To say the least, many young graduates aren’t excited about working with AI. Instead, students — primed by tech executives who say their frontier labs stand to automate away many careers — are booing graduation speakers who bring up large language models (LLMs). Even computer science majors who still want to join the ranks of Silicon Valley face an uncertain future, since AI is rapidly reducing the number of coders that companies actually need.
Another incommodious dynamic is that women, disproportionately, seem to be biased against using the technology. There are myriad reasons for this apprehension: Many are anxious about AI’s capacity to make errors, or are turned off by AI’s energy demands and its potential to supercharge the already-massive influence of tech billionaires. As a result, there seems to be a gap in AI usage, particularly along gender lines.
Tarika Barrett, the outgoing CEO of Girls Who Code, knows her organization sits at the center of many of these tensions. When asked about uneasiness toward AI — particularly among women and girls — she says people shouldn’t disregard their real worries about the tech and should instead harness those concerns to guide their approach. “We have a deeply held belief that the quality of our technology, the future of AI in particular, depends on who’s going to build it,” says Barrett, who will be leaving the organization this summer. “It means that young people should be at the forefront, given its impact on every possible sector of our lives.”
This interview has been edited for length and clarity.
We hear a lot about vibe coding. When you think about Girls Who Code now, how do you think about coding itself?
Among the things we brought to market ... was actually vibe-coding programming for our young people. We saw it as yet another way to actually invite more people in ... This is a moment where we recognize that coding identity is shifting. At Girls Who Code, we’ve always been very nimble and are embracing it all. Yes, we’re going to expose our people to vibe coding, because that’s what you know is in the ether and they should understand what it looks like.
We’ve always, as an organization, been more than just code. It was always about the fundamentals of computational thinking. It’s problem-solving, it’s collaboration ... Every field requires people who understand how to use technology and how to leverage it ethically and effectively. We know it’s no longer enough to learn how to code to break into tech. So much of it is learning how to learn and having that kind of ethos. Because yes, some of it is vibe coding, but someone has to check that vibe coding, right?
How real is the AI gender usage gap?
The way we think about it is intention. We know what the data are telling us. We see studies, like from Harvard Business School, that say women are adopting AI tools at 25 percent lower rate than men ... But we also know that it’s not just about not using the tools just because we’re slower or don’t want to do it.
Women have reported feeling limited, feeling prohibited, and being uncertain about their employer’s AI policy. What happens when an employer has a policy that isn’t clear? The risk-takers are, like, let’s go. Folks who are approaching it with intention and care — less so, right? This often falls, I think, along gender lines. The other thing that was wild that we saw in our research was that participants were reporting that they were being actively discouraged from pursuing skill development that was unrelated to their work, which is really kind of crazy, because a lot of the AI stuff is out there. You’re kind of trying to get that information where you can. That was another barrier we saw.
Think about social capital: Part of why we built this organization and have reached 860,000 students is that we are all about that sisterhood, that social capital, those connections — which we know end up being the ways that people get their first job. But we’re seeing it play out also in AI adoption, and who has knowledge and how that knowledge gets shared. Respondents [to our survey] very much saw the value of mentors, but as they moved up in their career trajectory, it was really challenging to sustain relationships.
In the past, we heard a lot from tech companies about being more proactive about making sure that groups that are less represented in tech are getting a foot in the door. We have this new generation of companies like OpenAI, Anthropic, and other LLM labs. Do you feel like they’re similarly focused on getting girls and women into this industry?
We had our Alumni Advisory Council come to our office. It’s a group of twentysomething women ... they end up being kind of a huge resource for us ... If I listen to what they share with me about what they’re seeing, we’re not seeing that same passion that we saw before in terms of bringing everyone along. It’s not that I think that folks aren’t aware of it, but I think [it gets lost in] that AI arms race and initial [approach of], let’s just do it as quickly as possible. We’ll get to that after.
This is not exactly the thinking that’s going to mean we have the kind of technology that we want to see. When we’ve seen young people come to the table — or especially folks from historically underrepresented groups — we have tech that actually meets our needs. That’s why, for us, this past year, it’s been an interesting line to walk. We recognize that young people’s attitudes around AI are very mixed, and we have large swaths of young people who are not that excited. You’re seeing this in the data as well. We recognize that exposure is also really critical.
If we’re not careful, we could lose a whole generation of young people who were told that tech was the answer, right? Tech was infused in our schools and in our school system. It was all about partnerships with industry, because this was the future. If we’re not careful, those same young people are the ones who are going to opt out because they don’t think there are viable prospects. Their opting out would mean that we lose the opportunity to have the kind of technology that’s high-impact.
Hypothetically, maybe you’re talking as a young woman who is worried about some of the ethical challenges raised by AI. Maybe they have the environment or electricity use in the back of their mind. They’re worried about using AI and making a mistake at their job. What do you tell them? They feel a sort of paralysis about it.
I would say to lean into that a bit and know that that kind of paralysis or that concern is your superpower, because you are not going to use it willy-nilly ... It says something that a young person is thinking about the environmental impact and is a careful user, consumer, and decipherer of what they’re getting. Not every question is AI-worthy right now.
I would tell that person not to judge themselves too harshly around their reticence, because at the end of the day, that reticence is actually discernment. That means that the way they’re going to leverage this tool is going to be thoughtful, and they should actually seek out people who are thoughtfully using it as well.
Some of what we’re missing ... is for young people to have mentors who are bringing them along and actually talking to them about good use cases for whatever element they’re interacting with, or what it can look like to have a game-changing outcome with AI. We also don’t want them to fully opt out. If you’re deciding not to use it right now, maybe that’s for a good reason. But keep your eyes and ears open because the opportunities are there. If we don’t have their voices, we’re in trouble.
It sounds like the kind of conclusion that foregrounds your discernment in thinking about use cases.
It’s not something that you’re holding back on for no reason. If your gut is telling you, “Hey, I’m concerned,” listen to that. That’s something that — and especially women — are bringing to the table with AI ... That will probably be the thing that saves us when we think about deployment.
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