 40.

Our interview participants reported that most of the tech workers in their workplaces are under 30. For example, in an interview with an Israeli entrepreneur, he described his team as young and that he is the oldest with his 38 years. Participants also put forward an argument that the overwhelming presence of young people in tech companies is related to a generational issue. A male in his 30s in Silicon Valley, who considers himself as “older”, explained that the majority presence of young programmers is due to computer engineering not being common in universities when he was young.

This shared idea that most tech workers are young has moved tech towards normalising co-workers’ expected age as young. Some of our interviewees referred to rumours in Silicon Valley that there is an unspoken rule that one cannot get hired unless they are under 35, suggesting discriminatory practices against “older” tech workers. A female manager in her 50s in Silicon Valley, for example, explicitly stated that if you are over 50 (and white and male), it is “impossible” to find a job in Silicon Valley. Another participant in his early 30s even questioned whether he would be able to program when he was older:

I have thought to myself, what will happen when I end up outside this group, that is, when I get older, 50 maybe? Will I still program then? Will it be normal to be a programmer when I am 50? I don’t know really because today it is not that usual.

From our sample, we can thus conclude that age is either perceived as young or old, and the dividing line is between 30 and 35 in terms of when you transition from young to old in contemporary tech industry. In the next section, we present what interviewees reported as perceived consequences of these expectations of age.

When it comes to age expectations, the younger tech workers reported working in startup companies, while the more experienced participants were expected to get positions in more consolidated companies, where there were better salaries and working conditions could be adjusted to outside work matters, such as having a family. This difference is illustrated in the excerpt below:

This company is seven years old. When it started, all the people that came in were super young. An older profile means someone with a lot of experience, and the salaries don’t match.

According to our participants, salaries are lower and working conditions worse in companies where most of the workers were young. Younger tech workers are expected to be inexperienced and need to work more hours to acquire the skills required or to be more productive. Conversely, older tech workers are assumed to be experts and often expected to go into management positions with more responsibility, such as project leaders, researchers, managers, and so on. This kind of linear progression from coding to management is not desired by all tech workers, according to our interviewees, either because they switched to a programming career later in life, or simply because not all of them were interested in management positions, as in the excerpt below:

My problem is that after this position, the next step is managing people […] I don’t want to quit programming to manage people.

If this imagined path from coding to management is not followed, it can create problems, as older workers are expected to manage younger workers – not the other way around. When this is not the case – when an older worker is lower down in the work-place hierarchy – the assumption is that this would be hard to accept, both for the “older programmer” and the “younger manager”. This is illustrated in the excerpts below from our interviews with female tech workers in both Silicon Valley and Barcelona:

So, the managers themselves are uncomfortable managing older people [...] my managers were never younger than me so [...] and the guys that I work with who are younger than me, they don’t feel intimidated.

Our findings reveal that it is also expected that older tech workers are less interested in technology and will not come up with “anything new, modern or exiting”, as one participant put it. An idea exists that older workers are not really up to date with the latest technologies and their potential. A male programmer in Barcelona in his 20s described this quite bluntly: “These managers in their 40s, the problem is that they don’t understand the real potential of this technology”. This idea also spilled over in comments on the imagined user of the kind of tech products and services our participants work on. There is a common assumption that most users are young, and hence that the design and development of products and services is best handled by young tech workers. When we asked why there were so many young people at her company, a non-binary tech worker in her (her preferred pronoun) 50s in Silicon Valley explained that “it is because the average age of our users is younger, so it is more market-related than anything else”. However, at least in Europe, tech is used by people of all ages (see Eurostat, 2019). By focusing on younger users, the particular interests of older users risk being neglected.

There is also an expectation of older tech workers being less interested and inclined to learn new things and adapt to changes (so-called cognitive decline, see McCann, 2012). A young male programmer in Silicon Valley stated that the Bay Area is populated by young people “because things are evolving and old people have trouble keeping up… I’m not saying they cannot, but sometimes they have trouble”. A female programming student in her late 30s in Berlin seconded this, complaining that it is “very hard to keep being relevant, you know… you should not always be in your comfort zone because then you are not relevant anymore”. This was further underlined by the non-binary tech worker in Silicon Valley:

Good older developers who are great can keep up, but they probably can’t keep up with the pace of change. It also depends on where you are. So, I see a tendency for the bad or mediocre [programmers] […], they will just continue with the ancient but necessary technology, make a living out of that and get left behind.

Among our participants, there was an idea that people willing to enter the tech industry are not able to assimilate the required skills if they are “old”. A tech worker in his 40s in Barcelona linked the motivation to learn new skills with age in this manner, that older tech workers were not as willing to embrace the role of a beginner, as exemplified in the excerpt below:

Getting into the tech industry as an older person is incredibly difficult given the number of young people… it is a lot of learning, and it takes a lot of years, and adults are generally not willing to be beginners in that way.

Some interviewees perceived getting older as a potential problem in terms of processing information. The interview excerpt below shows a freelance programmer's argument for why he had chosen to stay freelance:

I don’t know if I can continue my work forever. I like my job, but I don’t know if my brain can process all this information or if I’ll be able to sit and program for 10 hours a day when I get older. When I get home after a long workday, I’m braindead.

This resonated with the female programming student in Berlin attempting to change her career and finally pursue her dream of becoming a programmer. But in her late 30s, she felt old:

I need more time to learn, to spend time at home. Sometimes I’m there until 2 am [...] I just feel the breath of my younger colleagues behind me, so […] it is not easy […] I have two pretty young colleagues […], and they are faster, not always as good as they should be, but fast, and sometimes their experience is not so good […] it is not easy to stay motivated.

There thus seems to be a continuous trade-off between being in a comfortable position and continuing to learn and work as hard as is expected in contemporary tech. In some cases, our participants found a niche, for example, working with specific technologies such as old programming languages that might still be used in some areas (as was the case with our freelance programmer in Berlin).

As previously discussed, tech's roots in hacker culture – its entrepreneurship and startup values – impact how work in the tech industry is conducted. Tech workers are expected to live for their company and work long hours, and the team is supposed to be their family. That this becomes problematic when, or if, starting a family became obvious in our interviews. A tech worker in his 40s in Barcelona, for example, explained that he wanted to work in a company “close to home, in a company without any financial struggles, to have a balance between quality of life and stress, and to have the possibility of working remotely”. Influenced by startup and entrepreneurship values, contemporary tech industry emphasises the importance of being hungry and mobile, and it expects workers to devote their whole lives to the company. This is illustrated in the excerpt below:

The Bay Area is where you live for a little while, and then you cash out. People are here for the experience, and that is why you don’t see a lot of old people; the old people you see here are the rich ones.

The norm of being young and enthusiastic (especially in startups) was illustrated by the Israeli entrepreneur when asked whether he had ever experienced ageism in tech. He told us a story about how he talked to an investor about his partner, who was 27, and he portrayed him as “super passionate, quick at coding”, and connected this to his younger age. The investor really liked that part of his description. That was the first time he had experienced ageism, he told us. This connection between youth and startups values was further underlined by an entrepreneur in Barcelona:

Young people are more open to this aspect of startups, working endless hours, devoting all the passion that the product requires; an older person probably has a family and different priorities.

Hence, while young tech workers are expected to work longer hours, they are at the same time perceived as less experienced, less productive, but nonetheless willing to learn, pick up new things, and devote their time to their work. Conversely, older tech workers are portrayed as slower, less connected to their work, and more focused on personal and family issues, and hence less attractive as co-workers. “When people get older, they value other things in their life”, such as their families, and “maybe they don’t put a lot of effort into their work”, as the Israeli entrepreneur put it.

This is also connected to the way in which tech companies often provide on-site entertainment, games, food, and drinks meant to encourage team-building. When visiting some of the headquarters of our interviewees’ workplaces, we were blown away by the scale of the place and the number of free services, exquisite art and delicious food available to us without having to open our wallets once. We saw environmentally conscious services like bike-repair shops, an in-house smokery for Texan barbecue, and art workshops where tech workers could make, for example, their own pins or posters. This underlines the way in which the tech workplace almost becomes like a playground and a space for sociality where workers are expected to spend their whole day and also the evening. This often fits better with a young lifestyle, or at least for tech workers who have no family responsibilities; they are the ones who have a greater incentive and possibility to take advantage of these services. For example, a male programmer in his 30s in Barcelona described how there “are juices, and fruit and games”, but how he nonetheless “would like to work from home [and don’t] want to have to work extra hours”. Another Barcelona tech worker, a bit older, put it in the following manner:

I know it means bad salaries and fewer benefits. There is beer, and some stay on after work to have a few beers; I go home to my family. The time I spend playing table football, I would rather be at home.

Hence, workers who prefer not to use these on-site resources risk being marginalised in their teams. A corporate culture that promotes team-building through a young lifestyle (see also Kunda, 2006), risks deprioritising, disregarding, and excluding older workers.