By Inna Weiner, VP Product, AppsFlyer
AI is moving faster than most people can keep up with. Yet no one has it all figured out. The technology is probabilistic by nature, its use cases are still emerging, and the standards for governance and trust remain unsettled. Still, the public conversation is filled with certainty. Each week brings bold predictions about the end of dashboards, the rise of generalist assistants, or the automation of entire professions.
This chorus of overconfidence is misleading. Underneath, enterprise builders and engineers know that ambiguity is the ground state of AI adoption and development. Progress requires experimentation, careful evaluation, and the humility to admit what we do not yet know. The problem is that the voices best equipped to balance progress with the present reality often get drowned out. A culture that rewards hype and projection over care and precision makes it harder for those voices to be heard.
Gender and Certainty
Social science research has long shown a gap in how confidence is expressed. Men are more likely to speak early and refine their thinking through the act of speaking. Women, on average, wait until they feel more certain before contributing. When it comes to AI, where few answers are final and little is certain, that dynamic works against women.
This is, of course, not a question of ability, but rather one of social conditioning and professional norms. Many women in engineering roles report holding back ideas until they feel fully formed, whereas their male peers tend to treat public speculation as part of the process. In a field defined by rapid iteration and uncertain outcomes, the willingness to “project into the unknown” becomes a valuable currency. If women are more calculated in public statements but less outspoken, the conversation risks tilting toward overconfidence and away from balance.
Why Female Voices Matter in AI
The gap in confidence is amplified by representation. Women remain underrepresented in the talent pipelines for computer science, machine learning, and data engineering. According to Women in Tech, women make up just 35% of the total tech workforce in the US. As a result, the pool of female voices in AI is smaller from the outset. That matters not just for fairness, but for product outcomes. Models trained on biased data already risk amplifying disparities. Without diverse technical perspectives to challenge assumptions, the systems we build are more likely to reflect the views of those who design them rather than a broader scope of data, perspectives, and information.
Visibility also matters for discourse. Public commentary influences how enterprises, regulators, and even consumers perceive the possibilities and risks associated with AI. If the dominant voices are disproportionately male and disproportionately confident, the industry narrative will skew in the same direction. Female engineers and leaders bring different perspectives not because they all agree on an alternative view, but because their participation broadens the range of what gets asked, tested, and valued.
Permission to Project
This is where the idea of “permission to project” comes in. As we have seen, speaking in public about AI does not require claiming to know every answer. Every industry has once been an emerging one, whether modern finance, digital advertising, even industrial manufacturing, and women have established themselves, bringing unique insights and innovation to each. When women in the technology sector voice their opinions and questions, they not only shape the conversation but also make it easier for others to step forward. Representation multiplies: one person projecting into ambiguity makes it safer for the next to do the same.
The AI industry will be stronger if more women are visible making claims, predictions, and statements as freely as men do. It will require structural work to grow the pipeline of female engineers and leaders. It will also require cultural work to reward humility, curiosity, and rigor over false certainty. In a moment where AI is defined by what we do not yet know, the voices that lean into ambiguity may be the ones the industry needs most.
