The First Job Problem: Where the Gender Gap in Tech Actually Begins (ft. Sue Keay)
An article from the What The Tech (AU) podcast, expanding on our conversation with Dr Sue Keay — Director of the UNSW AI Institute and Founder of the Robotics Australia Group.
Most organisations today would say they take diversity seriously.
They publish targets, report on representation, and speak confidently about inclusion in leadership forums. At a glance, it looks like progress is being made. Yet despite this growing attention, the gender gap in technology remains stubbornly persistent, both globally and in Australia. Women make up only around 28–30% of the technology workforce locally, and their representation drops even further in highly technical and senior roles.
The industry often describes this as a pipeline problem, as though the issue lies somewhere upstream in education or interest. But that framing, while convenient, obscures something far more immediate and far more actionable.
Because pipelines do not just leak. They are shaped.
And as Dr Sue Keay explains, one of the most decisive points in that system is not where most leaders are looking.
It is the very first job.
“…the first job that they get is crucial.”
That statement sounds simple. It is not.
The Gap Doesn’t Start Where We Measure It
Most diversity conversations focus on visible outcomes. Leaders talk about representation at the top of organisations, promotion rates, and executive pipelines. These are the metrics that get tracked, reported, and debated publicly. However, they are also the least useful place to intervene.
By the time a gap appears at leadership level, it has already been compounding for years. In fact, research consistently shows that one of the most significant drop-off points for women in STEM is not mid-career, but the transition from education into the workforce.
This is the moment where potential becomes trajectory.
And trajectory, once set, is difficult to change.
The First Job Is a System, Not an Event
The first role someone receives in Tech is not just a job. It is the beginning of a system that determines how their career unfolds.
It is where they gain their first real experience.
It is where they build relationships and networks.
It is where they are seen, evaluated, and remembered.
These factors do not operate independently. They reinforce each other. Early experience leads to stronger CVs. Stronger CVs lead to better opportunities. Better opportunities lead to faster progression.
This is what makes the first job so powerful. It is not about immediate output. It is about long-term positioning.
The Quiet Decisions That Shape the Future
In the conversation, Sue describes a pattern that exists in many organisations but is rarely examined closely. Formal hiring processes tend to include structured checks, such as balanced candidate pools and defined evaluation criteria. However, not all hiring decisions follow the same rules.
Internships and short-term opportunities are often treated differently. They are seen as low-risk, informal, and easier to fill quickly. In practice, this means they are frequently allocated based on familiarity rather than process.
“We know this person… we just want to put this person on.”
And over time, those informal decisions follow a predictable pattern.
“Invariably, that person will be a man.”
No one intends for this to happen. It does not feel like bias. It feels efficient. It feels practical. But it is precisely this kind of decision-making that creates structural imbalance.
How the System Reinforces Itself
The mechanism is subtle, but once understood, difficult to ignore.
When internships are disproportionately given to men, those individuals gain early exposure, practical experience, and internal visibility. When full-time roles later become available, hiring panels evaluate candidates based on experience and familiarity. Naturally, those who were given appear more qualified.
At that point, the decision appears fair.
However, the outcome was already shaped months earlier.
“You’ve weighted your pool of applicants towards men.”
This is not bias in a single decision. It is bias embedded across a sequence of decisions.
The Pay Gap Starts Before It’s Measured
The same pattern applies to compensation.
Sue highlights that when men and women are hired at the same time, men are often offered higher starting salaries. The justification is rarely explicit and usually framed in terms of experience or negotiation. Each decision appears reasonable in isolation.
“The men will be on more money right from the get-go.”
However, salary is not static. It compounds.
Annual increases are typically percentage-based. Promotion thresholds are influenced by current pay levels. Opportunities often follow perceived seniority. Over time, a small initial difference becomes a significant structural gap.
“That is a gap that you may never close.”
This is why the gender pay gap in technology remains persistent, with estimates suggesting it sits around 20% in the sector.
It is not simply created later in careers. It is embedded from the start.
The Myth of “No Suitable Candidates”
One of the most common explanations for the lack of diversity in hiring is that there are not enough qualified women applying for roles. Sue encountered this argument directly and chose not to challenge it rhetorically. Instead, she changed the incentive structure.
She proposed a simple condition: If a team wanted a role filled, they would receive an additional role if they successfully hired a woman into the first position.
The result was immediate.
“It is amazing how quickly people were able to find a woman.”
This reveals something uncomfortable but important.
The issue is rarely the absence of talent. It is the level of effort the system is designed to exert in finding it.
When incentives change, behaviour follows.
This Is a Systems Problem, Not a Pipeline Problem
What makes this insight powerful is that it extends beyond gender.
It reflects a broader truth about how organisations operate. Outcomes are not primarily driven by intention. They are driven by systems.
This same pattern appears in another part of the conversation, when discussing AI adoption in Australia. Many organisations express strong intent to adopt artificial intelligence, yet struggle to translate that intent into measurable outcomes.
“Organisations are still trying to work out what AI means for their business.”
The barrier is not technology — it is structure.
Incentives, decision-making frameworks, and internal capability determine outcomes far more than strategy statements.
The gender gap in tech follows the same logic.
What Leaders Need to Reframe
If this is true, then the question for leaders changes.
It is no longer about fixing the pipeline in abstract terms. It is about examining the specific decisions that shape it in practice.
It is about asking who gets access to opportunity, who gains early experience, and who becomes visible within the organisation. These are not large strategic initiatives. They are everyday operational choices.
Internship selection.
Informal hiring.
Starting salary decisions.
Individually, they appear minor. Collectively, they define careers.
The Work That Actually Changes Outcomes
The implication of Sue’s argument is not that organisations need more policies. It is that they need better-designed systems.
This means recognising that internships are not low-stakes decisions but high-leverage ones. It means analysing starting salaries at the point of entry, rather than focusing only on aggregated pay gap data. It means designing incentives that encourage equitable hiring behaviour, rather than relying on guidelines that can be bypassed.
Most importantly, it requires shifting attention from outcomes to origins. Because the system does not fail at the top. It performs exactly as it was designed at the beginning.
The Bigger Picture
The gender gap in technology is often framed as complex and long-term, and in many ways it is. But complexity should not be confused with abstraction.
The gap is not created by a single decision. It is created by many small ones.
Decisions that feel efficient.
Decisions that feel harmless.
Decisions that feel temporary.
But compound over time.
“We have to make place at the table.”
The mistake is assuming that table is the boardroom. In reality, it is the very first opportunity someone is given or not given.
Final Reflection
If you look at your organisation today, the question is not whether you have a diversity strategy.
The question is much simpler:
Who got the last internship?
Who was given the first opportunity?
Who started just one step ahead?
Because that is where the next generation of leaders is being shaped right now.
Listen to the Full Episode
This article builds on our conversation with Dr Sue Keay on the What The Tech (AU) podcast. In the full episode we cover:
What AI adoption really looks like inside Australian organisations
Why physical AI has accountability that software AI doesn’t
The structural and cultural barriers facing women in AI leadership
What real AI capability looks like at the national level
🎧 Listen now on:
📬 Subscribe to What The Tech (AU) on Substack for episode breakdowns and deeper explorations of how AI is reshaping work, technology and policy in Australia.
Question for readers
If you look at the internship hires your team made in the last twelve months — who got those opportunities, and who didn’t?
References & Further Reading
This article draws on insights from our conversation with Dr Sue Keay, Director of the UNSW AI Institute, on What The Tech (AU), alongside supporting research and industry data on gender diversity in technology and STEM.
Australian Government — State of STEM Gender Equity 2024
https://www.industry.gov.au/news/state-stem-gender-equity-2024
(Women represent around 15% of the STEM workforce in Australia)Tech Council of Australia — Next Wave: Women in Tech
https://techcouncil.com.au/next-wave-women-in-tech/
(Women make up only 20% of the highly technical workforce, with significant drop-off over time)ACS / Industry Reports — Women in Technology Workforce Data
(Women account for roughly 28–30% of the tech workforce in Australia)Workplace Gender Equality Agency (WGEA) — Gender Pay Gap Data
https://www.wgea.gov.au/pay-and-gender/gender-pay-gap-data
(Gender pay gaps persist across industries, with higher gaps in male-dominated sectors)Diversity Council Australia — Lifecycle Approach to Gender Equity in STEM
https://www.dca.org.au/news/blog/more-than-getting-girls-into-science-the-lifecycle-approach-to-gender-equity-in-stem(Women remain underrepresented in STEM and experience persistent pay disparities)
UNSW — The Fix for STEM Workplace Inequity? Change the System
https://www.unsw.edu.au/newsroom
(Highlights structural causes of inequity, including participation and pay gaps)