Clinical Trials For AI? with Prof. Karim Lakhani

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Market details to come. But first, should we test AI before use the way drugs go through FDA approval? We'll hold that thought while we first set the stage for trying to answer that question. Well, we'll start with some very big numbers. Specifically $1.5 trillion. That's the projected worldwide spend on AI in 2025 alone. And according to Gartner, the technology insights company, it looks set to hit 2 trillion doll. Now the sheer scale of spending on artificial intelligence gets all of our attention. And as market nerds like me, so does the concentration of that spend. Four public companies alone account for over $300 billion of it. That's Microsoft, Alphabet, Amazon and Meta. And one company that is private. OpenAI sits at the heart of a now ramping spend in US capex. From here on in, with a labyrinth of deals cut with Nvidia, amd, Oracle, Broadcom and many more. But while those are headline grabbers, corporate investment in AI enhanced tools is happening everywhere. In sales management systems, in accounting software, in every industry and every function. So with all this money flowing to AI, with the tech getting better, faster and more accessible, are people ready to use all the AI that employers are putting in front of them? Does AI hurt or help productivity? Are changes really measurable anyway? And even if so, do they translate into tangible returns on all that spend? Well, one professor has tried to answer these questions by conducting what he calls, to use the drug development term, clinical trials of AI. And I sat down with him, Professor Karim Lakhani, to unpack what he's been testing AI for, what he's learned in the process and when we can maybe expect just one person working alone running a billion dollar company. All thanks to AI. Well, stay with us for that interview. And yes, before you ask, I used to be his student. But first, a word from our sponsor, Capital Group. John, why don't you tell us a bit more about their new show, the Power of Advice. That's right.

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Subscribe and listen. Today published by Capital Client Group Inc. And now my conversation with Kareem Lakhani, professor at Harvard Business School. Passionate about helping business people become data savvy. Professor Lakhani co founded and chairs the Digital Data Design Institute at Harvard. He's co author of the book Competing in the Age of AI Strategy and Leadership, When Algorithms and Networks Run the World. And he serves on the board of cloudflare. Well, I'm absolutely thrilled to welcome today Professor Kareem Lakhani, who I had the pleasure of studying with back in the day when I was at school. And so I don't. Can I call you Professor Lakhani? What do I call it?

Karim is good. Professor Lakhani. I don't know. Somebody. My uncle or something.

Or something. Okay, well, I'm gonna call you Kareem. So you recently did a TEDx talk. Yes, which is on YouTube. I encourage everyone to go watch it. It's fantastic. And I'm going to quote your opening line. Gen AI to me is like a drug that has been introduced to our economy, to our organizations, and to us. We actually don't know the right dose to take, the efficacy of the product, the side effects, the toxicity, or even the right diet to follow when we're using this drug. And so you, Karim, now with your team, are focused on doing, quote, clinical trials to figure out how companies should be using AI in the right way. Talk about that.

Yeah, absolutely. Look, it's. It's so much fun to actually work with companies and figure this out because I think there's a lot of hype, as we know about AI. There AI in the workforce, agents this and that, but very little actual studies saying, like, how does it work and when does it work and when does it not work? And so we basically think of ourselves as clinical trial specialists. We come in, we randomize who gets access to the t, to the tools and who don't. And then we study what happens. And that for US is the only way we'll actually get causality. Like, does this thing actually work and what's the impact going to be and what are the downsides along the way?

And are companies coming to you saying, help us figure this out, or are you sort of saying, look, I'm a professor and this is academic help. Give us that.

It's a collaboration. So the two big studies that have recently come out, one with Boston Consulting Group, one with Procter and Gamble, we had known the executives for some time, was very much a collaboration, saying, oh, we're introducing these technologies, and we would come and say, you'll probably do it the wrong way and let us help you think it through. That's how it happened with BCG and similarly at P and G. It was a collaboration with the head of R and D there because they really wanted to understand how to accelerate the R and D and the commercial interface with R and D. And so we said, let's actually do it in a proper way so you actually get results that you can believe in.

So let's talk about that Procter and Gamble clinical trial. What did you find?

Well, basically, we found that AI is transforming from a tool to a teammate. And now we have a basis to understand how agents might start to work. So the setup was as follows. We had early stage R and D and commercial teams working together with or without AI, but also individuals of the same caliber with or without AI. And we found three big effects. The first one was that AI improves productivity, and that's what teammates do. Right? When you add a team, when you create a team in an organizational setting, you get more work done. So the AI actually allows you to do more work getting done. The second thing is you actually get missing expertise. So if you are a marketing person and you don't have access to the R and D person, the AI provides you with that missing expertise. And the third bit is people feel great working with AI. Unlike the lots of other buzz about the fact that people feel depressed when they use AI. In our study, in this causal trial, we actually discovered that people have more positive emotions and less negative emotions when they're using AI.

Why is that? Why are they feeling more positive?

I think what it is is it's unlocking new capabilities both for themselves and their teams. So all of a sudden they're like, wow, I didn't know we could do all this work and also get all this expertise for ourselves. And that's beneath what I think, the positivity and the less negativity as well.

So you feel good about being able to do better.

Yeah, absolutely. And you can feel it. You can feel it.

So that's an interesting example where you could prove demonstrably AI was better because there was a team like Impact.

Yes.

What happened at bcg, BCG was very interesting.

And for us, you know, was like the big wake up moment for us. And so we were trying to understand, you know, lots of graduates from HBS end up at consulting companies, investment banks. And you might remember, the early stage job is twofold. One, there is like a deeply creative part. You have to be creative, come up with new ideas, come up with new business models for the companies you might be advising. But it's also a deeply analytical task. Like you have to crunch spreadsheets, look very carefully at what the customers are saying, and then make some predictions about what they should be doing. So we actually had about 800 consultants in our study. Again, the same setup, with or without AI, randomly assigned. And then we observe how well they're doing with these tools in both those types of tasks. And what we discovered is that AI has a jagged frontier. So basically, in some cases, it actually works dramatically well. And so performance improved by about 40%, which was unbelievable. Again, for us in terms of economists studying these kinds of effects. This was in the task of creativity, new product development, coming up with new ideas, new business models, and trying to write convincing text to convince their executives on doing this. Great news. The effect is such that if you were of median skill level inside of the BCG construct, nobody's below average at BCG. So if you're at median skill level without AI, with AI, you go to 95 percentile, which is a massive shift in performance and quality. But then for the task where it would require spreadsheet analysis, and remember, our study was done two years ago. Two years ago, all these tools were not good at handling spreadsheets. They would just hallucinate. And so if you put in the spreadsheet and you put in analysis done, you would get the answer wrong. And so here performance dropped by 20%. And this was shocking because we had told the people, look, be careful when handling numbers inside of this machine.

They had fair warning, they had fair.

Warning, and still the performance dropped. And so what that tells us is that AI has this jaggedness where for the same sort of role, in some cases it'll be incredible, in other cases they'll have sharp edges and you'll bleed. And so you actually have to pay attention to this. Now, of course, two years later, ChatGPT and Anthropic and all those tools can take care of the spreadsheet problem, but there are new capabilities that are being unlocked and again, with the same jaggedness that comes through as well.

So when you collaborate with the proctors and gambles and the BCGs of the world and taking the clinical trial you've done and then putting your ruthless money hat on as an HBS professor.

Yes.

One of the big concerns in the markets, just to sort of look at it through the market lens at the moment, is with all the capex that's going on, AI massive, with all of the software that's being generated to try and adopt AI and sort of retrofit all of these companies, the big, big question that investors are asking is what is the return on investment going to be? Could you see a path, even if you hadn't done it in that study two years ago, to being able to put dollars around the impact that you were seeing?

I think the dollar story is premature yet because there's a big learning curve on how AI gets adopted inside of your organization. And we know from a massive history of technologies there's often what we call a J curve, right? So you're at a certain performance level and then all of a sudden you introduce new technology, performance sympathy drops and then it increases again because the organization has to figure out where in the workflow, where in the processes do you actually bring the technology in? And so we always say there's always going to be a J curve. Don't believe the hype. That is always going to be straight up, straight up. And that's why we need these studies and the role of management and executives and the vendors is to figure out what the amplitude is and what the phase is and shrink them both. We know that's going to happen and you got to predict it and make it happen. And the biggest issue we see increasingly is that you actually have to change your work processes, right? Because if all of a sudden you get these magical powers with one company that we've studied, where they went from 100 hours to create marketing messages down to 1.5 hours, oh, wow, right. With better click through rates, 15% click through rates, you have to reimagine your entire workflow from creative to marketing to launch. And you also need to rethink, do you have the right volume of messages, do you change the messages and so forth? Because you now have shrunk basically effectively by about 98% with the time needed to launch messages. And so it's this change in workflow that has not been accounted for, which will then give you roi. But I think if you come in blindly saying I'm going to expect ROI without changing workflows, you're going to be in a load of trouble.

One of the things that you said in a paper you co authored for the Harvard Business Review, and I'll post links to all of this, by the way, so people can go find the original work you wrote, quote, persuading people and achieving organizational change can be more challenging than technical implementation.

Yeah, yeah. I think it's a 37 year old. It's 30% tech. Tech for me is actually getting easier. And 70% change management leadership, providing the right mechanisms, rethinking jobs, rethinking workflows along the way as well.

One thing you said in your TEDx talk was leaders need to get their hands dirty.

Oh, yeah.

That everyone is our responsibility. Whatever role you're in, if you're starting out in your career, you're top of the totem pole. You've got to go try some of these tools yourself.

You got to use it. So our basic thesis is that this technology is about expertise. It's providing you expertise. So you're the expert in your job. If it's providing you more expertise, you're the only person that will know how to use these tools to the best of your capabilities and improve your capabilities. And so, you know, I'm lucky to be at hbs. We see so many executives come down through our doorways and all of them are happy to talk about AI. But then I ask them, like, do you actually use AI when, Even when you're doing work on AI. And they're like, look at me, like, what am I talking about? I've got my big brain and I can use my big brain to come up with how to use AI.

I'm strategic, exactly.

Like a high level. I can look across the mountain and so forth. But really it's about you using the tool yourself is only when you actually use the tool will you understand both its capabilities, its shortfalls. And then when your teams come back to you with crazy ideas on how to use them, you'll actually have some credibility and some insight. To say, apply it here, don't apply it there. With Venmo stash a taco in one hand and ordering a ride in the other means you're stacking cash back. Nice. Get up to 5% cash back with Venmo stash on your favorite brands. When you pay with your Venmo debit card from takeout to ride Shares, entertainment and more. Pick a bundle with your go tos and start earning cash back at those brands. Earn more cash when you do more with Stash. Venmo Stash terms and exclusions apply. Max $100 cash back per month. See terms @venmo me stashterms.

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Well, look, I took an Uber over here from Midtown to your studio and the algorithm allocated me to a driver. The driver picked me up, the driver got directions from Uber as to where to drop me off to your studios, and then the driver got paid in the back end. When you apply to Uber for a job, the algorithm selects you. You know, there might be some human based interview process, but it's very minimal. And that scale. Millions of drivers are now being controlled through an algorithm, being managed through an algorithm and millions of riders. People like us are using these algorithms as the beneficiaries of the kind of things we want out of Uber. So that world already exists. Instacart is this way as well, right? Many of the online services have the agent as the boss. And the question will become, as more capabilities get unlocked in these AI agents, why wouldn't they be a boss as well? Why wouldn't they be able to allocate tasks, define tasks, interact with people, right? Deal with conflict, maybe even be more objective than your boss. Often do this fun trick in a big meeting. Everybody close your eyes. Now raise your hands if you really like your boss. Only half the people raise their hands so you know that about half are dissatisfied. Well, maybe the algorithm could be more fair, could be more judicious and dealing with employees as well.

So if there's a world in which there are fewer people as bosses, and there's a world in which there are fewer people perhaps needed for tasks like those you saw at BCG, where you did actually see a 40%, for example, improvement, whether that's productivity or whatever else it is. I want to talk about a study I think that you wrote, but I certainly heard you talk about it in 2023. And I think it was Ant Financial.

Yes.

And you painted this picture of an AI native financial firm. Well, let's talk about why it's so crazy and whether it actually is crazy when we look at what's happened now. Just paint that picture for our listeners. What was Ant Financial doing that got your attention? It totally caught mine when you described it.

Yeah. So look, in. In 2020 I wrote this book with my fellow colleague Marco Uncidi called Competing in the Edge of AI. Well before the AI craze. And we were saying that AI is not about technology, AI is about business. And it actually impact your business architecture, how you create value, how you capture value, your business model and then your operating model. Remember taking the TOM class with me, Operations management class with me. And we think about scale, how you serve lots of customers, scope, how you offer the many things and then learning as well. And we saw directly AI impacting both sides of a business architecture, your business model and your operating model. Ant Group, which was basically initially starts off as Alipay and the PayPal for Alibaba in China, starts to become a full service financial services company. And by 2019 they had about 1.2 billion users and about 10,000 employees, which is just like mind blowing. Right? I think, I think I was doing some back of the envelope calculations for the large financial services institution that I'm a customer of for a long time. I said probably for them to serve 1.2 billion users, it might need like 5 million employees, just given the scale of the thing. So if it's 10,000 versus 5 million, you know, they've organized themselves in a very different way. The basis for competition is not people doing tasks, it's organizing machines to do all the tasks. And one number that they had was like stunning for me. And I actually use that as inspiration for many executives here. They have a mantra of three one zero. Anything on their app should take no more than three minutes. Okay, one minute for approval. Right. And zero human intervention.

Zero human intervention.

Intervention. Three one zero. So what does that mean? That means that they've built systems to drive all the transactions in a large scale financial services business like that. Now, I think my bank Locally also has three 1 0, but I think three months, one month, zero computers. Sometimes it feels like that they often ask me for my signature card for various things. But. But I think what this means though is that a set of companies are going to show up which are going to have data algorithms, AI at their core. Humans will still be important, but they'll be in the edges and they'll be able to scale in dramatically different ways than what we're used to thinking. And I think what's going to happen is that we're going to have this ecosystem of these supercharged firms and then our existing companies and the question becomes, what does a transformation look like for our existing companies? Companies.

Well, you wrote that book in 2020 and you were ahead of your time. I just want to reiterate that for folks because well past that moment in time, this narrative picked up starting sort of last year about this idea that there could be a one person unicorn.

Yes, right.

A one person billion dollar company. Do you think we're there yet?

No, not yet.

No, not yet. How long will it take?

I would say people think like a couple of years. I think, you know, some, some companies like lovable and so forth have, you know, very less than 100 employees and crazy ARRs and so forth. So we're seeing that, I would say at least a decade before that shows up to be at, at that level of scale. I think, I think what we see right now, and we see this actually at hbs. So it used to be that if you were to start a tech based company at hbs, you'd be then making a pilgrimage across the river to my alma mater mit and try to find a tech co founder for yourselves. Or you'd now go across the street to across Western Avenue to the engineering school and try to find a technical co founder. What we're observing with our students is that the first MVP the students can do through vibe coding, they can actually build fully functional MVPs and show to their investors what they can get done. What that means is that you delay hiring the technical talent till you actually have some heartbeat on a business model, some heartbeat on product market fit. And then you can start to say, okay, I've got something now, I need somebody technical. And maybe you don't give them as much equity as you would as a co founder. So we're actually seeing that on campus. It's actually amazing. You know, I've been at the school 20 years now, 19 years, and it feels like 20 years. And so, and so when you see that, you go, okay, like something has really changed. And so our MBA students are more empowered to do more technical things. Some of them may actually have had technical backgrounds, but that gets supercharged. And even if you don't, you can now basically build it yourself for maybe up to the C stage, which is quite incredible.

Well, let's talk a Little bit about the sort of rebalancing of quote power for people with different kinds of technical skills. It used to be you wanted to be a software engineer. Software was going to eat the world. Right. And now you've got the OGs of software like Salesforce.

Yes.

You know we had on one of the sister podcasts, I was lucky to talk to Mount Benioff.

Yeah.

And he just launched Agent Force at the time you look at Salesforce's share price.

Yes.

It has been on the wane. Same with Adobe. These software originals are now facing these existential questions from the analysts who cover them saying, well, Jenny is going to eat software.

Yeah.

When you look at the world of academia.

Yes.

And you look at the brains in AI, what are you seeing in terms of a potential brain drain? Huge amounts of money are being thrown at top talent in AI.

Oh yeah, look, I mean I think this is a significant issue for computer science departments everywhere because what's happened is that computer science, which used to be on your laptop or on your machine, on your server, you could do all your work. All of a sudden you need lots of data, you need lots of compute and you need big talent to be able to pull this off. And so increasingly the bleeding edge of computer science is in companies and not inside of academia. And that creates a big risk for us because they'll end up there. And I think the case in point is DeepMind. Demis Hasabis got a Nobel prize for doing stuff inside of DeepMind and Google inside of academia. Now I think what's happening though is that I think the problems they're going after then will be different. What happens inside of computer science is that we'll differentiate on this is what industry will do, this is what we'll do. The boundaries are blurring both at Harvard, mit, at Stanford, all the best schools where we're allowing our faculty to spend more time in industry because they can actually be exposed to these problems and do a two way connections. There have been some academic papers that are sort of showing this kind of effect coming through. But I think what's going to happen though is that there will be differentiation. Academia will pursue different tasks versus what happens in industry. And the question then becomes of like, do you want to be. When you become academic, you make different choices than just maximizing your wealth, for example. And so then you'll and you're like, I hate having a boss. That's why I became an academic. I left bcg, I left GE because I don't want a boss telling me what to do? I want autonomy. And so we'll select on different traits. And so I think in the short term there is a lot of risk. I think in the long term, what I expect is differentiation. And academics are good at coming up with all. The basis for today's boom has been in academia. And so we'll set up the next stage of the revolution coming through as well.

Does academia become the heart and soul of the ethical questions around AI? I wasn't going to go there, but I think let's talk about it because I think you're very sort of articulate on this.

Yeah, look, I think what I say, like I have this five act play playbook for executives. I go, you got to do learning, you got to learn about AI and what it means for business. You got to do AI as we talked about. Then you got to think about acting and how you're going to act. Then you're going to imagine, sorry. So you got to learn, you got to do, you got to imagine what AI for you. Then you got to do and act and drive the change management. And the last bit, which is actually not in the TED talk, which I sort of hit me most recently, was you got to care. Because what's happening is that as we discuss the one person unicorn, as we discuss ant group, everybody's worried about their jobs. Whether you be in academia, whether you be in industry, whether you be a software developer or you are a lawyer, or you are a marketer or a finance person. And for executives, caring about what this technology means is very important. And I think the ethical responsibilities we now face in terms of how we actually deploy these tools, what it means for our companies and for our people, their identities, those are all going to be significant concerns for executives. But also for us as academics, we have to raise those issues. We can't just be handmaidens of AI. We actually have to be the soul and the conscience consciousness of AI as well. And that's part of the thing we're doing back at Harvard in terms of making sure the ethical questions are investigated properly and, and raised in our teaching as well.

And is anyone listening? Let me ask, let me clarify why I asked that question. OpenAI, if you look recently in their latest product launch, right, they had to opt, you know, flat raise your hand if you don't want us to use your ip.

Yes.

Right. You had to opt out of the usage, making it just an extra step for folks who are seeing their creative styles being used.

Yeah.

Is someone going to listen to a professor at Harvard Business school if Sam Altman is out there showing what the odd of the possible.

Yeah, no, look, totally. Look, I have a bit of a conflict of interest here because I'm also on the board of Cloudflare, okay. And you know, company founded by HBS alums and 20% of the Internet's traffic comes through our servers. And we have launched, you know, an initiative with publishers to actually create a marketplace for training content and inference content as well. So our sense is that the incentives to create content have to be in place, have to be in place, otherwise we'll be dealing with AI slop, which everybody's worried about. And so I think the solutions are going to be market focused. We have to create the incentives, the technology is there for us to both meter, provide access, create access. But now it's a matter of the large companies including Google, including OpenAI, including Anthropic, everybody to sort of say that we just can't steal the traffic, steal the content, we have to pay for that. And I think opt out seems, seems not the most ethical way to deal.

With this kind of, it's less transparent, it's definitely more of a hurt.

And actually there's no, like you don't know where to go to opt out. Like they don't tell you where to opt out, do we, do I send a cease and desist letter to Sam Altman? That doesn't seem correct. So I think Cloudflare is coming up with solutions there. You know, Matthew Prince and Michelle Zatlin, the co founders, CEO president are committed to making this happen. And so I do believe that this will look, we saw this before with Napster, right, 25 years ago, it's back.

The name is back out there.

So Napster basically allowed for made music sharing convenient, music discovery convenient. And we couldn't figure out a business model until Jobs came in and said I'm going to centralize it, I'm going to itunes it, I'm going to take 30% of the revenue, but I'm going to make it legal for people to share music and to download music. And then of course Daniel Elk came up with Spotify. So we've seen a way to actually provide incentives back to the music industry, back to artists. I think the same thing has to be done not just for music, but for across all intellectual capabilities, which is what the AI relies upon.

And last question for you because I have to ask you this, are we in an AI bubble?

You know, I'm not a macro economist, so it's really hard for me to sort of say this, you know, look, as you notice, right, the Capex is ridiculous. Right now about 500 billion is being set aside for Capex just in the biggest companies, just in the biggest alone. My sense is that from what I see in terms of the capability unlock from our careful clinical trials, the benefits are real. The question becomes the 70%, the deployment inside of the organizations to take advantage of it. And so that could take long time or short time depending upon the company. And what I say to most executives is that the capability frontier of these technologies appears to be improving exponentially every 6 months, 9 months, 18 months. But the difference is the following. If you remember back to the early days of the Internet, we had dial up and then we had DSL lines, then we had T1 lines. And the performance of our network connections improved exponentially but slowly. But the translation to business took a while. Same thing with the next generation of the Pentium chips and so on. In this case with AI, special generative AI, it's really about expertise and the expertise doubling. So the benefits to businesses are actually more immediate. And if you don't stay on the same exponential curve, you're going to be linearly behind, right? So there's going to be an exponential gap, right. The technology will improve exponentially, will be absorbing linearly, and there'll be a big gap. So the question for all the user firms is $500 billion in capex. Can you translate those benefits back into your organization and can you stay on the exponential curve yourself in terms of absorption? And the story is it's about organization management, change management, and getting your organization ready for change continuously.

And who's doing it? Well, just who, who is doing that transformation, in your opinion? Who's doing it?

Well, just around the corner from here, J.P. morgan. I mean, I think, I think Jamie Dimon has gotten the memo and he has both the Chief Information Officer and the Chief AI officer as part of the operating committee and the bus. The business units are actually put into play to say, deploy AI tools. We just did a case study on them. And so I think they've got it, they've got the memo and they're working at it pretty hard. Another company that I've actually again paid some attention to is Procter and Gamble. They've also like O Line soap manufacturer. Right. And they figured out how to deploy AI in the supply chain and the marketing side and the R and D side in some incredible ways. So we do see good lights. But I would say the average firm executive team is mostly lost today.

Karim Professor Lakhani, come back. Brilliant conversation. Thank you. Well, big thanks to Professor Kareem Lakhani for joining us. It's 4pm There it is, the closing bell. 4pm on the east Coast. John, did anything catch your eye this week?

Of course. It's earnings season and we just heard in your interview with Professor Lakhani about the case study at Procter and Gamble, or as he called it, the soap company. Well, P&G's earnings came out this morning and the company beat profit forecasts and reaffirmed its outlook on better than expected results in the beauty and grooming segments. This offset more tepid showings in areas like fabric and home care. Sounds like consumers are focused on looking their best. Shares of P and G were up a percentage. And from the retailer that sells many of those P and G products, Target. Target has been struggling with flat or declining sales in nine out of the past 11 quarters. So it announced yesterday it is eliminating more than 1800 corporate positions, which represents 8% of the company's global corporate workforce. Although the majority of the affected employees work at the company's Minneapolis headquarters. The company's chief operating officer driving all this now steps up as CEO in February. He said, the truth is the complexity we've created over time has been holding us back. Too many layers and overlapping work have slowed decisions, making it harder to bring ideas to life, a sign perhaps of what we will see of a CEO style in the future. Finally, to highlight the Fed's dual mandate to promote maximum employment and stable prices, let's pivot from layoffs to inflation. The Consumer price index hit 3% for the month of September. The data was released this morning. It came in a tenth of a percent lower than expected, but inched up from the 2.9% rate in August.

While the Federal Reserve meets next week, the market hotly anticipating another rate cut. And we'll be here covering it for you. That's it, folks. Meanwhile, for today's blue markets, Daily Brew.

Markets Daily is hosted by and Barry and produced by John Coteau, Tarka Delatief and Emily Miller. And our technical director is Chenowa Ogu. Rosemary Minkler handles audio and the president of Morning Brew Inc. Is Devin Emery.

Wake up on Monday with the Morning Brew newsletter and tune in to Neil and Toby on Morning Brew Daily. We'll see you back here on Monday. Have a great weekend.

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