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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. AI's use cases, whether

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Speaker 1: it's sending way beyond the office. Pressy Taste AI, for example,

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Speaker 1: a company bringing a power of artificial intelligence into the

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Speaker 1: restaurant space, helping manage crews even predict demand. We're joined

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Speaker 1: now by the CEO of that company in goos Stalk,

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Speaker 1: as well as Union Square Hospitality Group founder Danny Matt,

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Speaker 1: an investor in Press Taste. Danny, I start with you

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Speaker 1: because inlightened hospitality investments is where you've made this investment,

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Speaker 1: but you've actually got a plethora of AI investments. How

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Speaker 1: and at what point did you decide the artificial intelligence

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Speaker 1: was going to change up your game of restaurant.

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Speaker 2: Here we're facing, as we always have in our industry,

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Speaker 2: really a business model where we have pretty thin margins.

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Speaker 2: In the restaurant industry is notoriously competitive. But I think

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Speaker 2: what a lot of people may not realize is that

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Speaker 2: by the time you're done paying your rent and the

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Speaker 2: talent on the team, and the food costs and the

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Speaker 2: insurance and the florists and all the expenses that only

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Speaker 2: go up over.

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Speaker 3: Time, you're not lived with much.

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Speaker 2: And so we're constantly needing to find ways that we

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Speaker 2: can become more productive, and machine learning, it turns out,

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Speaker 2: is really really good at a number of things. It

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Speaker 2: helps us to not only have much better data with

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Speaker 2: which we can take much better care of our guests.

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Speaker 2: Buy the proper amount of food because we never want

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Speaker 2: to run out of food, and on the other hand,

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Speaker 2: we don't want too much food, have the right amount

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Speaker 2: of labor and talent on the floor, never want to

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Speaker 2: have too little, you never want to have too much.

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Speaker 2: We don't necessarily have the ability to think about that

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Speaker 2: and be very very present to offer great hospitality for

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Speaker 2: our guests. And so the more that we can find

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Speaker 2: outstanding applications for machine learning AI, what it does is

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Speaker 2: we get to be a lot smarter, but we also

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Speaker 2: get to be better at hospitality.

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Speaker 1: Which is precisely where you want to be. Getting a

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Speaker 1: sticking to that kitchen knitting. Meanwhile, Ango, you have been

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Speaker 1: applying AI in the real world for a long time,

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Speaker 1: whether it be in academia and we moved across to

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Speaker 1: really wanted to change up the back of kitchen. What

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Speaker 1: drew you there? And how quickly have you managed to scale?

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Speaker 2: Well?

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Speaker 4: We have I have been in machine learning. I did

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Speaker 4: my pH in machine learning, when I still need to

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Speaker 4: explain everyone what that is. But now I'm you know,

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Speaker 4: since a couple of months, almost like just two years,

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Speaker 4: where CHET, Chipte or Google AI other CHET agents came

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Speaker 4: to market, it is now a big, big hype. Everyone

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Speaker 4: you know has AI capabilities now in.

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Speaker 5: A chatbot, but the chetbot, you know, they have their limitations.

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Speaker 5: You cannot have a drive your car, you cannot have

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Speaker 5: it prepared your meal. You need specialized AI capabilities such

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Speaker 5: as you know, using computer vision to identify what is

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Speaker 5: going on machine learning predictions to that breaks down. Okay,

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Speaker 5: what does this weather forecast and this POS forecast and

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Speaker 5: this trade.

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Speaker 4: Area information mean for my kitchen right now in those

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Speaker 4: fifteen million increments, so my crew can do and deliver

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Speaker 4: the best to optimize those razor thin margins that exist

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Speaker 4: in the restaurant AI.

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Speaker 6: For this specialized AI, that's what prec taste AI brings

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Speaker 6: to the table. And it's basically also the missing puzzle

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Speaker 6: piece why software couldn't come into the kitchens. Kitchens are

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Speaker 6: often still run like in the fifties, and AI is

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Speaker 6: the missing puzzle piece that can help to bring true

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Speaker 6: help to those kitchen operations.

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Speaker 1: Danny, you've got some interesting puzzle pieces from investments. You've

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Speaker 1: got seven shifts, seven rooms, precy Taste, AI Converse, now

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Speaker 1: your amount who's often gone forward breaking boundaries when it

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Speaker 1: comes to tipping culture, when it comes to talking to

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Speaker 1: your own workplace, how they reacted to artificial intelligence with

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Speaker 1: fair with excitement.

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Speaker 2: Oh, with excitement. Everybody who we hire is somebody who

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Speaker 2: is a learn it all, not a know it all,

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Speaker 2: and we're all looking for ways we can learn to

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Speaker 2: be better. If you look at the different companies that

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Speaker 2: you're showing right here, each one of these is providing

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Speaker 2: the opportunity to make us better at the thing we

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Speaker 2: were doing anyway. So there's nothing there that we were

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Speaker 2: not doing before. It's just much quicker, much smarter, much

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Speaker 2: more productive. And everybody likes it because once you learn it.

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Speaker 3: By the way, when you get a new toy, it's

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Speaker 3: if I get a new update to my Apple phone,

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Speaker 3: my iPhone, it takes me a day to get used

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Speaker 3: to it, but once I get used to it, I.

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Speaker 2: Get exactly why they made those changes. So I don't

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Speaker 2: want to say that the first day we put something

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Speaker 2: in the practice, everybody goes so hallelujah. We have a

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Speaker 2: new reservation syst we have a new scheduling system, we

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Speaker 2: have a new way that we can talk to our guests,

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Speaker 2: a new way we can get to know our guests,

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Speaker 2: and in the case of Presi Tastes, a new way

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Speaker 2: that we can actually deploy our talent to be as

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Speaker 2: productive as possible per task. That's the thing I want

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Speaker 2: to be clear about Prezie Tastes, which is really cool,

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Speaker 2: is that it knows all of our recipes. It knows

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Speaker 2: exactly what we sold, what we're selling. It can actually

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Speaker 2: see how busy our restaurant is, and it can give

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Speaker 2: the kitchen in real time the predictions about how much

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Speaker 2: food you should be preparing to replace what just got sold,

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Speaker 2: how much food you should not be preparing, who should

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Speaker 2: do it. And it's the kind of thing that managers

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Speaker 2: have done an okay job of year after year. But

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Speaker 2: now those managers instead are taking their time to taste

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Speaker 2: the food and make sure it's perfect before a sauce

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Speaker 2: goes out, or they're taking the time to be with

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Speaker 2: our guests.

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Speaker 1: So I appreciate having you both. Pressie Taco Ingo Stalk

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Speaker 1: Union Square Hospitality Group Danny mar This is Bloomberg.

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Speaker 6: Yeah, I