She Builds with AI: Women Shaping the Future
Jul 20, 2026 · 34 min · 10 segments
AI doesn’t fail women because it’s “bad at math.” It fails when the data behind it leaves women out, flattening real lives into a default user that never had to navigate widowhood, caregiving…
Shubhi RaoGuestJulia LachHostBut what I hear you saying is that it also is about like whose reality is actually present in the system.
What does gender data mean in practice and what do people often misunderstand about it?
You know, I often say women are not one homogenous group, right? We can show up as caregivers, as mothers, as employees, as women leaders, as survivors, as investors.
And our lens, our frame can be quite different, depending not only relative to men, but even amongst ourselves as women.
And so we really need to capture those experiences of when a woman becomes a mother.
Let's say, for example, what does that really mean to her economic well-being? How is she now thinking about is she going to take a career break? Is she going to have to think about setting aside funds for her child to go to college? What are some of the decisions she's going to make? across her life that is going to, in the end, affect not only just her economic well-being, but many of the decisions and choices she makes.
Another one would be around, if you think, and I said this the other day on another show, which was, you know, when you look at the great wealth transfer happening and women, you know, being the recipient of wealth, these large sums of money, but more in the latter part of their lives.
Well, the reality is that women live longer, but unfortunately, we end up spending a lot of money towards the back end of our lives on healthcare, which then means that we need a very different investment profile.
And whether it's breast cancer, whether it's menopause, even heart issues, again, we have to capture those lived experiences, not only just across a bucket of women, as I would say, but even women, let's say of my race, brown women may have very different health symptoms and issues relative to other races.
So It's really figuring out that, first of all, we do need gender data assets to address those unique lived experiences women have as a virtue of the different roles they play.
But then within that also, you need to really think about the segmentation and the cohorts that come about.
What would you say, which life events or decisions show most clearly why mainstream AI systems often do not understand women's realities well enough? You mentioned a few examples.
there are so many, but I'll go back to just unpacking the example I just gave a bit more on, you know, the wealth transfer.
So It's not only the fact that, you know, with the boomer generation now that retired a few years ago and we're in a place where this great wealth transfer is happening from predominantly the boomer men to the women.
And so these women are maybe in their late 60s, 70s, early 80s having to deal with widowhood.
But what I hear you saying is that it also is about like whose reality is actually present in the system.
What does gender data mean in practice and what do people often misunderstand about it?
You know, I often say women are not one homogenous group, right? We can show up as caregivers, as mothers, as employees, as women leaders, as survivors, as investors.
And our lens, our frame can be quite different, depending not only relative to men, but even amongst ourselves as women.
And so we really need to capture those experiences of when a woman becomes a mother.
Let's say, for example, what does that really mean to her economic well-being? How is she now thinking about is she going to take a career break? Is she going to have to think about setting aside funds for her child to go to college? What are some of the decisions she's going to make? across her life that is going to, in the end, affect not only just her economic well-being, but many of the decisions and choices she makes.
Another one would be around, if you think, and I said this the other day on another show, which was, you know, when you look at the great wealth transfer happening and women, you know, being the recipient of wealth, these large sums of money, but more in the latter part of their lives.
Well, the reality is that women live longer, but unfortunately, we end up spending a lot of money towards the back end of our lives on healthcare, which then means that we need a very different investment profile.
And whether it's breast cancer, whether it's menopause, even heart issues, again, we have to capture those lived experiences, not only just across a bucket of women, as I would say, but even women, let's say of my race, brown women may have very different health symptoms and issues relative to other races.
So It's really figuring out that, first of all, we do need gender data assets to address those unique lived experiences women have as a virtue of the different roles they play.
But then within that also, you need to really think about the segmentation and the cohorts that come about.
What would you say, which life events or decisions show most clearly why mainstream AI systems often do not understand women's realities well enough? You mentioned a few examples.
there are so many, but I'll go back to just unpacking the example I just gave a bit more on, you know, the wealth transfer.
So It's not only the fact that, you know, with the boomer generation now that retired a few years ago and we're in a place where this great wealth transfer is happening from predominantly the boomer men to the women.
And so these women are maybe in their late 60s, 70s, early 80s having to deal with widowhood.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.