I hired an intern last summer named Maya. Bright kid, finance major, ambitious. On her second day, I gave her an assignment that usually takes new hires a full week: analyze five potential multifamily acquisitions and rank them by risk-adjusted return. She came back in 90 minutes with all five properties analyzed, ranked, and formatted in a presentation. My first thought was that she’d cut corners. But when I reviewed her work, it was thorough, well-reasoned, and caught details I would have looked for myself. “How did you do this so fast?” I asked. “I used Claude,” she said, like it was obvious. “I gave it the offering memos, asked it to extract key metrics, build DCF models, identify risks, and rank them by Sharpe ratio. Then I verified everything and added my commentary.” That’s when it hit me: The next generation doesn’t think of AI as a special tool they need to learn. They think of it as a basic utility, like Google or Excel. And the firms that treat their junior talent like they’re “too inexperienced” to use AI are going to get left behind. The Generational Divide in AI Adoption Here’s what I’ve noticed across dozens of CRE firms: there’s a perfect inverse relationship between seniority and AI adoption. Interns and analysts (0-2 years experience): Using AI constantly, for everything from research to formatting to learning new skills. Associates (3-7 years): Using AI occasionally, mostly for tasks they already understand well. VPs and Directors (8-15 years): Skeptical of AI, occasionally try it but don’t trust it. Managing Directors and Partners (15+ years): Either completely ignore AI or are very curious but don’t have time to learn it. The juniors get it immediately because they have no legacy workflows to unlearn. The seniors resist it because they’ve built their careers on expertise that AI might commoditize. And the middle is stuck trying to figure out which side is right. But here’s the uncomfortable truth: the junior person who’s fluent with AI is now more productive than the senior person who isn’t. That’s never been true before with any technology. What This Means for Summer 2026 If you’re hiring interns next summer, you need to be ready for a cohort that has been using AI for their entire college career. They’ve used it to write papers, solve problem sets, prepare for interviews, and build projects. It’s as native to them as smartphones. These interns will expect to use AI at work. If you tell them they can’t, they’ll either ignore you (and use it anyway on their personal accounts) or they’ll conclude your firm is behind the times. But if you embrace it, you can extract absolutely extraordinary productivity from junior talent. Here’s how: Give Them Structured AI Workflows on Day One Don’t wait until week three to introduce AI. On day one, hand them your prompt library and say, “Here’s how we use AI for common tasks. Start with these templates.” Show them examples of how your team uses AI for due diligence, market research, and financial modeling. Give them access to your tools. Set expectations: “We use AI to accelerate our work, not replace thinking. Always verify the output. Always add your insights.” When Maya joined, I gave her three example workflows on her first day: how to use AI for rent roll analysis, how to use AI for market research, and how to use AI to draft sections of investment memos. By day three, she was running them independently. By week two, she was improving them. Let Them Teach You This is the hardest mindset shift for experienced professionals: your intern might be better at using AI than you are. That’s okay. Actually, it’s great. Maya showed me prompting techniques I didn’t know existed. She taught me how to use AI to build sensitivity tables in Excel. She found a way to use Claude to cross-reference property data across CoStar and public records that was more thorough than what I’d been doing manually. If you’re secure enough to learn from your junior staff, you’ll get better faster. If you’re too proud to admit they know something you don’t, you’ll stay stuck. Redefine What “Entry-Level” Means Some years ago, my job was grunt work. Build the model. Format the deck. Pull comps. Aggregate data. It took 2-3 years before I was trusted with actual analytical thinking. That timeline is dead. AI does the grunt work now. Which means your junior talent can (and should) be doing higher-level work much earlier. Maya’s second project was analyzing whether we should enter a new submarket in Atlanta. Normally, I’d assign that to a VP-level person because it requires synthesis of market data, competitor analysis, and strategic thinking. But Maya had AI to handle the research and data assembly. Her