AI-geism

Can AI help or make ageism in tech worse?

Welcome to this week’s edition!

Today we’re going to focus on AI’s impact on older (well, at least perceived ‘older’) adults.

We will cover:

• Population trends and why this is an important topic (that no-one talks about)

• How AI specifically can bring benefit or make things worse

Let’s dive in!

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We’re getting older, and fewer

It should be no surprise that with fertility rate decreasing in most countries, except Africa (which we wrote about in ‘Africa’s Hustle Generation’) the share of older age groups will be getting much bigger. As much as 1 out of 4 people by 2100 is projected to be 65 and above, in complete contrast with the last 100 years trend. The global median age, which stood at 24 in 1950, increased to 29 by 2010 and is projected to reach 36 by 2050.

For some countries, things will be even worse: working population is set to decline, and if the math is not wrong - this will mean we will have proportionately more older workers than younger ones.

Andy talked about Japan’s example in ‘Japanification of Work - 5 Lessons’ which can help understand some of the ingredients that are being implemented in what once was a blooming economy full of innovation in technology, manufacturing.

Here’s where AI can be a tricky subject: if we’re getting older and fewer workers, is it actually a bad thing that some jobs are potentially going away?

The answer is not an easy one, because it lives in the conflict between present and future, and lurks behind our bad capability to predict the future and Black Swan events (Pandemics, wars, climate disasters and more) that could flip scenarios in a heartbeat.

Tech: Industry that doesn’t practice what it teaches

What I want to focus on today, is specifically how this problem applies in Tech, and how AI can play a role in this.

Let's be blunt: for an industry that constantly bangs on about innovation and being future-focused, tech has a massive, persistent blind spot. It's ageism. And it's worse here than in pretty much any other industry. We're talking about discrimination based purely on how many years you've been on the planet, or rather, how many years you've been working in tech, because 'old' hits you way faster here.

Tech Ageism on the ground:

• We see it in who gets hired, who gets promoted, who gets paid what, and frankly, who even feels welcome.

• The myths are pervasive and frankly, tired: older workers 'can't learn new tech' (nonsense), 'aren't adaptable' (double nonsense), 'cost too much' (maybe, but value?), or just 'don't fit the culture' (the biggest dodge of all). These are lazy stereotypes, but they stick.

• Oddly, evidence actually points the other way – there's this idea of the "Tech Sage Age", where experienced folks (maybe 40-ish and up) are often top performers. Go figure.

There are some deeper reasons why ageism thrives here, beyond just individual prejudice. Think of it like the industry's operating system having a bug.

• The Money Angle (Profit Logic): There's a clear incentive to hire cheaper, younger talent. Why pay for 20 years of experience when you can pay for 5 at half the price? The focus is often on short-term profit margins, not the long-term value that experience brings.

• The Dreaded "Culture Fit" (Culture Logic): This is often code for "are you exactly like everyone else here, which means young?" If your culture is built around ping-pong tables and late-night pizza coding sessions, someone with family commitments or just... different preferences, regardless of skill, might be seen as a bad fit. It's less about actual productivity and more about maintaining a youth-centric "vibe" that has nothing to do with building good tech. This focus on "culture fit" becomes a convenient, nebulous excuse to filter out older candidates.

• The Innovation Obsession: Tech worships speed and disruption. These traits are stereotypically slapped onto young people. Older workers get unfairly tagged as slow or resistant to change, which is insane, but it's part of the narrative. The very thing tech prides itself on – innovation – is paradoxically used to justify excluding experienced people who could actually drive deeper, more sustainable innovation.

Three types of innovation, two of which are proprietary of older workers.

• Tech Years Are Like Dog Years:

  • Seriously, the point where you're considered "old" in tech is ridiculous. It hits way earlier than in other jobs. Studies suggest tech workers feel "old" in their late 30s. In the UK, some reports say age discrimination kicks in around age 29 – over a decade earlier than the average for other industries!

  • Look at the numbers: the average age in US tech is 38, compared to 43 elsewhere. But drill down to the big names – Meta's median age is 28, ByteDance is 27.

• The Prevalence is Hard to Ignore (If You Bother to Look):

  • Some countries obviously have it different. Something like 90% of US workers over 40 feel they've faced ageism at work. Nearly half of all tech workers surveyed in a recent study have witnessed or experienced it. In the UK, over 40% of tech workers think older employees face prejudice. Italy, the usual black-sheep, may be actually preferring things the other way around (which doesn’t work either).

  • It's not just feelings; it's official complaints. Almost 20% of discrimination charges filed with the EEOC in tech are age-related – higher than in other sectors.

AI: Amplifier or Unlikely Hero?

Now, let's bring AI back into the picture. This is where the "tricky subject" gets even trickier. AI is increasingly used in HR – hiring, managing talent, reviewing performance. And it's a total double-edged sword when it comes to age - especially in times like these where layoffs are an unfortunate and frequent buzzword.

Ageism is one of the key reasons why workers feel that they may fear for their jobs, but it comes in a pretty paradox:

AI should Automate Entry-Level Jobs/Tasks: If and until we get to AGI, super-intelligence or whatnot - AI is more likely to make more manual and ‘easy’ tasks redundant. These tasks are usually tied to younger workers.

Value/Time: While older workers' contributions may be recognized and valued once they are established within an organization (as reflected in performance ratings), they face significant age-based barriers preventing them from getting hired in the first place. The institutional logics prioritizing youth, innovation-linked stereotypes, and "culture fit" in recruitment seem to override the potential performance value that experienced hires could bring. AI-driven hiring tools, often trained on historically youth-skewed data, likely exacerbate this initial entry barrier.

So, unless younger workers become super-skilled faster (which is definitely an objective of the tech industry - yet to see if possible) it may be the case that there will be even more value in training and getting value from older workers.

AI Discrimination - This means it’s also an ‘AI problem’ given that AI is trained on our human biases, and is well known to discriminate in hiring.

AI Democratization - On the flipside, AI is a potential equalizer - making non-technical people able to code, design and do a number of other activities that were previously out of scope and out of reach.

AI-Powered Learning Platforms - Beyond tools for specific technical tasks, AI is increasingly embedded in educational platforms themselves, offering adaptive and personalized learning experiences. The ability of AI to adapt the learning pace can be particularly beneficial for older adults who may learn more slowly than their younger counterparts.

Also, check out the next two graphs: creativity, leadership and people ‘problems’ are what will matter most in an age of potential super-intelligence and increase in automation. These are all responsibilities and skills that experience and maturity requires.

Every Problem is a ‘People’ Problem

This famous saying, will become even more critical in the age of AI. Because the more technology we throw at work and life, the more we expose the underlying human mess: our biases, our flawed cultures, and our struggles to adapt. AI won't fix those; it's more likely to amplify them.

Ultimately, this boils down to a cultural problem, like the 'tech bro' mindset that, frankly, seems to be digging its heels in rather than fading away. It's this culture that fuels the real roadblocks for older workers trying to adapt. So while AI tools sound great for democratizing skills, just having them out there doesn't make things fair. Maybe even more critical are the mental hurdles this environment creates: that nagging 'tech anxiety,' or the beaten-down confidence that comes from constantly being told (explicitly or implicitly) that you're 'too old' for this game.

AI isn’t going to disproportionately kick older workers to the curb because they supposedly "can't adapt." AI is going to shake things up for everyone. The need to constantly learn new tricks, upskill, and reskill isn't an "old person problem"; it's the new normal for the entire workforce, top to bottom (and it’s going to create serious cognitve overload problems, which I discussed in “Buried by Bots: How AI is maxing out our cognitive bandwidth”

Ciao, Matteo