Microsoft is finally beginning to combine its consumer and commercial Copilot AI assistants into a single "super app" interface, starting with the Copilot and Microsoft 365 Copilot apps. Both personal and work accounts will be moved to the new unified app, which recycles the "Microsoft Copilot" name but features an updated app icon. The single app also means there won't be two annoying Copilot icons in the system tray or taskbar anymore.

"Starting with a fresh look, the updated app combines the best of Copilot chat and image creation, with the power of Microsoft 365 for your work," Microsoft said in its update announcement. The updates are …

Read the full story at The Verge.

A typical apartment building generates data on everything from leaking toilet flappers to tenant demographics, yet most of the real estate industry still runs its analysis by hand in spreadsheets. Fundrise co-founder and RealAI CEO Ben Miller argues the missing piece isn’t a better AI model but a proprietary data layer that general-purpose tools can’t replicate. Miller joined Ben Lorica on Generative AI in the Real World to cover how RealAI evolved from a data project into an AI-powered analyst tool, why the AI acts as an orchestrator rather than a financial calculator, which investment trends aren’t yet pricing in AI’s economic impact on real estate markets, and why he believes the trillion-dollar AI CapEx build is temporarily masking what will eventually be a deep trough in white-collar hiring.

About the Generative AI in the Real World podcast: In 2023, ChatGPT put AI on everyone’s agenda. In 2026, the challenge will be turning those agendas into reality. In Generative AI in the Real World, Ben Lorica interviews leaders who are building with AI. Learn from their experience to help put AI to work in your enterprise.

Check out other episodes of this podcast on the O’Reilly learning platform or follow us on YouTube, Spotify, Apple, or wherever you get your podcasts.

Transcript

This transcript was created with the help of AI and has been lightly edited for clarity.

00.20
Ben Lorica

All right. So today we have Ben Miller. He is the co-founder and CEO of Fundrise. And today, we’ll talk about their interesting AI application called RealAI, which you can find at RealAI.com. Their tagline is “Answer any real estate question in seconds.” And we’ll also talk to Ben about broader trends in the AI industry. And with that, Ben, welcome to the podcast.

00.48
Ben Miller

Thanks for having me.

00.50
So, I definitely want to talk to you about RealAI in more detail, but I think to take a step back. . . so broadly speaking, as I understand it RealAI does the job of a real estate analyst, but I think, Ben, for our audience, they don’t actually know what a real estate analyst does. So maybe if you can briefly describe what does a real estate analyst do?

01.16
Yeah. So our product’s designed for a professional real estate investor, an institutional real estate investor will typically buy large scale properties that are worth tens of millions of dollars. And then analysts, like any financial analyst, would [do due] diligence [of the] property, make a lot of financial pro forma, do different sorts of investment memos and then make a recommendation to buy or sell. And they also then manage the investment after you acquire it. And asset management is actually where most of the activity happens in terms of how you optimize for rents and occupancy and different financing. And so the real estate analyst’s job is to be the financial brains of the owner of the property.

02.07
And typically this person, when they go about doing their job, they have access to different data sources, they pull it together, they run some financial models, and then they write up recommendations, right?

02.24
Yes, exactly. That’s what the white collar worker today is like. . . manual CRUD operation. Right? So they gather data points from different reports and different online services they might have. They usually populate a spreadsheet cell by cell that way, and then they also will write up a memo. And that is all done by hand, so it’s sort of like handcrafted. And all of that work, at least the first draft, can be done by AI. And then the question becomes, “why our AI versus Claude or ChatGPT?”

03.05
As I understand it too, Ben, RealAI actually didn’t start out as an AI project. It started out as a data project because back in 2023, as I recall, you were starting to talk to me about wanting to start a data business of some sort for real estate. Right? So I guess, for our listeners, Ben, why does real estate generate so much data? I think I remember you telling me that actually, most people don’t know a typical apartment building throws off so much data, right?

03.40
So Fundrise is a fintech platform that democratizes investing into private assets. And one of our original investment classes was real estate, where we would have a fintech website and an iOS app, an Android app, and we would have millions of users and they invest through our platform, and we have a sort of Robinhood-type platform. So we have payment processing and a system of record for ownership, and all of the API services you need and microservices for that company. And then over time we started eating the value chain, and so we started heading from the investor towards the asset. And eventually we ended up where when we acquire the asset, we run the asset. We vertically integrated, so we’re the real estate company and the tech platform, and the fund manager. And as we acquired the asset, we discovered the underlying infrastructure for that asset had a lot of data in it. And the way that people in real estate do their work is sort of how we started. It’s all very manual.

04.53
Can you describe what this data is?

Yeah. So there’s a bunch of different kinds of data sets you care about. At the property there’s a lot of activity happening. So, a toilet might be running and that’s the water bill. You have all sorts of leasing activity. You have marketing funnels of ‘where does the tenant come from’? You have lots of activity around the building in terms of like, who lives there, who’s moving there, who’s leaving, what’s the rent of the property across the street, what’s being built? So there’s endless amounts of data that’s happening at any point in the real world. And that real-world data matters to the person who owns real assets in that location.

05.46
And so then, it seems like the traditional approach was to ignore all of this data? Is that right?

05.56
It had to be distilled so that a human being can pretty much only consume a certain amount of tokens per second, and that usually ends up in a spreadsheet. So what happens is, people at the property or property managers, or maybe there’s our market company, CBRE, will produce market reports and they turn all that data into a spreadsheet so that you might get a 12-month reporting or rent roll or a market report. And so they take a lot of data and they distill it or transform or aggregate it into very few metrics. And that was necessary. And then once a month you get emailed six reports or 12 reports, and those reports are in a sort of dashboard for how real estate people make decisions. And I appreciate this and this is so obvious that there’s just you know, those reports are both a distillation and there’s a lot of meaning lost in the transformations, and a lot of data just left behind because the system of record for real estate and for a lot of industries, really the accounting system. . . income statements and, you know, get operational data like a toilet flapper that’s leaking, and that’s not coming into the report. And how could it?

07.36
So basically, it sounds like what you’ve done is the classic digital transformation of this industry. If you look back to the data in 2023 to today, what is the evolution of this data set? What was in the original data set, and what have you added since then?

08.02
So we originally were thinking about how all this data could be really useful for the real estate industry, the original business we called Basis, and we were originally using it just for our own company. And, there’s sort of property data. If you’re going to make decisions in real estate, you care about change over time. So time series, and you care about comparative analysis. How is my property versus another property? How’s my neighborhood versus another neighborhood? My city versus another city? And so you need to have not just your own data, but you need to have the context.

08.42
You need to have the data of other properties that are in other places. And the entities in real estate are places, properties, and then people, which was that we had this sort of insight when we were in the data gathering property data and place data, is that there’s a data model in real estate which essentially is the property management system, which is basic accounting systems. That’s Yardi and Tratta, RealPage. . . And those accounting systems have an implied data model that is a property address and the unit, but inside the unit is a person that’s not in their data model. And a really big gap in their thinking. It’s sort of like digital marketing. Kind of like TV. . . you’re going to do it on CNBC, you can do it at 3 p.m., but you don’t know who is watching. And then the internet showed up, and you can actually then target with Facebook or, you know, Google. You can target people who have eggs for breakfast and like Mozart. So all of a sudden the people data becomes really critical. And the real estate industry didn’t have any people data. It wasn’t even part of their thinking. It’s not currently part of their thinking. So we started realizing, “Hey, we get the kind of granular people data you have in the marketing industry.” We bring that into the real estate industry because you care about who lives in a building and who’s leaving, who’s moving in, who moving in the neighborhood? Who’s moving into the city. Are they rich or are they poor? How old are they? What’s their gender? All this stuff. Do they have iPhones? Do they have Androids? So we started gathering up huge data sets of people data and then putting that in the same databases you have with real estate data and building out, and you can actually see that people matter to real estate and you can actually make really good insights, new kinds of data, correlations and stuff like that for the sector.

10.43
By the way, this is the kind of thing that the hedge funds have been doing for years. Right?

Right. Yeah. I was actually talking to a famous data tech guy, and we were on the phone, they’re like, you know, we sell our data to all these hedge funds, for like 50 big hedge funds in the world, which probably be Renaissance, and Jane Street, and Citadel, we’ve only ever had one real estate company by our data. I said, “Who’s that? Who’s that?” They said, “Blackstone.”

11.18
Oh, that makes sense.

So yeah, high-frequency traders are data hogs and they put their data into these massive data models, and they do a lot of data engineering and data transformation. And real estate people do it with Excel spreadsheets and do it by hand. And so there’s this journey that every industry goes through. . . real estate’s way back in the stone ages, where they still do everything by hand with people.

11.45
So the end result for RealAI. . . the back end data. . . most of it is structured, probably sitting in some sort of data lakehouse or warehouse, right?

We have two different kinds of data. We have a transactional database and we have one for rows and one for columns. Because we have a lot of data and we do a lot of compute. . . And because mostly people when they’re using AI are doing computations on the fly, we had to pre-compute a lot of stuff so that when people are asking questions about what’s happening, how much growth there is in the market or what’s happening with like rents or whatever the things are, a lot of that stuff has to be pre-computed every night. And that gets pre-computed in a Snowflake database. And we also do clustering algorithms, we clustered people by city and by block and by lots of different things so when people want insights it’s all pre-computed. And then that sits in Snowflake migrating to a Databricks-type database. And then there’s a separate database which is the transactional store that’s on the fly, that’s fast when people are asking questions and hitting our API.

13.00
So, let’s set aside the AI model and whatnot. For listeners who haven’t tried it, I recommend you go to RealAI.com. I use it once a month to monitor my properties. But basically then what happens is you have this chat interface and then I presume the AI model acts as some sort of reasoning layer and also obviously parses your intention and what you’re interested in, but then the heavy lifting is done by the data back end, correct?

13.38
Yeah. I mean, I think most people have gotten here where you do deterministic analysis, real and where you’re writing Python and doing SQL. . .

13.49
Yeah. Okay. So in your case, if you hallucinate there’s consequences, right?

Yeah. So the way we structured our application is that where there’s facts, there’s essentially a  field that goes and pulls it from the database. So it’s not coming out. It essentially says, “What was the rent for the last 12 months in this property or in this neighborhood?”

14.16
But the AI model is the one that translates that prompt into some sort of SQL query?

Yeah, but I mean, we used to use Claude and we moved to Vercel, but we have basically a software sandbox and it opens it up and does all that sort of. . . This is for real estate, for most people you say, well, you probably know how to do this, but if you’re going to do an analysis about your home or your property, you know, most people can’t write Python, most people aren’t good at doing SQL queries, plus they don’t have a good understanding of the the database schema. And so we built a semantic layer on top of it. And it really democratizes data science. Originally you said we were a data company, and then AI showed up and the AI sits on top of our data, and that made it a lot easier for people because to do the type of insights or analysis that otherwise they would have no idea how to do before.

15.28
But just to clarify, the AI is not doing any kind of financial modeling or calculation, right?

No.

There’s some sort of forecasting or financial model or some sort of financial computation involved, and that’s not being done by the AI?

Right. The AI is like an orchestrator, and has the tools, right? And the different tools it uses and the tools are the web search or the calculator. I think everybody’s gone here, where I think a year ago or something, people were using vector databases and RAG and we moved away from that because you just want to be where it’s deterministic analysis. You want to just use regular software, to put that tool in the hands of the AI.

16.21
So the hard thing here is really maintaining these data sets in many ways, right? So that is the key, because you have to gather or you have to have domain knowledge to understand what data you need and what data really matters. And, now you have a year or so of history of people using the app and you’re understanding, really what’s valuable.

What’s the hard thing? I mean, it just depends on who you are. We didn’t mean to do this, but luckily we have a lot of data. The data is proprietary. The data is really valuable. And so that gives us a reason to exist because what happened is that we built RealAI and we rolled it out, and Claude arguably made it obsolete. And this is a strategy question. I think the hard thing is trying to build the software business.

17.18
How can it be obsolete if it doesn’t have access to your data?

Well, because people have. . . it’s really hard to get them out of their. . . they’re sitting in Claude. Trying to get it. And the reality is that. . .

17.38
There’s no A/B test. They can’t tell that they can get better results if they. . . 

So, our product is really good at, let’s say seven of the 15 things a real estate person needs to do. But there’s other things they need to do that they know our products are not designed to do, and so they need to have a general purpose tool that’s doing all their stuff. And so the day before yesterday, we got approved to be in the Claude marketplace. So we built a connector, we’re building a plugin, and now we’ll go to where the customer is, which is Claude. That’s our current strategy. And you can see it’s getting better. Claude is not as good as ChatGPT at making it developer-friendly. But we can build what’s almost the same as RealAI.com in Claude. And that’s where we’re currently headed. And you and I talk about strategy. I think that’s actually a temporary strategy. I don’t think that’s where the market ends up. But, yes.

18.50
But obviously, as users interact with your app, there’s a lot of learnings there that you can leverage to improve the app itself or even maybe the model powering the app. But in other words, there’s a compounding loop. So the question is will Claude cut you out of that at some point?

I think the hard thing is always the people. I think the technology is actually at least. . . You know, I’m not an AI researcher at the frontier. The hard thing is really building a software product that people become addicted to, and the data engineering and data science and stuff, that’s just a grind, this is work. In the short term, we’re sort of conceding. . . I believe we should concede that the customer wants to be in Claude today, and they don’t want to be in a different application. Claude’s like the hot new hotness. But I think they’re going to get Claude fatigue within a year. I think that the real estate people are lagging the rest of the industry. Most people who’ve been using Claude for the last six, 12 months hit a lot of limitations for enterprise. And I think they’re going to want a harness that’s designed for real estate. But, in the meantime, we’re going to sacrifice some of the things we don’t get by having them in our app. And I actually think they’ll end up going back and forth between Claude and our app, because our app is better for real estate than Claude, but Claude is better for everything else. And the way that the Claude plugin and, and I think it ultimately ends up being a RealAI app in Claude. It wants to go back and forth between our app and their app and wants to be seamless to the user. But after that—and this is really a strategy question—after that, do we end up with an open source real estate fine-tuned, trained model instead of Claude in a year or two? I don’t know, but it seems that would be a good expectation.

21.23
At the end of the day, even if you open up that model, that model still needs the data, right?

At the end of the day, that open source model a year from now. . . when we rolled out Sonnet 5, it didn’t change. It didn’t get better for the customer. Like we just don’t need the Fable. . . it’s actually worse for the customer. So I think that once the open source models are comparable to—I’m just gonna say Sonnet 5 or whatever that generation is—the customer is not getting the premium they’re paying for. They just don’t need it.

22.05
By the way, there’s tons of companies now that focus on helping enterprises get to specialized AI. And basically there’s two branches, right? So there’s the post training branch which comes down to fine tuning and reinforcement, fine tuning, tons of startups there that are coming online. And then, there’s even companies that will help you pre-train models from scratch. I think the trend is toward the inevitable simplification of both things. Which will mean that a lot more of the compute will go towards specialized models like yours. And then less of the compute going to the. . .

22.52
I’m not as confident in that. . . I mean, I’m agnostic. I think we could end up in a world where Claude replaces Microsoft and everything’s inside Claude.

Yeah, it’s possible. But then, I think that the general purpose models increasingly are going to be perceived as too overkill for a lot of things.

23.15
Yeah, I think that’s true in theory, but the consumer is so sticky and it’s hard to get them to change their behavior.

Yeah. If it’s a consumer app. But most enterprise work is just back end. . .

23.31
But I’m dealing with an SMB not like a Fortune 500, generally. And the SMB, in a lot of ways, acts more like a consumer than like an enterprise. And so I hear what you’re saying, though.

23.46
Hey, I have a question for you as far as UX. You have an application where someone is asking a question. It could be an important question for the user. How do you communicate uncertainty or how do you say, “Hey, here’s an answer, but there’s a lot of caveats behind this answer” How do you do that in your case?

24.23
That’s very hard to do. I was just getting that feedback yesterday because even facts in the data world are not 100% facts usually.

24.33
There’s alternative facts.

Yeah. I mean there’s just methodologies that change the information. Is the rent growth a point to point 12 months ago? Is it a median? There’s just a lot of ways you can get to something that’s true, but not the same answer as someone else. But my actual experience with consumers is that they actually don’t care about that. It’s all really about perceived credibility. . .

25.16
So I guess in the UX then, the question is if there’s some sort of uncertainty or disagreement, do you surface that?

25.28
Yeah, we surface data coverage. We have a whole section in the response that’s about a meta analysis about the data and about where we made decisions. . .

25.39
Or do you say, “This property, we think the rent you can charge is this?” But it’s really more likely in this interval. It’s not a specific value. And here’s the reason why there’s an interval instead of a value.

26.00
AI is really good at that kind of caveats and adding context in and again like there’s. . .

26.12
But it’s a UX problem, though. You have to decide to surface it, right?

We definitely surface it. But I find that the user ends up like it’s really a journey. In the first phase, they’re trying to determine if the data is good, and then once they’ve decided the data is good, they don’t really care about the caveats. They’ve sort of internalized to them how to think about the data. And it’s really a problem in the beginning of a user’s use of it. It’s not really a problem once they become a power user.

26.50
So I think that that’s a UX debate that people will have, moving forward. Because I think basically, like I said, in terms of actual studies around decision-making, it does make a material improvement. Since we have you, we’ll close the discussion. And before I ask a question, listeners, this is not we’re not about to give you financial advice; we’re not financial advisors. So there you go. That’s the discussion. First, first thing then, you have access to all this data in real estate, right? So you have all the patterns, but you’re also obviously an investor in AI and AI startups. Right? So, obviously you’re seeing trends in AI that you think aren’t being reflected yet in the real estate market.

27.53
I know I think you’ve mentioned to me in the past, I think so, as an example, to make this concrete, I think you mentioned to me in the past that maybe if you’re buying property with the assumption that there’s a lot of back office workers moving forward and those back office workers are located in, I don’t know where.  . . Phoenix, Arizona. . . Maybe you should have second thoughts. Right? So what are some of the AI trends that you think are not yet being priced into real estate investing?

28.28
What’s good about the trends I’m seeing in the data I’m seeing is I think they’re consonant or consistent with what people would intuitively think. So, AI is causing—in the data—a lot more disparity in outcomes. And so in real estate something used to be a truism. I mean, people will say people always need a place to live. And so you would buy workforce housing, affordable housing, things where if you have housing that’s affordable. . .

29.04
Or always buying is better than renting.

It’s just like cheaper is better than expensive, right? If you can offer housing that’s less expensive, less expensive to file, it’s less expensive to rent. That’s good business. And that actually I think is breaking down, which is maybe unintuitive but also maybe intuitive. Is that where the marginal dollars are ending up in the hands of people who have a lot of money and the people who don’t have a lot of money, which is basically the bottom half of the country, or even maybe the bottom 90% of the country. . .

29.45
95%.

Maybe. You can break a lot of different ways. But the point is that, this K-shaped economy where the normal person is doing worse and the best are doing better is. . . AI is accelerating that trend. And that way that affects real estate is if you’re an investor, you want to actually focus on the high end. If you’re going to be building, you can be investing. . . high end essentially is price-insensitive and the low end. . .

30.15
Because the low end people have no disposable income.

The low end is under strain. And then what is happening in real estate is that strain is showing up as a political manifestation of controlling pricing. And so you can’t make money providing affordably priced housing because the government won’t let you. So the government will let you take essentially excess profits from the rich, but it’s becoming more and more challenging to invest in and build for the normal person, because the government’s intervening in all sorts of small ways that people don’t see around how permits are pulled and lots of impact fees and things like that. I mean, in San Francisco and LA and New York, you see obvious examples of that. But it’s happening everywhere. And in real estate, you usually make an investment over a five year period, maybe even ten years. And so the long term trend is the most important thing. And I think then you essentially say. . . this is terrible by the way, from a social point of view, the societal point of view is very negative. I’m not commending this trend,  I’m just saying that’s what’s happening on the ground.

31.40
What about this notion that certain hubs are optimized for a certain type of work? Is that something you think about?

31.54
Yeah. That’s the thing you were I talked about last time. San Francisco is obvious, and then you look at Charlotte. But certain types of people move to certain places to get certain types of jobs and work from home drove that middle income white collar worker to work remotely, or they moved to more affordable places like Charlotte, Tampa, and Orlando, all these places where it’s affordable to live, and they do middle office work, back office work, and that job is getting decimated by AI. In particular, a lot of cities where young people would move there. They’d move to an apartment building, they’d move to downtown. They’d have a new job in a middle office of a medium to big size company. Those jobs don’t exist today. And so they’re struggling with where to live and they have roommates or living at home. And so the real estate that used to be where young people moved to is struggling as a result.

33.08
So I took you on your first Waymo ride. And how about things like that? I mean, now I can Waymo from anywhere.

33.19
I tried to invest in Waymo and I was not yet successful okay. I reached out to the CEO I had 37 connections to and I got no love. But yeah, the Waymo thing is such a good example of diffusion, a diffusion problem because the technology is completely mature and you can’t get it rolled out in any of the. . . DC’s fighting it and Boston’s fighting it. And for it to really affect real estate, it needs to become as seamless as Uber and Uber rolled out way faster, way more aggressively. They obviously took more risk on the rollout. So, you know, if you said ten years from now, how does that affect the patterns of living? I think you’re going to see. . . I’ve looked at this a number of times, but I think it’s premature to make these investments. You want to be investing in the wealthy satellite towns of. . . if you’re doing San Francisco, which is a little bit challenging to the Athertons. But in Atlanta, it’s the Greenvilles. There’s a lot of great wealthy exurbs that have many miniature downtowns that are cute. And so I think you’re going to see these satellite cities become very, very successful as a result. But those satellite cities are going to be mostly for the wealthy. So again, it’s all about wealth, not about affordability.

34.50
All right. Closing question. You’re a CEO. I know you’ve talked about the impact of AI and automation on hiring. And you talk to other CEOs. So as best you can tell, set aside the headlines and the news accounts. What are you hearing from CEOs about AI’s impact on the workforce and hiring? Hiring in particular.

35.20
It’s more mixed than the headlines. I think I’d say it’s both true that we have needed less people, haven’t hired as many people and also have needed to expand as a result of AI. And so I think I’m hearing a “both” situation. So it’s really a question of what’s the net impact? And I feel like it’s too soon to call. I think on net it’s still net negative on hiring, but it’s too fuzzy for me to have a good call on it.

35.58
Do you have any sense whether or not all the stories we’re reading about. . . if it’s particularly challenging for new college grads and people looking for that entry level job?

36.17
We don’t hire those people, and we rarely ever did.

Because the stereotype is those jobs I can just use AI.

36.28
I think of it as this cascade effect. Where we hired hundreds of people over the years, and what happened was there’s this elevator dynamic or escalator dynamic, where the person who was an early grad becomes middle and then they make space for the next person.

36.52
The talent pipeline, right?

Right. And that dynamic. . . AI’s changed it so much because you just don’t need. . . the people who are at the upper middle are so effective. . . And actually, this is something everybody learned in 2020-2021—more people means less work gets done because you have to manage people and you’re needing meetings and remote work is really brutal for information transfer. And so it’s actually way more effective just to do it yourself with AI than it is actually to have five people. And I think it’s actually more satisfying. And so I believe I’m net negative on jobs in the white collar work. And we’ve invested in some data centers that are like $50 billion data centers. And when you go there, there are thousands of people on site working. I mean, it looks like we’re in China. . .

37.50
For now, while they’re building it.

So I think what’s happening is that the job market doesn’t seem as bad because of this trillion dollar a year AI CapEx build. But I think that’s a five-year build, not a 50-year build. And then I think that in the meantime, that’s coring out the white collar worker. And so I think on the other side of this, when the bubble finally bursts, it’s going to be a deep trough.

38.22
So then that talent pipeline . . . if you don’t have the entry level jobs who become the middle managers. . . and so on and so forth. . . So you have the entry level developer who gets more knowledgeable. . . then you don’t have that. Right? So you end up with what?

38.42
Your talent pipeline, it’s a thing. I’ve one of the things I’ve said to the team and this is harsh, but I’m going to say it, but it’s a brutal thing. There’s a lot of people in an organization who are good but aren’t going to be the next great people. And I said, “If we don’t have a talent pipeline anymore, you need to turn those people over faster.” Essentially, unless you were bad. . . bad people managed out, but if you’re good, we kept you. But if you’re great, you get promoted. But now if you don’t have this talent pipeline, you have to change what you expect of the good. You have to turn over some of them, and look for people who are good with the prospect of building that. . . So you have to change the talent pipeline or talent escalator because you don’t have as many people coming through as you used to. So it’s more of an up-or-out dynamic than it used to be.

39.42
Oh, I forgot one last question on RealAI, which is, I think you still do seat based pricing, right?

39.52
We don’t know how to price it.

So that’s it then? If I’m a real estate analyst, you’re charging me $50 a month, but I’m hammering this thing, costing you $1,000 a month.

40.04
No. The seat buys a certain number of tokens. So it’s more like it’s more like usage tiers, is how it’s priced today, but I don’t know how to price it. We’re still early in this evolution. I mean, we’re real estate. . . so real estate is going to be lagging other sectors in terms of adoption. And so I don’t know what the right way to price it is because it doesn’t want to be only usage, either, I think that’s not right. But I don’t know the answer.

40.41
But even the tiered pricing, if I’m on a token matter, at some point I’m already at your top tier price and I’m still busting through it.

40.52
Yeah, that’s a great situation though, because tokens aren’t that expensive. I know everybody’s obsessed with them, but I think tokens are the smallest part of our cost.

41.04
But if you’re charging me $200 at the very top tier, I’m actually spending so much compute. . .

41.15
I think that would be a great scenario to have in the future. . . for the few power users, that’s happening for us. Great. I hope they feel like they’re getting a good deal.

41.26
And with that, thank you, Ben.

Yeah. Thanks, Ben. Thanks for having me.

Great name, by the way.

---

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Less than 1% of healthcare venture capital goes to women's health. This isn't just a funding gap—it's a health crisis that affects 51% of the population. In this powerful kickoff to the "Invest Like a Girl" series, hosts Rebecca Love, Joy Rios, and Grace Vinton pull back the curtain on a system where men (who make up 85% of VC general partners) are making the vast majority of decisions about which health solutions get funded and brought to market.

The hosts dive deep into the startling statistics: women make 80% of healthcare decisions, yet conditions like menopause, endometriosis (which takes seven years to diagnose), and cardiovascular disease—the number one killer of women—remain chronically underfunded. They unpack the systemic biases, from male-based symptom models that delay cardiac care for women by an average of 35 minutes, to the social dynamics of "golf course" deal-making that exclude women investors.

But this episode isn't just about the problem. It's a rallying cry for action. With the largest wealth transfer in history moving to women over the next decade, the hosts explore how women can harness their financial power to reshape the future of healthcare, build generational wealth, and finally demand the innovations they deserve.

Key Takeaways

• The Funding Gap is a Health Gap: Less than 1% of healthcare VC goes to women's health. This directly correlates with delayed diagnoses, inferior treatments, and poorer health outcomes for women across the board.

• The 80% Rule: Women make 80% of healthcare decisions for their families but are almost entirely absent from the rooms where health solutions are funded. This disconnect is a massive market failure.

• The "35-Minute" Disparity: Because cardiac symptoms are modeled on male patients, women experiencing heart attacks face an average 35-minute delay in diagnosis—a delay that can mean the difference between life and death.

• The Trust and Bias Problem: Women investors face unique hurdles, from being told to "pitch like a man" to being excluded from informal networking and "male bonding" that often seals deals. This is compounded by a trust gap where founders and colleagues often default to male counterparts.

• The Wealth Transfer is a Tipping Point: The largest intergenerational transfer of wealth in history is heading to women. How women choose to invest—and whether they prioritize mission-aligned, women's health solutions—will reshape the economy and healthcare for generations.

• Convergent Care is Key: The hosts highlight the need for a "convergence" approach, breaking down historical silos (like the 100-year separation of dentistry and medicine) to treat the whole person.

Chapters & Key Moments

00:00 | Series Kickoff The hosts introduce a multi-part series on investing, fundraising, and the women's health funding gap, explaining why this conversation is critical right now.

00:30 | Why Investing Now Rebecca sets the stage by highlighting the shocking statistic that women represent only 10-17% of the investor market.

00:43 | Women Missing in VC The hosts expand on the lack of women in General Partner roles (less than 15%), pointing out that the same people are making decisions about products for a population they don't fully understand.

01:46 | Health Funding Gap Grace reveals that less than 1% of healthcare VC goes to women's health. This section breaks down the "90% problem"—where the tiny women's health pie is itself dominated by reproductive health, fertility, and women's cancers, leaving everything else behind.

04:15 | Underfunded Conditions The conversation turns to the "least attention" areas: menopause, endometriosis (and its seven-year diagnosis delay), PCOS, and cardiovascular disease—the number one killer of women that remains vastly underfunded.

05:37 | Data to Action Joy recounts a powerful conversation with investor Marissa Fayer, who highlights the "35-minute delay" in diagnosing women's cardiac events and issues a call to action: "We don't need to talk about the statistics anymore. It's time to take some action."

07:56 | Diversity in Dealflow & Bias in Fundraising The hosts discuss how the lack of diversity leads to blind spots. Rebecca shares a personal story of being told to "pitch like a man," illustrating the double-bind women founders face.

10:38 | The "Golf Course" Problem The conversation explores how informal male networking spaces can exclude women investors, forcing them to work twice as hard to build trust and perform due diligence, often lengthening the investment timeline.

12:04 | Women as Investors Joy shares her awakening about the existence of women's health-focused funds and real estate investing for passive income. She challenges the narrative that investing is "complicated" and only for men.

13:58 | Wealth Transfer Moment Rebecca highlights the JPMorgan study on the largest transfer of wealth in history moving to women. She poses the critical question: "What will women do when they control the assets of that large amount of dollars?"

14:53  | Financial Independence Joy shares her personal pride in being the first woman in her family to buy a house on her own, illustrating the liberating power of financial independence and the need to empower other women to do the same.

18:20 | Convergence Health Lens The hosts connect the conversation back to themes of "convergent care" showing how breaking down health silos is a critical part of solving the women's health funding puzzle.

20:46 | Impact Beyond Returns & Allies Wrap Up The hosts close by emphasizing that investment returns are important, but so are health outcomes and community impact. They end with a shout-out to male allies who are pulling women up through the ranks.

Download audio: https://www.podtrac.com/pts/redirect.mp3/pdst.fm/e/traffic.megaphone.fm/DHT9111565910.mp3

Most of us are familiar with one-to-one agent chats: private, direct interactions where rules are relatively simple. With only you and the agent, each moment has clear intent. You can ask follow-up questions, pause, or restart without worrying about how each exchange affects anyone else.

Collaboration in groups works differently. Every message competes for attention, and a reply that is helpful to one individual may interrupt five others. Effective collaboration is built on countless small social judgments and gestures: deciding when to respond or stay quiet, choosing how much to say, and using the conventions and affordances of the setting appropriately. Through direct guidance, trial and error, and observation, we learn the behaviors needed to keep communication manageable in busy workspaces.

As agents join us in these spaces, they need to adopt the same kinds of behaviors. In a collaborative space, it’s not enough for an agent to understand and respond to what was said, it must be able to participate without making the conversation harder to follow. Emoji reactions, threaded replies, and quoted replies are three key features of Teams conversations that agents can use to add value while keeping a conversation flowing.

Emoji reactions

Sometimes an emoji is enough: reactions let an agent acknowledge a message without adding another reply to the chat. A teammate might say, “Hey @agent, can you pull the latest sales report?” The agent can react with an right away to show it saw the request and is working on it. When the task is done, it can replace that with a . The status is clear and the chat stays quiet.

app.on('message', async ({ api, activity }) => {
  // React to the user's incoming message with 
  await api.conversations.addReaction(activity.conversation.id, activity.id, 'like');

  // ...and later remove it
  await api.conversations.deleteReaction(activity.conversation.id, activity.id, 'like');

  return;
});

Threaded replies

In channels, a thread is often the right place to continue, keeping discussion contained instead of pulling the whole channel along. Someone kicks off a code review in a channel, and the agent responds in the thread with its findings, keeping the main channel clean. Anyone involved can expand the thread; everyone else can scroll past undisturbed.

app.on('message', async ({ reply }) => {
    // reply() sends a quoted reply to right thread automatically
    await reply('This is a threaded reply to your message.');
    return;
});

Quoted replies

When we want to bring an earlier message back into focus, we quote it so everyone knows what we’re responding to, and agents can do the same. If someone asked a question ten messages ago, the agent can quote that message and answer directly. The chat keeps moving along, and no one needs to ask, “What are you responding to?” This is especially useful for asynchronous tasks. You might assign the agent to follow up on an incident and then move on, while the agent takes a few hours to finish the task. When it does, the quoted reply ties the response back to the original message, no matter where the conversation has gone.

app.on('message', async ({ quote, send }) => {
    const sent = await send('The meeting has been moved to 3 PM tomorrow.');
    await quote(sent.id, 'Just to confirm — does the new time work for everyone?');
    return;
}); 

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These patterns matter because they give agents more than one way to contribute while mirroring the conversation patterns we use every day. Without them, an agent’s default interaction model is simply another message in the conversation. Group collaboration often calls for something lighter: an acknowledgement, a contained follow-up, or a response anchored to the right moment. Reactions, threaded replies, and quoted replies offer agents ways to do this without always speaking up in the primary channel. They allow agents to participate without assuming every action deserves a new message.

Designing agents in shared spaces is not just about making them more capable. It is about helping them participate with social cues in mind. The best collaborative agents will not be the ones that respond the most, but the ones that know when and how to answer. You can start building these interaction patterns into your agent today with the Teams SDK and coding agent skill. To see these ideas in action, check out our Build demo session, Build agents where work happens: chats, channels, and meetings in Microsoft Teams.

The post Building Agents for Teams: Managing the noise of collaboration appeared first on Microsoft 365 Developer Blog.