When People Ask What’s Broken in Tech, This Is the Answer We Keep Avoiding
Why hiring, training, role clarity, and automation are failing talent long before bias ever shows up on a dashboard
When people ask what the biggest problems in the technology industry are, the answers tend to sound familiar: talent shortages, skills gaps, diversity challenges, rapid change.
Those explanations are not wrong—but they are incomplete.
What is breaking tech is not just who gets hired. It is how readiness is defined, how expertise is evaluated, and how little responsibility organizations take for developing people once they are inside the system.
The Quiet Disappearance of Training
One of the least discussed issues in tech today is the disappearance of meaningful training.
Many organizations expect new hires to contribute immediately, with minimal onboarding and no ramp-up time. Entry-level roles are rare. Career switchers are viewed as risky. Even positions labeled “junior” often require years of experience across multiple domains.
At the same time, companies frequently encourage candidates to apply even if they do not meet every qualification. In practice, those qualifications often function as rigid filters. Candidates are still expected to meet nearly all requirements. The language suggests flexibility. The process does not.
This creates a system where potential is acknowledged rhetorically but rejected operationally.
Tools Over Foundational Understanding
Another structural issue is the industry’s fixation on tools rather than foundational knowledge.
Job descriptions increasingly list long stacks of specific platforms, frameworks, and technologies—even when many of those tools can be learned relatively quickly by someone with strong core understanding. This approach narrows opportunity unnecessarily and favors candidates who have already had access to certain environments.
Technology changes constantly. Expecting professionals to arrive fluent in every tool while offering little investment in learning is neither sustainable nor realistic.
When Roles Are Unclear—Especially in Cybersecurity
These problems become especially visible in cybersecurity.
Cybersecurity is not a single discipline. It includes distinct areas such as governance, risk, and compliance; incident response; threat analysis; application security; cloud security; and penetration testing. Yet many job descriptions implicitly expect one person to cover all of them.
A professional may be highly skilled in governance and risk but not in offensive security. Someone may specialize in incident response without deep cloud expertise. Another may understand cybersecurity broadly without working in highly specialized cloud environments.
When organizations write roles as though one person should function as an entire security team, hiring becomes confused, expectations become misaligned, and qualified candidates are screened out for failing to match an unrealistic profile.
This is not a talent problem. It is a role-definition problem.
What Happens When Automation Enters Broken Systems
As automated tools are introduced into hiring and workforce decisions, these structural problems do not disappear. They scale.
Hiring algorithms, résumé screening tools, and ad-delivery systems inherit the assumptions embedded in the processes they support. When expectations are inflated, roles are unclear, and training is absent, automation simply accelerates exclusion.
This is not theoretical.
Amazon discontinued an internal AI recruiting tool after discovering it penalized résumés associated with women because it was trained on historical hiring data dominated by men.
Source: Reuters
ProPublica investigations revealed that job-ad delivery systems allowed advertisers to exclude older workers, effectively enabling age discrimination even when age was not an explicit requirement.
Source: ProPublica
Research from the MIT Media Lab demonstrated racial and gender bias in facial recognition systems, particularly when models were trained on non-representative datasets.
Source: MIT Media Lab, Gender Shades
These cases did not emerge because organizations were unaware of bias. They emerged because automation was layered onto systems that lacked accountability, clarity, and oversight.
Age Discrimination Is Part of the Same Failure
Age discrimination in tech is often discussed separately, but it is tied to the same structural issues.
When hiring emphasizes constant novelty, tool churn, and unrealistic breadth, experienced professionals are quietly filtered out. When job postings prioritize speed, “culture fit,” or ambiguous signals of youthfulness, older candidates are disproportionately excluded—even when their experience is directly relevant.
Automation does not create this bias. It reflects it.
The Problem Underneath It All
At its core, what’s broken in tech is not innovation. It is accountability.
We have built systems that expect perfection from candidates, minimal investment from employers, and neutrality from tools operating within poorly defined processes. We prioritize efficiency over development, specificity over understanding, and automation over clarity.
Bias, age discrimination, and inequity surface in these systems not as isolated failures, but as predictable outcomes.
Fixing this does not require better slogans or ethics statements. It requires investment in training, honesty in hiring, clarity in role design, and restraint in how automation is applied.
Those are operational choices.
Until they change, the same problems will continue to resurface—no matter how advanced the technology becomes.
