Latest / Choppin’ It Up by Bloomberg Intelligence / SignalFlare’s Lukianoff on Gas, Fast Casual, AI

Transcript

SignalFlare’s Lukianoff on Gas, Fast Casual, AI · Choppin’ It Up by Bloomberg Intelligence

• 0:00
  Speaker 1: Welcome to Chopping It Up.
• 0:01
  Speaker 2: I'm your host, Mike Halon, a senior restaurant and food
• 0:03
  Speaker 2: service analyst at Bloomberg Intelligence. Our research and that a
• 0:07
  Speaker 2: bi's five hundred analysts around the globe can be found
• 0:10
  Speaker 2: exclusively on the Bloomberg terminal. If you enjoy the pod,
• 0:13
  Speaker 2: please do me a solid and leave us a five
• 0:15
  Speaker 2: star review on Apple or Spotify. Today we're joined by
• 0:19
  Speaker 2: Mike Lukiano, founder and CEO of Signalflare dot AI. Signal
• 0:24
  Speaker 2: Flare is a decision intelligence platform that translates data into
• 0:27
  Speaker 2: decision frameworks using AI and machine learning models.
• 0:32
  Speaker 1: Welcome back, Mike.
• 0:33
  Speaker 3: Hey, thanks for having me, Michael, always a pleasure, dude.
• 0:37
  Speaker 1: Congrats on being our first three time guest.
• 0:40
  Speaker 3: Hey I am. I am excited to carry that on or.
• 0:44
  Speaker 2: Yeah, I think number four, So thanks for supporting us
• 0:48
  Speaker 2: early on and then seventy six and right now we're
• 0:50
  Speaker 2: ninety nine, so getting close to one hundred.
• 0:53
  Speaker 3: All right, all right, congratulations on that. That's a lot
• 0:58
  Speaker 3: of episodes. Man.
• 0:59
  Speaker 1: Yeah, it's been fun.
• 1:01
  Speaker 2: Man, I can't believe it's been almost four years, but
• 1:04
  Speaker 2: time flies. But anyway, I keep having Mic on because
• 1:08
  Speaker 2: he's one of the few people that I actually trust
• 1:10
  Speaker 2: enough to let inform my research. I pride myself being
• 1:14
  Speaker 2: a contrarian and looking at things differently, and we often
• 1:19
  Speaker 2: see eye to eye, but we'll see right, Because what
• 1:23
  Speaker 2: I want to start off with is kind of telling
• 1:26
  Speaker 2: you where I stand in case you haven't been reading
• 1:31
  Speaker 2: my notes, and you know, I'd really like you to
• 1:34
  Speaker 2: let me know if I'm onto something or if I'm
• 1:37
  Speaker 2: you know, taking crazy pills. You know, a lot of
• 1:39
  Speaker 2: this is based around inflation, particularly in gas prices.
• 1:43
  Speaker 3: Are you ready to go? I am? I am ready?
• 1:46
  Speaker 3: Bring it on?
• 1:46
  Speaker 1: All right? Good stuff. So coming into the year, I
• 1:49
  Speaker 1: think you knew this.
• 1:50
  Speaker 2: I was pretty confident we'd see a first half improvement
• 1:53
  Speaker 2: in restaurant industry.
• 1:54
  Speaker 1: Seems sort of sales, right.
• 1:55
  Speaker 2: We had thirteen percent lower gas prices in one U,
• 2:00
  Speaker 2: we had higher tax refunds were coming. We were lapping
• 2:04
  Speaker 2: two years of very soft comps. Twenty twenty four was
• 2:08
  Speaker 2: very weak for casual dining. Twenty five was a week
• 2:11
  Speaker 2: for quick service. But two weeks after the war started,
• 2:15
  Speaker 2: I flipped parish right. Two of the legs of my
• 2:19
  Speaker 2: bullish stance were kicked out from underneath me. Right, So
• 2:22
  Speaker 2: gas prices went from being a tailwind to a massive headwind.
• 2:27
  Speaker 2: As you know, they have an oversized impact on perceived
• 2:30
  Speaker 2: inflation because there's billboards up and down every street broadcasting
• 2:35
  Speaker 2: the price and taed insult injury margins are going to
• 2:39
  Speaker 2: get hit due to the higher oil and fertilizer costs,
• 2:43
  Speaker 2: you know, as those start to bleed through. So does
• 2:46
  Speaker 2: that kind of jibe with what you're seeing and what
• 2:49
  Speaker 2: you think right now?
• 2:50
  Speaker 3: Yeah? So I think, as you know, all right, coming
• 2:53
  Speaker 3: into the year, you know, this is one of the
• 2:54
  Speaker 3: times where we where we stood in a little bit
• 2:57
  Speaker 3: of disagreement, right I was seeing much different a much
• 3:02
  Speaker 3: much different forecast, So you know, I was looking at
• 3:06
  Speaker 3: it maybe from a different vantage point where I'd seen
• 3:10
  Speaker 3: a lot of overbuilding and a lot of new construction
• 3:15
  Speaker 3: that was out of alignment with demand, particularly in a
• 3:19
  Speaker 3: lot of a lot of areas, so much overbuilding in
• 3:22
  Speaker 3: in in fast casual and in limited service, and and
• 3:28
  Speaker 3: population growth that had just completely flattened. So the way
• 3:32
  Speaker 3: that we measure the industry in terms of same store
• 3:35
  Speaker 3: same store sales just mathematically, there was no way that
• 3:42
  Speaker 3: that I could foresee that that that we could reach,
• 3:46
  Speaker 3: you know, a growth trajectory coming in the other the
• 3:49
  Speaker 3: other piece that was looking not very promising was that
• 3:54
  Speaker 3: we started to see a lot of consolidation in spending.
• 3:58
  Speaker 3: Even though spending was still looking pretty healthy, it was
• 4:01
  Speaker 3: consolidating so tightly into the top ten percent of income holders.
• 4:07
  Speaker 3: So as we started to see as as you mentioned, okay,
• 4:11
  Speaker 3: you know, great, you know great, you know, tax policy,
• 4:15
  Speaker 3: but that was also you know, just really going to
• 4:19
  Speaker 3: be beneficial to that same that same group primarily, So
• 4:25
  Speaker 3: that was just really really going to exacerbate you know,
• 4:28
  Speaker 3: that uh, that dimension, whereas you know, the restaurant industry
• 4:33
  Speaker 3: has a much much broader base. So you know, I
• 4:37
  Speaker 3: was not as confident, and we were already starting to
• 4:39
  Speaker 3: see it in uh, in the data that you know
• 4:42
  Speaker 3: that we have. So so I was already getting concerned
• 4:47
  Speaker 3: about that coming into the year, and I had you know,
• 4:49
  Speaker 3: I had done my own projections, which we were saying,
• 4:52
  Speaker 3: you know, look, we will be lucky to have you know,
• 4:55
  Speaker 3: negative two percent you know, uh comps coming into the year.
• 4:59
  Speaker 3: So before we got to March, you know, I was
• 5:01
  Speaker 3: already you know, looking at the numbers and that can
• 5:04
  Speaker 3: you know, we were sort of on target by the
• 5:06
  Speaker 3: time we got to March, you know, it's it started
• 5:10
  Speaker 3: to look a lot more dour. So I think I
• 5:12
  Speaker 3: think that's when we got sort of on the same page.
• 5:16
  Speaker 1: And population growth was another issue, right.
• 5:18
  Speaker 3: That's that's right. You know, population growth forecasts were already
• 5:22
  Speaker 3: looking you know, uh pretty you know, say, you know
• 5:28
  Speaker 3: below below projection in twenty twenty five, and then turned
• 5:34
  Speaker 3: some of the most amnemic population growth you know in
• 5:37
  Speaker 3: uh in years, and especially when you go when you
• 5:44
  Speaker 3: get local, So when when you look at you know,
• 5:48
  Speaker 3: some of the largest metropolitan areas in the country, which
• 5:51
  Speaker 3: are also some of the biggest restaurant markets. Uh, you know,
• 5:54
  Speaker 3: that's where you see you know, actually population growth turning
• 5:58
  Speaker 3: turning negative, that you've still got restaurant growth positive. So
• 6:03
  Speaker 3: you know that's where you've got your your ratios of
• 6:06
  Speaker 3: population to restaurants turning pretty pretty ugly.
• 6:10
  Speaker 1: All right, good stuff.
• 6:12
  Speaker 2: So the war, to your point, started at the end
• 6:15
  Speaker 2: of February. Energy price is spiked. We're currently looking at
• 6:18
  Speaker 2: gasoline prices more than forty percent higher. I can go
• 6:21
  Speaker 2: on for a while while the price about why the
• 6:24
  Speaker 2: price of oil isn't going back to seventy but you know,
• 6:27
  Speaker 2: I'll refrain what has been the impact of the gas
• 6:30
  Speaker 2: price jump so far on spending and what will the
• 6:34
  Speaker 2: impact be in the coming months.
• 6:37
  Speaker 3: Yeah, so, yeah, this is not the first time, you know,
• 6:42
  Speaker 3: we've we've seen you know, gas prices and its effect
• 6:46
  Speaker 3: on restaurants. So you know, in my years of doing this,
• 6:53
  Speaker 3: you know, it's it's something that's a known phenomenon. It's
• 6:56
  Speaker 3: it's it's it's highly predictable in in being able to
• 7:01
  Speaker 3: forecast restaurant traffic. And what's interesting about it is it's
• 7:11
  Speaker 3: not a one it's not a it's it's it's not
• 7:14
  Speaker 3: a one off phenomenon. Meaning when when there's when the
• 7:21
  Speaker 3: price goes up at the pump, there's the sticker shock,
• 7:25
  Speaker 3: and people will adjust their behavior at that moment. But
• 7:30
  Speaker 3: that's not when the real impact happens, right, the real impact,
• 7:35
  Speaker 3: you know, the vast majority, you know, and now it's
• 7:37
  Speaker 3: you know, you know, in the ninety percent dial range
• 7:42
  Speaker 3: of gas purchases happen on credit. So and that compounding effect,
• 7:50
  Speaker 3: you know, really starts to happen six to eight weeks
• 7:54
  Speaker 3: later when it actually starts to hit people's propensity to spend.
• 8:00
  Speaker 3: So you know, so you need to count forward from
• 8:05
  Speaker 3: from March, and when people start getting the bills and
• 8:09
  Speaker 3: paying the bills, and then the second, you know, second
• 8:12
  Speaker 3: credit cards start to come in, this is going to
• 8:14
  Speaker 3: start to compound. So you know, all of that points
• 8:19
  Speaker 3: to yes psychological impact of prices and seeing that at
• 8:25
  Speaker 3: the pump out right, you know, consumer sentiment. Boy, maybe
• 8:30
  Speaker 3: I got to change my behaviors, so on and so forth. Right,
• 8:32
  Speaker 3: that's the psychological impact. But when it hits your wallet
• 8:37
  Speaker 3: and it starts to change what you can actually do,
• 8:39
  Speaker 3: and when you match that with people's savings rates from
• 8:41
  Speaker 3: where they were already struggling, and that's that's where summer
• 8:46
  Speaker 3: starts to get really really grim.
• 8:49
  Speaker 1: Yeah, not good news. You know.
• 8:53
  Speaker 2: I remember when they were telling me, and I don't vote,
• 8:55
  Speaker 2: by the way, I feel like it's a humiliation ritual.
• 8:59
  Speaker 2: But I'm rem I remember being told that, you know,
• 9:03
  Speaker 2: people were voting for Trump because they were going to
• 9:05
  Speaker 2: get lower gas prices and less inflation. So that really
• 9:10
  Speaker 2: hasn't materialized.
• 9:12
  Speaker 1: You know, do you think.
• 9:13
  Speaker 2: That timeline of six weeks is going to be impacted
• 9:16
  Speaker 2: by the larger tax refunds on one hand or the
• 9:19
  Speaker 2: speed of the price increase on the other.
• 9:22
  Speaker 3: Well, I think that you know, look that there's there
• 9:25
  Speaker 3: are certain pockets of the population that are you know,
• 9:29
  Speaker 3: somewhat resilient you know, to to these price changes. So,
• 9:33
  Speaker 3: you know, and it goes back to you know, the
• 9:36
  Speaker 3: the K shaped economy, right, you know, the people who
• 9:41
  Speaker 3: are at the lowest tier of the economy. You know,
• 9:45
  Speaker 3: you know, I would say actually that those people dropped
• 9:49
  Speaker 3: out of restaurants, you know, a year and a half ago,
• 9:53
  Speaker 3: you know, some of them started to come back in
• 9:57
  Speaker 3: on deep discounting. You know, so we're starting to see
• 10:00
  Speaker 3: you know, okay, you know, some of the qs rs
• 10:02
  Speaker 3: from the discounting are actually starting to do a little
• 10:05
  Speaker 3: bit better because they're you know, they're they're able to
• 10:08
  Speaker 3: do that. But they're also benefiting from a trade down
• 10:12
  Speaker 3: from the fast casuals. Right, so we're seeing that it's
• 10:15
  Speaker 3: really the middle that is falling out either trading down,
• 10:21
  Speaker 3: trading out, you know, fewer visits and so forth. The
• 10:25
  Speaker 3: the high end. You know, if if you're you know,
• 10:30
  Speaker 3: two hundred thousand plus, you're probably still doing okay. And
• 10:37
  Speaker 3: the tax you know, the tax changes on you know,
• 10:41
  Speaker 3: check tax policy is benefiting you. The other thing that
• 10:45
  Speaker 3: might benefit you is you know, hey, oil prices you
• 10:49
  Speaker 3: know at ninety you know, if you're you know in
• 10:52
  Speaker 3: or around Houston, some parts of Colorado, right some you know,
• 10:56
  Speaker 3: some places that you know, you could be doing really
• 11:00
  Speaker 3: really well, you know in in some places in the
• 11:03
  Speaker 3: US where we're a net you know, a net exporterer, right.
• 11:06
  Speaker 3: So it's not all bad for for the US. You know,
• 11:11
  Speaker 3: in fact, it can be very very good in some
• 11:14
  Speaker 3: in some community. So you need to know what industries
• 11:16
  Speaker 3: you're in the The other wild card that we need
• 11:19
  Speaker 3: to think about, right is, you know, a year ago,
• 11:23
  Speaker 3: you know, I was talking about how bad UH tariffs
• 11:27
  Speaker 3: are going to be, you know, on on the overall
• 11:29
  Speaker 3: UH economy, in particularly for you know, potentially inflation. Well,
• 11:35
  Speaker 3: now tariffs might be our savior because there is uh
• 11:39
  Speaker 3: what one hundred and eighty billion dollars that's now has
• 11:47
  Speaker 3: to be refunded thanks to the Supreme Court, you know,
• 11:51
  Speaker 3: saying you know, hey, tee it up, that wasn't legal.
• 11:55
  Speaker 3: You got to give it back. So that could be
• 11:58
  Speaker 3: a huge stimulus that ending on how quickly they're refunding
• 12:02
  Speaker 3: those tariffs go back into the economy, so you know,
• 12:06
  Speaker 3: counter to you know, and again you know, not not
• 12:09
  Speaker 3: picking a political dog in this fight. But you know,
• 12:14
  Speaker 3: counter to what the purpose of you know, the tariffs were,
• 12:17
  Speaker 3: if that refund goes in, you know, that might actually
• 12:19
  Speaker 3: be the economic savior.
• 12:22
  Speaker 2: Yeah, I mean, do you really think that like a
• 12:25
  Speaker 2: Walmart is going to actually start cutting checks the consumers?
• 12:28
  Speaker 3: Right, Well, it's not necessarily going to the consumers, but
• 12:31
  Speaker 3: it might stop some layoffs, gotcha, you know, might might
• 12:34
  Speaker 3: help the hiring. Right. It's that's a lot of money
• 12:38
  Speaker 3: you know, to go back into in the stimulus. It
• 12:40
  Speaker 3: might help some of these you know importers who you know,
• 12:44
  Speaker 3: who otherwise could have gone out of you know, gone
• 12:46
  Speaker 3: out of business. Right. It changes the trajectory on a
• 12:48
  Speaker 3: lot of uh, you know, on a lot of you know,
• 12:52
  Speaker 3: on a lot of importers.
• 12:53
  Speaker 2: What regions of the country were hit hardest by the
• 12:55
  Speaker 2: gas right, So interestingly, the biggest spike in gas prices
• 13:01
  Speaker 2: was the South and the South actually.
• 13:05
  Speaker 3: You know, when we look back in you know, the
• 13:09
  Speaker 3: past several several years, that was actually the you know,
• 13:16
  Speaker 3: the you know, restaurant fortress, right. You know, they they
• 13:20
  Speaker 3: tended to do the best, you know, they they tended
• 13:22
  Speaker 3: to not be tracking is as high with with inflation
• 13:26
  Speaker 3: and so forth. So it's going to be really interesting
• 13:29
  Speaker 3: to see with their bigger spike in uh in gas prices,
• 13:35
  Speaker 3: how well, you know, how how well they they hold
• 13:38
  Speaker 3: up to to this much larger spike in uh in
• 13:42
  Speaker 3: gas prices, and whether that you know, continues continues on.
• 13:46
  Speaker 3: I'm not sure why you know they they got the
• 13:48
  Speaker 3: bigger spike in uh in gas price inflation, but but
• 13:52
  Speaker 3: it was interesting to see in the CPI report.
• 13:55
  Speaker 2: And those consumers are doing more driving than other regions
• 13:57
  Speaker 2: of the country too, right.
• 13:58
  Speaker 3: That's right. So that's that's gonna that's going to compound.
• 14:02
  Speaker 1: And what segments of the industry do you think are
• 14:04
  Speaker 1: most exposed.
• 14:05
  Speaker 3: I think I think the I think fast fast casual
• 14:09
  Speaker 3: is is rough. I think that you know, despite what
• 14:14
  Speaker 3: I think, how well a couple of the casual diners
• 14:19
  Speaker 3: you are doing, I think that that's not it's not
• 14:23
  Speaker 3: necessarily a safe space. I think what's interesting there is that,
• 14:29
  Speaker 3: you know, the ones that are doing really well have
• 14:32
  Speaker 3: managed to really show what that what that value proposition
• 14:39
  Speaker 3: is because the you know, the fast casuals were really
• 14:44
  Speaker 3: creeping up on price to a full service, you know,
• 14:48
  Speaker 3: sit down experience, and when that when that when that
• 14:53
  Speaker 3: gap gets too narrow and people say, well, I could
• 14:55
  Speaker 3: just come in and sit down and be waited on. Uh,
• 14:59
  Speaker 3: you know, and and the gap is not that much different,
• 15:02
  Speaker 3: Like why am I standing in front of a kiosk?
• 15:06
  Speaker 3: What's the experience that I that I'm actually looking for? Right?
• 15:09
  Speaker 3: Or for that matter, you know, which food do I
• 15:12
  Speaker 3: want when I want to delivered to my house and
• 15:14
  Speaker 3: when I'm paying you know, thirty dollars to get a
• 15:16
  Speaker 3: burrito delivered, maybe I want a full service you know,
• 15:20
  Speaker 3: delivered from you know, from a casual diner. Right, So
• 15:23
  Speaker 3: the ways that people are making choices about what type
• 15:26
  Speaker 3: of food that they want come into their house also
• 15:29
  Speaker 3: also matters.
• 15:30
  Speaker 2: Now, yeah, and Bringers doing a lot of things, right,
• 15:32
  Speaker 2: But you know, Chili's new chicken sandwich is eighty percent
• 15:35
  Speaker 2: bigger than you know, what you're going to get at
• 15:37
  Speaker 2: a at a fast food joint for ten ninety nine
• 15:39
  Speaker 2: with a drink and bottomless chips. I mean, there's a
• 15:43
  Speaker 2: lot of value there, and it's and you know, one
• 15:45
  Speaker 2: of the reasons why they've done so well. You know,
• 15:48
  Speaker 2: which chains historically are most insulated from the higher gas price.
• 15:53
  Speaker 3: I think it depends a lot on not just region. Right,
• 15:57
  Speaker 3: So you know, if you know, if you're if your customer,
• 16:03
  Speaker 3: you know, to your to your point about regions where
• 16:05
  Speaker 3: people drive more, right, if you've got fewer commuters and
• 16:08
  Speaker 3: and so forth, Right, who you know, who aren't you
• 16:11
  Speaker 3: know driving as much? Who are higher income? Uh? You know?
• 16:15
  Speaker 3: Then then from at least a consumer standpoint, you're you're
• 16:21
  Speaker 3: going to have less less issues you know with you know,
• 16:24
  Speaker 3: with with people with less money in their pocket. But
• 16:28
  Speaker 3: we can't forget that they're not immune. These restaurants are
• 16:33
  Speaker 3: not going to be immune to the the cost inflation
• 16:38
  Speaker 3: of what's going to come in their back door. Right.
• 16:41
  Speaker 3: And and it's not just the you know, the cost
• 16:45
  Speaker 3: of goods, right, there's going to be you know, delivery
• 16:47
  Speaker 3: surcharges and all those things are going to start rearing
• 16:50
  Speaker 3: their their their head again, you know, for all the
• 16:52
  Speaker 3: cost of goods coming in the back door. The timing
• 16:56
  Speaker 3: of what happened in the Gulf is also critical because
• 17:00
  Speaker 3: you can't forget that you know, this drives fertilizer costs
• 17:06
  Speaker 3: to right and going into planting season. So you know,
• 17:11
  Speaker 3: so what does that mean for what the cost of
• 17:14
  Speaker 3: all food is going to be going into the fall right,
• 17:16
  Speaker 3: all of these trickle down costs. So you know, I'm
• 17:20
  Speaker 3: hearing people going consumer's week, traffic is down. Look, traffic
• 17:26
  Speaker 3: was going to be down. Traffic is going to be down.
• 17:31
  Speaker 3: Another fact, your costs are going to be up. If
• 17:36
  Speaker 3: restaurant tours are saying we're going to keep our prices,
• 17:42
  Speaker 3: you know, steady because we're worried about the consumer. I
• 17:46
  Speaker 3: get that, you know, yes, you know, be very cautious
• 17:49
  Speaker 3: about it. But this is a needle. You got a thread, right.
• 17:54
  Speaker 3: If you know that you're going to lose customers because
• 17:58
  Speaker 3: of price, be really stick about the customers that you're
• 18:01
  Speaker 3: going to lose because of price. But if you're standing still,
• 18:06
  Speaker 3: you know, are are you just doing more damage to
• 18:08
  Speaker 3: yourself or do you still need to be able to
• 18:11
  Speaker 3: very cautiously figure out where and how you're going to
• 18:15
  Speaker 3: adjust your prices. Otherwise you're going to be playing catch
• 18:18
  Speaker 3: up later on and trying to make up for lost ground. Right,
• 18:22
  Speaker 3: this is not the time to be saying, you know,
• 18:25
  Speaker 3: and you know to the same point, right, And it's
• 18:28
  Speaker 3: because we were talking about AI and the power of
• 18:31
  Speaker 3: Claude and you know, and and uh, you know, everybody
• 18:34
  Speaker 3: is an expert in coding now, right, It's like VOD code.
• 18:37
  Speaker 3: I can build it myself and all this right, and
• 18:39
  Speaker 3: you know, and everybody's a you know, everybody's a data
• 18:44
  Speaker 3: scientist now because you can you know, you can make
• 18:47
  Speaker 3: Claude you know, run a price elasticity model. Right. It's
• 18:50
  Speaker 3: like I know, you know, it will answer any question
• 18:55
  Speaker 3: for you, right, and it will do anything out of
• 18:56
  Speaker 3: that magic box. But wow, you know, what do you
• 19:01
  Speaker 3: actually get? So you hear everybody you know and their
• 19:03
  Speaker 3: brother saying like, oh, I'm taking this in house, or
• 19:06
  Speaker 3: I'm going to do this with the magic box, right,
• 19:08
  Speaker 3: or I'm going to get like my own you know,
• 19:11
  Speaker 3: data scientists to do it for me. Is that the
• 19:14
  Speaker 3: smart thing to be doing like right at this moment, right,
• 19:18
  Speaker 3: And I think that's something to to really you know,
• 19:21
  Speaker 3: be probing right as you ask these uh you know,
• 19:24
  Speaker 3: as you ask these people, because because it is is
• 19:27
  Speaker 3: across the industry, right, everybody's thinking, you know, hey, these
• 19:31
  Speaker 3: magic tools, right, I can now do everything myself.
• 19:34
  Speaker 1: It sure feels like.
• 19:36
  Speaker 2: A genie in the bottle from the programming side anyway.
• 19:39
  Speaker 1: But you know, to your point, you know, when.
• 19:42
  Speaker 2: I tell it, tell Claude to analyze restaurant stocks. It's
• 19:46
  Speaker 2: it's not very good, you know.
• 19:48
  Speaker 3: So you know, be's confident though, isn't it. It says
• 19:53
  Speaker 3: it beautifully, It really does, it really does.
• 19:57
  Speaker 2: So now I'm more so having it aggri gate news
• 20:00
  Speaker 2: for me and spit that back to me, and and
• 20:03
  Speaker 2: and things of that nature, just because I'm very careful about,
• 20:07
  Speaker 2: you know, making sure that that I'm putting out accurate research. Right,
• 20:12
  Speaker 2: I'll have it analyzed documents for me. I'll have an
• 20:15
  Speaker 2: advert care about I'm like that, I don't know, but
• 20:20
  Speaker 2: I do so, yeah, yeah, no, But what you mentioned
• 20:28
  Speaker 2: was really important in you know, in terms of raising prices, right,
• 20:32
  Speaker 2: because we got a lot of chains coming into the
• 20:34
  Speaker 2: year when inflation's you know, slowly going down back down
• 20:37
  Speaker 2: to two percent, finally saying that we're not raising price
• 20:41
  Speaker 2: this year. We're gonna you know, hold the line, and
• 20:44
  Speaker 2: we're going to try to just be more efficient with
• 20:46
  Speaker 2: our labor, you know, and by doing better scheduling and
• 20:50
  Speaker 2: reallocating labor to when we really need it, right, And
• 20:54
  Speaker 2: now all of a sudden, it looks like we could
• 20:57
  Speaker 2: be back at four by the time the year is over, right,
• 21:02
  Speaker 2: And so you know, just the question I have is
• 21:06
  Speaker 2: because I hear a lot of people, even people that
• 21:08
  Speaker 2: make money, saying I'm cutting back at restaurants because it's
• 21:11
  Speaker 2: just so damn expensive. Right, So, like you know, I
• 21:16
  Speaker 2: obviously this is a case by case basis, This is
• 21:19
  Speaker 2: an item by item, store by store basis.
• 21:22
  Speaker 1: That these increases have to come through.
• 21:25
  Speaker 2: But you know, I don't see many companies that I
• 21:28
  Speaker 2: cover being able to push through more than two percent
• 21:31
  Speaker 2: this year at all.
• 21:33
  Speaker 1: I mean, I guess, you know, I guess this is
• 21:36
  Speaker 1: where signal flare comes in.
• 21:37
  Speaker 3: Right, Yeah, Well and look and and that's okay, right,
• 21:41
  Speaker 3: you know it's yes, it is a time to be
• 21:44
  Speaker 3: to be conservative and selected, selective and you know, and
• 21:48
  Speaker 3: and pointed and surgical. Right. So yeah, you need to
• 21:53
  Speaker 3: you need to understand your trade area. You need to
• 21:55
  Speaker 3: understand your market. You need understand your consumer better and
• 21:58
  Speaker 3: your competitors. But part of it is is understanding what
• 22:01
  Speaker 3: your baseline is, right, And I think that's where a
• 22:05
  Speaker 3: lot of there's there's a there, you know, whether it's
• 22:09
  Speaker 3: with us or or without us. Right, it's it's trying
• 22:12
  Speaker 3: to start with this, you know, fundamental attribution error. Right,
• 22:18
  Speaker 3: Like you know, I think that you know, I remember
• 22:23
  Speaker 3: going coming out of COVID and going you know, as
• 22:27
  Speaker 3: as prices were going up universally, right, and I heard
• 22:32
  Speaker 3: everybody talking about, you know, their price elasticity, you know,
• 22:36
  Speaker 3: and and it didn't exist, right, we got no price elasticity,
• 22:39
  Speaker 3: like really, are you sure? Right? And and really they
• 22:45
  Speaker 3: were just reading, you know, prices are going up, you know,
• 22:50
  Speaker 3: and you know, and consumers were still spending, right because
• 22:54
  Speaker 3: prices were escalating so fast. And then they were measuring
• 22:58
  Speaker 3: you know, correlation is not causation, right, So they have
• 23:01
  Speaker 3: these two things going up simultaneously. And look at and
• 23:04
  Speaker 3: I know what those models were because I, you know,
• 23:07
  Speaker 3: I built them. I didn't know what the flaws in
• 23:08
  Speaker 3: them were, right, and that's when I started to reconstruct
• 23:11
  Speaker 3: even know how they were built, right, So you know,
• 23:15
  Speaker 3: you know, price elasticity is not the same thing as
• 23:18
  Speaker 3: pricing power, right, And you know, maybe I taught a
• 23:23
  Speaker 3: good swath of the industry, you know, to look at
• 23:26
  Speaker 3: a measure of you know that wasn't forward looking, right,
• 23:32
  Speaker 3: and you know, re educating on what the other measures
• 23:36
  Speaker 3: are that need to be forward looking to make a
• 23:38
  Speaker 3: forward looking decision. You know, is is not an easy
• 23:42
  Speaker 3: is not an easy thing to do, right, because you know,
• 23:45
  Speaker 3: it's like you're showing me a price elasticity, but then
• 23:48
  Speaker 3: you're telling me I've got low pricing power. It's like, yeah,
• 23:51
  Speaker 3: I know, it's a hard it's a hard thing to
• 23:53
  Speaker 3: you know, it's a hard thing to explain. But you know,
• 23:57
  Speaker 3: this one is actually incorporating some forward looking things, right.
• 24:01
  Speaker 3: But it's maybe the most important thing that I've I've developed,
• 24:04
  Speaker 3: you know, in the past several years, and it has
• 24:08
  Speaker 3: more meaning and times like now, right, And it was
• 24:10
  Speaker 3: developed you know as I was going and watching people
• 24:13
  Speaker 3: saying I've got no you know, I've got no price elasticity,
• 24:17
  Speaker 3: you know, because they were riding a wave of price
• 24:20
  Speaker 3: increases when it was easy to increase prices. Now we're
• 24:23
  Speaker 3: on the other other side of that again, right, and
• 24:25
  Speaker 3: people are saying, Okay, prices are going up, you know,
• 24:28
  Speaker 3: and traffic is looking ugly, so you know, there's no
• 24:33
  Speaker 3: there's no pricing power at all. Well, I get what
• 24:37
  Speaker 3: you're saying. Consumers are falling out, but there are still
• 24:43
  Speaker 3: consumers that are coming, right, So what are the other opportunities.
• 24:46
  Speaker 3: It doesn't necessarily mean price increases, right, but it does
• 24:49
  Speaker 3: mean how do you make sure that you've got the
• 24:52
  Speaker 3: right value for the remaining customers. And with it, with
• 24:56
  Speaker 3: those customers, how do you try to get more you know,
• 24:59
  Speaker 3: more visits and more money and more spend right from
• 25:03
  Speaker 3: those people who are coming in. That's going to be
• 25:07
  Speaker 3: really the biggest winning formula going forward, you know, because
• 25:12
  Speaker 3: as spending consolidates to the people who have the most money,
• 25:17
  Speaker 3: you know, it's ah, you know, that's that's going to
• 25:20
  Speaker 3: be the game, right Or if you're a discounting brand,
• 25:23
  Speaker 3: you know, how do you get those discount customers because
• 25:26
  Speaker 3: the middle is going to be hard pressed to to
• 25:29
  Speaker 3: get money.
• 25:29
  Speaker 1: Out of Yeah, for sure.
• 25:32
  Speaker 2: Uh So it sounds like you you're not really confident
• 25:35
  Speaker 2: that AI is the great equalizer just yet for restaurants.
• 25:40
  Speaker 2: So so companies with large engineering teams, like young brands McDonald's,
• 25:44
  Speaker 2: they they they're going to extend their tech lead right now.
• 25:48
  Speaker 3: I am. I am a huge believer in uh in
• 25:51
  Speaker 3: AI and how it can become the great equalizer. Actually,
• 25:54
  Speaker 3: so you know, I'm not actually knocking that, I'm just
• 25:57
  Speaker 3: I'm saying that you know that that one of the
• 26:02
  Speaker 3: downsides of it is that it's so easy to get
• 26:07
  Speaker 3: overconfident in what it can achieve, because going from nothing
• 26:12
  Speaker 3: to something with AI feels so easy, you know, and
• 26:16
  Speaker 3: all of a sudden, you've created something just by typing
• 26:19
  Speaker 3: some words into something. So you know, people who have
• 26:22
  Speaker 3: never created, you know, a piece of software, you know,
• 26:25
  Speaker 3: all of a sudden have done something. And you know,
• 26:29
  Speaker 3: and on the other side of that, you know, people
• 26:32
  Speaker 3: who actually have done coding before, you know, see something
• 26:36
  Speaker 3: that becomes much easier. But scaling that two enterprise level,
• 26:43
  Speaker 3: you know, things that need to be maintained or if
• 26:47
  Speaker 3: you're or if you're actually incorporating like real deep machine learning,
• 26:52
  Speaker 3: you know types of things, it's it's going into another
• 26:56
  Speaker 3: another universe of disciplines. So it's there's a complexity when
• 27:02
  Speaker 3: you start to build those combined systems that need to
• 27:07
  Speaker 3: be maintained and grow, and you're changing the entire the
• 27:13
  Speaker 3: entire technical stack has to be UH has to be changed.
• 27:17
  Speaker 3: And I think that some people don't understand the scope
• 27:21
  Speaker 3: and the scale of it. So and and it all
• 27:26
  Speaker 3: starts with getting the data layer correct. You know, you
• 27:31
  Speaker 3: keep seeing people coming into the industry saying, you know,
• 27:35
  Speaker 3: I can apply you know, this layer of AI into
• 27:38
  Speaker 3: the restaurant industry. And a few months later, you know
• 27:42
  Speaker 3: they're they're gone because you know what they did, they
• 27:47
  Speaker 3: couldn't get the data layer right to get the correct
• 27:50
  Speaker 3: answers out. On the other side, you know, it's a
• 27:53
  Speaker 3: longer you know, it's a longer path to get into
• 27:57
  Speaker 3: the minutia of you know, crazy difficult cult restaurant data.
• 28:02
  Speaker 3: But once you get that, you know the knowledge and
• 28:05
  Speaker 3: the insights and and the accurate insights. Actually you know
• 28:09
  Speaker 3: prolifery really quickly. You know, that's that's what we're building.
• 28:13
  Speaker 3: Because I've been a data geek in the industry for forever.
• 28:17
  Speaker 3: But but that can be the great equalizer for people
• 28:21
  Speaker 3: who actually want to you know, want to invest in
• 28:24
  Speaker 3: in the data, in the data first, right, and then
• 28:27
  Speaker 3: apply to a on top of it.
• 28:28
  Speaker 1: Cool.
• 28:29
  Speaker 2: So, uh, I use Claude quite a bit, you know,
• 28:34
  Speaker 2: I have been reading that you know, they I should
• 28:36
  Speaker 2: be opening new chats uh to save tokens.
• 28:40
  Speaker 1: But I'm having an issue.
• 28:42
  Speaker 2: If by not remember because Claude is not remember remembering
• 28:47
  Speaker 2: everything that we've done in some of these other chats
• 28:50
  Speaker 2: in the past. So you know, we we were talking
• 28:53
  Speaker 2: before we pressed record.
• 28:55
  Speaker 3: Uh.
• 28:56
  Speaker 2: And it sounds like signal flare is solving for this.
• 28:58
  Speaker 2: How you're doing that?
• 28:59
  Speaker 3: Yes, so I think you look, you know, we're building
• 29:04
  Speaker 3: a completely different kind of system, which is in an
• 29:07
  Speaker 3: agent uh uh an agent platform which uses you know,
• 29:13
  Speaker 3: the the the intelligence of the of the base models.
• 29:16
  Speaker 3: But then it builds what we call hierarchical memory. Right,
• 29:22
  Speaker 3: So if you're using the other the other models that
• 29:25
  Speaker 3: you're using it through the the A P I not
• 29:28
  Speaker 3: putting your data actually you know load you know, loading
• 29:31
  Speaker 3: it into into the screen like you would with with
• 29:35
  Speaker 3: with A claud But then we use what's called hierarchical memory.
• 29:39
  Speaker 3: So for an organization, it maintains the memory within uh,
• 29:46
  Speaker 3: within your instance, and it always remembers that memory. And
• 29:50
  Speaker 3: it's for you, it's not for the model, it's not
• 29:52
  Speaker 3: for us, right, it's for the organization. And then within
• 29:56
  Speaker 3: each team it remembers, uh, it remembers everything that happened
• 30:00
  Speaker 3: within the team that you build. And then for every
• 30:02
  Speaker 3: project it does the same thing. So if you wanted
• 30:05
  Speaker 3: to do you know, a if you had a team
• 30:08
  Speaker 3: that's financed and then you had FP and a you
• 30:12
  Speaker 3: know commodity tracking, right that you always wanted to do,
• 30:15
  Speaker 3: and everybody who you know, ask questions and so forth
• 30:19
  Speaker 3: and comes back to it. If you had an ongoing
• 30:22
  Speaker 3: thread inside of a project, you know, to your point,
• 30:28
  Speaker 3: you would have to keep on coming in and going,
• 30:31
  Speaker 3: you know, and building a new chat inside of that,
• 30:35
  Speaker 3: you know, and it would forget everything, you know, chat
• 30:38
  Speaker 3: to chat in a claud What we're doing is we're
• 30:42
  Speaker 3: using agents to say, okay, well we want to preserve
• 30:45
  Speaker 3: this memory. So you know, there's what's called a context
• 30:49
  Speaker 3: window in in in the l MS that it might
• 30:52
  Speaker 3: be a million context window. But really the way that
• 30:55
  Speaker 3: that works is, you know, what we've seen is that
• 30:59
  Speaker 3: within that million contexts, when it's really only great in
• 31:03
  Speaker 3: the first maybe you know, thirty to fifty two hundred thousand,
• 31:07
  Speaker 3: you know, say you know characters or you know tokens,
• 31:11
  Speaker 3: so the middle starts to fall out. So really what
• 31:16
  Speaker 3: you want to do is sort of chunk that and
• 31:19
  Speaker 3: then consolidate it. So what we're doing is we're using
• 31:22
  Speaker 3: agents to take every piece of that context and preserve
• 31:26
  Speaker 3: it as memory. Think of it as sort of like
• 31:29
  Speaker 3: you know, making bullet points out of your entire out
• 31:32
  Speaker 3: of your entire memory. So every piece of your chat
• 31:35
  Speaker 3: and everything that you're talking about gets preserved as memory
• 31:38
  Speaker 3: as a bullet point. So we're sort of you know,
• 31:41
  Speaker 3: condensing it and making sure that everything about that gets
• 31:46
  Speaker 3: get stored so every time you come back, and you know,
• 31:50
  Speaker 3: in the case of a claud you know, that's built
• 31:53
  Speaker 3: for individuals. We're building it for organizations and teams. So
• 31:57
  Speaker 3: if you've got you know, an FP and a team,
• 32:00
  Speaker 3: and you've got you know, people who are leaving and
• 32:02
  Speaker 3: coming back, and you know, and you know the team,
• 32:06
  Speaker 3: you know turns over, the memory of what's been built
• 32:10
  Speaker 3: there doesn't go away. You know, new team members come in,
• 32:13
  Speaker 3: they pick right up from whatever was was was left before,
• 32:17
  Speaker 3: right all that memory stays with the organization.
• 32:20
  Speaker 1: Yeah, that's very cool.
• 32:23
  Speaker 2: You know, I I created a little data I know,
• 32:25
  Speaker 2: just enough to be dangerous. I created a little database,
• 32:29
  Speaker 2: but I have to kind of cut and paste the
• 32:32
  Speaker 2: important parts of the discussions that I want to keep,
• 32:35
  Speaker 2: and then I drop it into that. I have a
• 32:38
  Speaker 2: you know, front end where I drop it into that database.
• 32:41
  Speaker 2: Just so all the different AI tools I have and
• 32:46
  Speaker 2: can kind of or I have one basic we're basically
• 32:50
  Speaker 2: one repository, so that all.
• 32:53
  Speaker 1: You know, for one place.
• 32:54
  Speaker 2: Because I have different discussions with different tools and they
• 32:56
  Speaker 2: don't know what I talk about with the other one.
• 32:58
  Speaker 1: So yeah, it's been a fun project for the last
• 33:01
  Speaker 1: couple of months.
• 33:02
  Speaker 3: Which it's a you know, it's a it's a it's
• 33:04
  Speaker 3: a brave new world. I mean, it's it's fun, right,
• 33:06
  Speaker 3: And and look, I use all of them, and I'm
• 33:11
  Speaker 3: taking a learning from you know, from every you know,
• 33:13
  Speaker 3: from every different model that I do to say, okay, well,
• 33:17
  Speaker 3: how do I learn from from all these models and
• 33:19
  Speaker 3: the strengths of them, you know, to to build our
• 33:21
  Speaker 3: own enterprise grade, you know, so that we can you know,
• 33:26
  Speaker 3: take the functionality of all of those and say, okay, well,
• 33:29
  Speaker 3: how does this work in an organization? How do we
• 33:32
  Speaker 3: take that functionality and start to build it into into
• 33:35
  Speaker 3: a tool that you know, that that enterprise organizations are
• 33:40
  Speaker 3: going to value to make their you know, their their
• 33:42
  Speaker 3: teams more efficient.
• 33:43
  Speaker 1: Very cool.
• 33:44
  Speaker 2: What are some of the most popular ways restaurants are
• 33:47
  Speaker 2: currently using signal flair to help, you know, manage these
• 33:50
  Speaker 2: crazy shifts in the market we're seeing.
• 33:52
  Speaker 3: Yeah, I think menu management is is a big one.
• 33:56
  Speaker 3: You know, understanding you know what the most profitable whether
• 33:59
  Speaker 3: it's you know, the lay out, what what items to
• 34:02
  Speaker 3: you know, consolidate to price management of course, uh, you
• 34:07
  Speaker 3: know market optimization. Uh. And since we've launched, uh you know,
• 34:13
  Speaker 3: our our AI product, you know, Signal Flair Navigator, you know,
• 34:16
  Speaker 3: which is that agentic data Data Analyst. It's kind of
• 34:23
  Speaker 3: amazing that you know, they're coming up, you know, with
• 34:26
  Speaker 3: with uses that that I didn't imagine you know, they
• 34:30
  Speaker 3: would because if you can tap into all of the
• 34:35
  Speaker 3: you know, foot traffic data, the the the menu data,
• 34:41
  Speaker 3: you know, it's got all of our knowledge bases. So
• 34:44
  Speaker 3: we've we we loaded it with you know, with with
• 34:47
  Speaker 3: all of our internal knowledge you know. So it's not
• 34:53
  Speaker 3: going out to the you know, to the web, right,
• 34:55
  Speaker 3: it's it's just doing you know, data that we've curated
• 34:59
  Speaker 3: and you know and that we've we've loaded into it.
• 35:02
  Speaker 3: So it's all we know that it's accurate and you know,
• 35:07
  Speaker 3: and people are just but what it will do is
• 35:10
  Speaker 3: it also lets you upload your own your own data
• 35:13
  Speaker 3: and it's and it's secure, right, so the models are
• 35:16
  Speaker 3: not being trained, are you are not training on your data? Right,
• 35:19
  Speaker 3: So it's it's one hundred percent secure and uh and
• 35:23
  Speaker 3: and I'm sort of amazed at you know, at the
• 35:27
  Speaker 3: use cases that that our customers are now creating you know,
• 35:32
  Speaker 3: and you know it's it's it's pretty cool. You know.
• 35:36
  Speaker 3: It's you know everything from you know, doing you know
• 35:41
  Speaker 3: many feasibility studies on you know, site site by site,
• 35:46
  Speaker 3: you know, in an amazing detail, you know, to UH
• 35:51
  Speaker 3: marketing campaigns and taking some of our UH GEO targeting
• 35:56
  Speaker 3: information to to really ratchet down on how they how
• 35:59
  Speaker 3: they target at target customers. It's it's uh, you know,
• 36:04
  Speaker 3: they they are becoming the new innovators, you know, using
• 36:08
  Speaker 3: using the tools that were putting in their hands. So
• 36:10
  Speaker 3: it's pretty exciting.
• 36:12
  Speaker 1: It's very cool. Any last piece of advice you have
• 36:15
  Speaker 1: for restaurant chains.
• 36:16
  Speaker 3: Yeah, I mean I think look, it's it's it's going
• 36:19
  Speaker 3: to be. It's going to be. It's going to be
• 36:21
  Speaker 3: a challenging year. I think understand, you know, understand your
• 36:29
  Speaker 3: trade areas, right, understand, you know. I think the thing
• 36:32
  Speaker 3: that the reason Signal Flair and the way that we
• 36:36
  Speaker 3: got our name was from you know, decades of you know,
• 36:40
  Speaker 3: standing in boardrooms, you know, with restaurant executives and hearing
• 36:46
  Speaker 3: them say, you know, really sort of taking these chains
• 36:51
  Speaker 3: and and operating some of them like a monolithic you know,
• 36:56
  Speaker 3: single organization, you know, and saying, give me the forty
• 37:00
  Speaker 3: thousand foot view, right, I need you know, I need
• 37:03
  Speaker 3: you to tell me the one thing that I need
• 37:04
  Speaker 3: to do for this organization. And I would say, well,
• 37:07
  Speaker 3: look at forty thousand feet. The one thing I can
• 37:10
  Speaker 3: tell you is that you've got you know, you are
• 37:13
  Speaker 3: operating one hundred or two hundred or five hundred, you know,
• 37:17
  Speaker 3: individual businesses, right, with unique sets of customers in unique
• 37:21
  Speaker 3: trade areas, right. And if you think that there's one
• 37:25
  Speaker 3: thing that you need to do to satisfy all of them,
• 37:28
  Speaker 3: you know, they're out there shooting up these signal flares,
• 37:31
  Speaker 3: you know, saying hey, I've got a different set of customers, right,
• 37:35
  Speaker 3: and I've got a different you know, different cost factors,
• 37:38
  Speaker 3: and I've got you know, different things that are driving
• 37:41
  Speaker 3: my business, you know, and we've got all that information
• 37:44
  Speaker 3: in these local trade areas. It's not always perfect, right,
• 37:47
  Speaker 3: but we can identify what those driving factors are, you know,
• 37:50
  Speaker 3: at these individual ones and help you to figure out,
• 37:53
  Speaker 3: you know, what those things are. You know, that can
• 37:55
  Speaker 3: help to make those businesses more successful, right. And if
• 37:59
  Speaker 3: you can start to figure out how to treat those
• 38:01
  Speaker 3: businesses like that you know, at that local level and
• 38:05
  Speaker 3: enable it from a central position. That's how you're going
• 38:08
  Speaker 3: to become more successful.
• 38:10
  Speaker 2: Very cool. Yeah, I would love to log in. I'd
• 38:13
  Speaker 2: love to play around with it one of these days.
• 38:15
  Speaker 1: Man, if you're down.
• 38:16
  Speaker 3: You got it. We'll get you set up for sure.
• 38:18
  Speaker 1: That would be really cool. Thanks for doing us, Mike,
• 38:21
  Speaker 1: you're the man.
• 38:21
  Speaker 3: Hey, thanks for having me Always fun for sure. Man.
• 38:25
  Speaker 2: I want to also thank the audience for tuning in.
• 38:28
  Speaker 2: If you want to do some research on signal flare,
• 38:30
  Speaker 2: check out signalflare dot ai, or like I mentioned, you
• 38:34
  Speaker 2: could listen to the episode seventy six the two of
• 38:36
  Speaker 2: us did back on June twenty fourth, twenty twenty five.
• 38:41
  Speaker 2: You should also follow Mike on LinkedIn. One of his
• 38:43
  Speaker 2: recent posts inspired this episode, and if you liked our discussion,
• 38:47
  Speaker 2: please share with your friends and colleagues. Check back soon
• 38:49
  Speaker 2: for episode one hundred. I'll be interviewing Chris Dudy, the
• 38:53
  Speaker 2: founder and CEO of Piata Italian Street Food