Dr. el Kaliouby warns that the underrepresentation of women in founding and funding AI companies is not just a social issue but a critical economic one. This "boys club" dynamic risks dramatically widening the economic gap over the next decade as wealth creation in AI accelerates.

While not yet visible in aggregate unemployment, Anthropic's research found a suggestive signal: hiring for younger workers in jobs with high AI exposure seems to have slowed over the past year. This may be an early indicator of AI-driven shifts in the labor market.

The initial impact of AI on the job market will target the "lanyard class"—bureaucratic, email-heavy roles in fields like HR and marketing. These positions are disproportionately held by women, which may create an economic shock that accelerates a cultural shift back toward family-oriented careers and homemaking.

The current, formative stage of AI presents a ground-floor opportunity. By actively upskilling and engaging now, while the field is still developing, women can prevent a wider skill gap from forming and ensure their perspectives are embedded in future technology.

The current AI wave could inadvertently harm diversity. The high-pressure environment demanding long hours, combined with a hiring focus on specific Bay Area networks, may lead companies to default to less diverse talent pools, setting back progress on gender and ethnic diversity.

The first wave of AI-driven job losses is hitting women harder, not due to gender bias, but because AI excels at tasks common in clerical and administrative support roles, which are overwhelmingly held by women. Studies show this is a global pattern, creating a significant, though incidental, gender disparity.

AI labs like Anthropic are developing a "barbell" hiring strategy. They prioritize senior talent whose experience and intuition are amplified by AI, alongside junior, "AI-native" hires who are experts with the new tools. This could squeeze out traditional early-career roles, which are now more easily automated.

Modern hiring, reliant on AI and applicant tracking systems (ATS), filters candidates based on keywords and specific experience profiles. This process often automatically rejects highly capable mid-career professionals from non-traditional backgrounds before a human even sees their resume, creating a systemic barrier.

Since no one has decades of experience in emerging AI skills, traditional hiring metrics like company pedigree are failing. Leaders need a "bias toward the future," evaluating candidates on their demonstrated ability to create and solve new problems rather than relying on outdated resume shortcuts.