When I was growing up in Madison, my father, Lendell Alston, made sure I knew the numbers. Not batting averages. The numbers that described what it meant to be a Black man in Wisconsin and Dane County: graduation rates, incarceration rates, income and wealth. He wasn’t trying to scare me. He wanted me to see the field clearly so I could decide how to play on it.
I’ve carried that lesson through more than 20 years of running businesses in this city. I’ve done design and video production, built Madison365’s first website, taught adults to code in YWCA Madison’s YWeb program, and today I work as a software engineer and help organizations put AI to work. So when I look at artificial intelligence, I do what my father taught me. I look at the numbers. Right now, they’re telling two very different stories at the same time.
The story we can’t ignore
Start with where we stand. According to the Federal Reserve, the typical white household in 2022 held about six times the wealth of the typical Black household and about five times the wealth of the typical Hispanic household. For both, the gap was more than $220,000. AI is about to pour new wealth into an economy built on that foundation.
Researchers at the McKinsey Institute for Black Economic Mobility estimate that generative AI could add close to $500 billion a year in new household wealth in the United States by 2045. That sounds like good news until you look at how new wealth flows. McKinsey found that Black households capture about 38 cents of every new dollar of household wealth. If that pattern holds, AI could widen the gap between Black and white households by $43 billion every single year. No one has run that same calculation for Latino families yet, but with a gap that size, there’s no reason to expect a different direction. Left on autopilot, AI widens the distance between People of Color and everyone else.
The second warning is about entry points. Stanford’s Digital Economy Lab has been tracking payroll records since ChatGPT arrived in late 2022. In its August 2026 update, employment for workers ages 22 to 25 in the most AI-exposed jobs sits about 19% below where it would otherwise be, and the main reason is that companies are simply hiring fewer young people. Software development and customer service are hit hardest.
Those first jobs are the rungs on the ladder. McKinsey points out that Black workers have relied heavily on gateway roles like customer support and office support to move into higher-paying careers, and those are exactly the jobs AI is reshaping. Researchers at the UCLA Latino Policy Institute found that Latino workers are overrepresented in fields at risk of automation. Different jobs, same squeeze. If the bottom rungs disappear and we haven’t learned to climb a different way, the ladder gets pulled up behind the people already at the top.
I’m not sharing this to scare anyone. I’m sharing it the way my father shared numbers with me at the dinner table, so we can see the field clearly.
The story that gives me hope
Now here’s the other side. When researchers from Stanford and MIT studied customer support agents who were given an AI assistant, productivity went up about 14% on average. For the newest and least experienced workers, though, the gains were around 35%. The most experienced agents barely moved at all.
Erik Brynjolfsson, who led that research, has pointed out that for most of the past 30 years, computers helped higher-skilled workers more than everyone else, which widened income inequality. This tool did the opposite. It took what the best performers knew and put it in the hands of the people still learning.
Read that again. A technology that helps the person with the least experience the most. For Black and Brown communities that have been shut out of networks, mentors and the unwritten rules of the workplace, that is a very big deal.
And we are not starting from behind. A national survey by Jobs for the Future found that 53% of Black respondents use AI tools daily or weekly, well above the 39% average across everyone surveyed, and more than a third had already used AI to get a better job. Microsoft and LinkedIn found that 84% of Latino knowledge workers use generative AI at work, compared with 72% of white workers. Slack’s Workforce Lab found that 43% of Hispanic/Latinx and 42% of Black desk workers had tried AI at work, compared with 29% of white desk workers. And growing Latino-owned businesses are adopting AI at about twice the rate of white-owned businesses, according to the Latino Donor Collaborative. People of Color aren’t behind on this. We’re out front.
So where’s the gap? Using a tool is not the same as owning it. In that same JFF survey, only 31% of workers had received any AI training from their employer, and the biggest barriers were time, cost and access. Using AI to polish a cover letter is a good start. Knowing how to use it to run a business, lead a team, or build something of your own is where the wealth gets created. That’s the gap I want this series to help close.
Why now, and not later
The World Economic Forum expects 39% of workers’ core skills to change by 2030, and two-thirds of employers say they plan to hire people with AI skills. PwC found that jobs in the U.S. requiring AI skills carried a wage premium of up to 25%.
Every big technology shift opens a window. I watched it happen with the web. The people who learned to build websites early weren’t smarter than anyone else. They just started before everybody else did, and that head start turned into careers and companies. The window for AI is open right now. It won’t stay open forever, and I don’t want Black and Brown communities standing outside of it, again.
Here’s the part I want you to hold on to: If we treat this as an opportunity instead of a threat, AI can be one of the rare shifts that actually narrows the gap. The research says the tool itself leans toward the learner. What it can’t do is make us show up.
Creating your why
Teaching adults to code at YWeb taught me something I still believe. Motivation outlasts talent. When the lessons got hard, and they always got hard, the reason you came is what got you back in the room the next day. But here’s what I saw over and over: The hardest part of learning to code wasn’t the syntax. It was the paradigm shift. You had to learn to think the way a computer thinks, breaking every problem into rigid, literal steps, and that is where most people struggled. AI doesn’t ask that of you. You talk to it in plain language, the way you’d talk to a sharp coworker, and it meets you where you are. That removes what I believe was the biggest barrier to success in programming and web development. Still, there will be moments when AI feels confusing, or when it gives you a wrong answer and you wonder why you’re bothering. Your why is what carries you through those moments.
Your why doesn’t have to be grand. It has to be yours. Here are three questions to help you find it.
Where does your time go that you wish it didn’t? Think about the emails, forms, reports, scheduling and paperwork that eat your week. That’s where AI can give you hours back.
Who is counting on you? Your kids, your parents, your customers, your coworkers, your congregation. Picture what changes for them if you get sharper and faster at what you do.
What would you do with five extra hours a week? Start the business. Finish the degree. Coach the team. Rest. There’s no wrong answer, but there should be an answer.
Now put it together in one sentence: I’m learning AI so that I can ______, for ______. Write it down and put it somewhere you’ll see it every day.
Here’s mine: I’m learning and teaching AI so that my community owns a piece of what comes next, instead of being on the receiving end of it.
This week, I’m asking you to do one small thing. Open a free AI tool like ChatGPT, Claude or Gemini and ask it to help with one task from your list. Draft that email you’ve been putting off. Plan this week’s dinners. Explain a confusing letter from your insurance company. See what happens.
In the next article, we’ll pull back the curtain on what AI actually is, how we got here, and why it’s less mysterious than it looks. Until then, find your why. Everything else builds on it.
