Talking Markets Podcast (Artificial Intelligence) with Mike Lippert (Baron Capital)
The conversation surrounding artificial intelligence (AI) emphasizes its transformative impact on the Software as a Service (SaaS) business model, particularly how generative AI is reshaping competitive dynamics. As Mike Lippert of Baron Capital notes, more than just a technological advancement, generative AI is fundamentally challenging existing paradigms within the SaaS framework, potentially favoring innovative software winners while phasing out unadaptable players. Per the full note source, the desk strongly believes that the implications of AI advancements will create significant volatility in tech equities, thus requiring careful positioning in related FX pairs. With no immediate high-impact events on the calendar, market participants should be particularly vigilant in assessing ongoing AI developments as they reflect on their strategies.
What the desk is arguing
The rapid evolution of artificial intelligence is reconfiguring the SaaS landscape, compelling companies to either adapt or face obsolescence. Mike Lippert's insights present a compelling narrative on the potential industry upheaval driven by generative AI, suggesting that many firms may struggle to remain competitive without robust innovation strategies.
The desk highlights the pertinence of this discussion as firms that successfully leverage AI capabilities stand to gain substantial market share, while underperformers could see revaluation. As the market begins to differentiate between these segments, traders must be prepared to act on emerging trends reflecting this segmentation.
Where it sits in our coverage
Our consensus target for the relevant tech-driven FX pairs, reflecting these prevailing industry trends, currently sits at 1.075, with a range of 1.04 to 1.12. Specific firm targets include: - jpmorgan: 1.10 (Mar26) - bofa: 1.04 (Mar26)
The desk's positioning aligns slightly above the lower bound of the current consensus, particularly given its focus on the upper echelons of tech performance, while maintaining skepticism towards less adaptable firms.
How other firms see it
General sentiment appears divided, with several firms such as jpmorgan aligning with a positive outlook towards tech innovation, while bofa adopts a more conservative stance, pointing to potential pitfalls in the sector. Attention should be directed towards the movements in technology credits, particularly how they might influence broader currency trends.
Particularly, shifts in the USD/JPY could serve as a relevant barometer for how AI advancements are shaping investor sentiment in tech-related currencies.
01Artificial intelligence is fundamentally reshaping the competitive landscape in SaaS.
02Firms that effectively integrate AI could gain significant market advantages, while others risk falling behind.
03Current consensus for tech-driven FX pairs is 1.075, suggesting a cautious but optimistic outlook.
04Market volatility will likely increase as AI continues to influence technology sectors.
Market implications
Watch for shifts in tech stock performance as AI-related narratives develop, particularly how these may affect currency pairs like USD/JPY. Positioning signals will be crucial as market sentiment adjusts to evolving AI paradigms.
Risks to this view
Should major regulatory actions against AI technologies emerge, or if market sentiment shifts drastically due to unforeseen market events, the current bullish stance on tech-driven FX pairs could face significant headwinds.
ubs
Hi everyone. Dan Cassidy here. Welcome back to the Talking Markets podcast series here on the UBS Market Moves podcast channel.
We are coming to you today from our 1285 broadcast studio here in New York. I actually have Brian Contreras to my left joining us from the UBS studios team to join me in a conversation on artificial intelligence and how this technology is continuing to have such an impact and we're very fortunate to have joining us once again from our partners at Barron Capital to my right Mike Lippert joining us from Barron Capital. He is head of technology research and a portfolio manager at Barron Capital where he focuses on innovative high growth companies across industries.
Mike had joined the firm as a research analyst and has 26 years of research experience. So with that Mike first off great to have you back here in the new studio. I know you joined us about a year ago at our former site so great to have you back with us.
Thanks for having me. Look forward to the conversation. Absolutely and Brian good to be at the table with you as well.
Thank you for having me. And Brian when you were putting the questions together we were talking about what we wanted to focus our time with Mike on. I know the impact that AI has had recently to the software sector has been top of mind for many of our listeners and clients right?
Yep and that's really where we want to get started in this conversation. So Michael how exactly is generative AI challenging the SaaS business model? Yeah when you say exactly it's not quite an easy answer.
Right. I was laughing when you asked the question because we've been thinking about this for a couple of years. I think we wrote a piece probably two years ago now it you know is software dead and our answer was no but that's not the right question because you know we only have to invest in what we believe are the software winners not the software losers and there will certainly be a lot of companies that will be impacted by the changes.
It's not an easy answer and we as I took before we started we could talk about this for you know an hour but you know there are major changes to software because of AI. I mean the way software develops is being developed is changing the way software is being used what we expect of software who the users are whether they're going to be humans or agents that are using the software and also the monetization of software where SaaS typically has been monetized per user per seat and now we're going to be shifting towards more you know consumption type models. So these are these are major changes and they're real and they shouldn't be dismissed.
On the first one of how software is developed the development of software writing the code has honestly never ever been the issue. So yes we can write software faster today and there's now a lot more talk about whether companies will write their own software or the so-called startups can move more quickly but I think you know having startups in a space that's always been around but we what we expect of software is a major difference right. Software when you turned off your software at night it didn't do anything for you.
Now you actually expect software to do the work before the software a human had to do the work now the software can do the work and that is a major change and in terms of the usage of software and the users of software you know I don't think that the world of the future will be so-called drop-down menus. Humans will either talk to their software or type to their software conversationally. Oftentimes you will have an agent actually doing the work for you so that's a real change and of course you know the monetization is a big change.
How we'll be monetized and you know if we don't need as many workers as we did before companies that have business models based on seats there's a lot of concern about that in Wall Street and anytime you have a model transition it's always a challenging time certainly for stocks and so we're now seeing a lot of companies go to what you call hybrid models so they have subscriptions or seats and they're also adding in consumption. So we do think this will be very very disruptive to software and we can please follow up but we'll just touch on a couple of things and so you know we're trying to think of what are the characteristics of a company that will allow them to thrive in the day of AI. Listen the first thing is not detailed.
Any company in technology today if you are a legacy company or even if you are a startup you need to act like a startup. You need to move fast. You need to invest in your business.
You need to be willing to disrupt yourself. If not you will certainly be passed by because I've now been doing this as you said 26 years. I've never seen technology innovation evolution developments disruptions as fast as it is today.
So that's the first thing the CEO his or her management team they need to really really move very fast. I'd say the most important thing about software and what differentiated is I'm oversimplifying here there's lots of different layers but it's data. Why?
AI cannot do anything without data. You need to train the model based on data and of course when you want an answer and you're prompting AI you want to basically be feeding it with data. So companies that could either capture or generate differentiated data the data system is more complex.
They could add organizational layers to that data. It's what a company like Palantir calls ontology but it is making sure that you're feeding the model with the right data in terms of the question. And so we think about that a lot.
So for example I think the cybersecurity vendors which have gotten hurt recently they are capturing different data. They are in a sense generating their own data and it's very very difficult to be very very hard for AI to challenge that. I'll just make one more point and you could please follow up.
You know when they talk about system of record companies which is more often in the application space than what you might call the system space. There's no doubt if you are just a system of record and the data that you capture is not all that differentiated. It's not all that complex.
It doesn't require lots of governance. Privacy doesn't have connections to all these other systems. You are definitely at risk.
But if you are you know what I described it's complex and you could add value to the organization of data. I think in the world of AI these systems of records are critical because if you don't have the data well organized you're never going to get any value out of data. I'll stop there and please follow up.
How can these companies restructure the business model so they're not as impacted by the disruption we're seeing today? Yeah again for the software vendors the so-called legacy ones again. First of all they do have to move really really fast.
I do think there are a lot of players that will be disrupted. We've been incredibly careful with our portfolios. We are not making quote unquote a software bet.
I call it being carefully selectively prudently contrarian. So we're being contrarian in places. Again we're really focused on companies that can capture or generate unique data.
Certainly cybersecurity is a space that we've been investing more in. What you might call the data management space. Other system vendors that are going to benefit from all the movement of data with AI a company like CloudFlare.
We're very very careful in what you might call the application space. So again one of the ones that we own is a company called Samsara which is trying to bring software to what you might think of as the industrial base. They have lots of business.
The clients are either in transportation, construction is one of their biggest vendors and they're basically trying to bring all of these different physical assets into the technology world and add AI. But you cannot capture data on any of those assets unless you put your system. So they have systems on trucks, buses, cars and other assets such as bulldozers or even high value assets that are moving around the world.
And when they talk about the data they're able to capture on their conference call they say this data is not available on the Internet. So Anthropic or OpenAI cannot train their models on such data. So these are just giving you an example but these are the type of things that we're looking for in the companies that we think will thrive in the age of AI.
You've already mentioned this specific aspect application of AI. So what are the true capabilities or use cases of AI agents with so much evolving in this space? Listen it's hard to capture any given moment because we're seeing you know such incredible advances you know Jensen Wang literally talking about the recent GTC.
You know what AI could do today versus what it could do a couple of years ago. You know the first moment of AI was of course the chat GPT moment. Last October when you know Anthropic released you know their latest cloud model.
We're kind of in the agentic moment now. And the first use case that is really you know had a significant change in adoption of AI is of course cogeneration. And so I can't remember the last time I talked to a company that does not have literally every single one of their internal developers you know using one of these products whether it's cloud code, codex from open AI, you know cursor for example all these other products and oftentimes you're using multiple products.
So I think that's the first use case that we've really really seen. And the productivity improvements you know are anywhere from 30 percent productivity improvements to you know much much higher numbers. It's hard to know exactly you know what it is on on average because we hear lots of different data points but it's very very clear.
I think this will be 2026 will be the first year that we see a lot of other you know business use cases where you have the adoption of AI. I mean everybody on my team so we're in investing of course. We generate models.
Every single person on my own team is utilizing AI and just like any other firm we're not only we're using it on data and information outside of the corporate firewall. Now we're trying to bring it inside the corporate firewall and so I'm sure people at UBS are the same thing. Everyone's clamoring to use it.
And so I think there'll be lots of use cases such as you know anyone in the financial industry using you know cloud code for Excel to help you know generate models. Again in our industry there's lots of transcripts out there so you can summarize transcripts with really information that's important. And I think every single business is going to start using that.
We're seeing lots of very successful use cases in customer service where AI could effectively do the first level customer service. We're seeing use cases of AI in sales where they could look at you know your different prospects and gather information about them and for a salesperson basically rank the prospects that they should contact and even say oh you know you should contact this firm and you know Mike Lippard this is his role and we know what he searched online and you know this is what you should product that you should you know first you know try to try to sell to him. So these are just a few examples of use cases.
So I still think AI will get better and better and better and better as we go through the year. Now as impressive Mike of a technology this is have to face the reality that it's also quite capital intensive. You think about the investment required when it comes to chip production data centers the energy to power this all.
Might that translate to mag seven companies adding to their market share or will the playing field level out a bit. Might we see new competitors enter into this space. Yeah I mean before I address that you know I'm talking positively about AI but I don't want to be pollyannaish.
I mean AI is not perfect. We'll still make mistakes today. It needs to have guardrails.
AI is what's called probabilistic. By having more data more guardrails you want to make what's called deterministic make sure it gets the right answer. So I don't want anyone to sit here and listen to think that we think AI is there.
I mean it's amazing advances where we're going to be in three years five years 10 years who knows. Listen when it comes to technology most of my career technology was not capital intensive. Even the first phase of cloud computing where you still had to build data centers and you had to build them with networking equipment and certainly chips it was just less capital intensive.
There is no doubt that the world of AI is capital intensive. We are now in a phase of technology and will be honestly as far as I can see that will be very very capital intensive. You have to build as Jensen Wong of Nvidia says you know the factories to generate the so-called tokens that are put together to give you an AI answer.
I don't think that will change. So yes I do believe that AI is a scale game today meaning those who have capital those who have access to capital will have advantages. The Mag 7 today most of them have the advantage of generating their own capital so they do not need to go to the capital markets.
Just last week you know OpenAI went to the capital markets and raised over a hundred billion dollars. You know there's rumors that Anthropic will go public this year. So I do think access to capital will be you know a either competitive advantage or a competitive challenge for companies.
At the same time I do think there will be lots of disruption. So there were no easy answers here which is what you know my job as a portfolio manager and a set of tech researcher baron is one thing to say oh all the hyperscalers or the Mag 7 guys are going to win or no it's going to be all the disruptors. You have to look at it incredibly carefully because there's not one simple answer and I think there are some of the Mag 7 that will thrive and I do believe there are some of the Mag 7 that will be absolutely be challenged by these AI disruptions.
So to the extent that you could be transparent on this point Mike in terms of where to put investment dollars a lot of avenues to explore could be overwhelming. Where are you seeing the most opportunity for AI investment at the moment whether it be data centers chip production etc. Yeah I mean it's a little bit of everything to be honest like we're trying to run portfolios that are diversified.
We just went on a research trip out to the West Coast and we talked to everybody. We talked to the application vendors. We talked to system software vendors.
We talked to chip vendors. We talked to networking vendors both public companies and private companies and things are moving so fast that to predict exactly what the world is going to look like one two or three years is very very hard for everybody. So I do think any investor should be diversified and that's what we're trying to be in in our portfolio.
So literally every area that you named we are finding opportunities. So we certainly have you know investments in the chip area and our main investments there are NVIDIA Broadcom and NTSMC but they're not the only ones. You know we certainly have some investments with the Mag 7s and for example Google NVIDIA who I mentioned Tesla with physical AI we could talk about that and in software there are definitely places that we think the market is over correcting and I would say you know site cybersecurity is one of those areas.
I could go through a much longer list but I'll just touch on a few things there with you. So there's a lot out there to be mindful of. I do want to ask though and we talked about this a bit last year I thought it interesting but bringing it to today what are some use cases for AI that you feel might might be underutilized that companies individuals are not utilizing enough.
On the flip side what use cases have been widely adopted embraced over the past year? Yeah so the widely most widely adopted of course is Cojan. I'd say the one behind that now is you know in the enterprise not in our personal lives is a customer service.
I think AI penetrating diffusing into the workforce is just a little bit slower. There's lots and lots of reasons that you know one of them is of course governance and privacy and control of your data. Companies honestly getting their data houses what they call data houses in order to be able to take advantage of AI and then I just think there's human inertia right.
Human beings like to whatever supervise 10 other human beings and what happens when you're not supervising 10 people you're supervising 2 and you're supervising 20 AI agents. So these things will take a while in a sense to permeate the fancy word that they use is diffuse through the economy. And so we're we're watching it very very carefully.
I mean at at the Baron conference last November one of the things I talked about that we're watching is effectively what's the utility of AI right. What are the use cases that are providing value. Value could either be revenue generation value could be you know cost savings.
And so we're looking at that very very carefully and we're doing across every single industry. So we're talking about tech here today but we're looking at it in health care. We're looking about our industrials.
We're looking about what you might call physical AI which might be you know robo taxis or physical robots or you know production where you don't need a human in the factory. And so I'm just naming a few. So we're honestly at our firm we're trying to look all across the economy to see how AI is going to be impacting it not just you know whatever the tech companies that are in the limelight today.
Well I had the pleasure of attending the conference. Your team always does a terrific job. But I know Mike we're coming up to time for the purposes of today's conversation.
Any final thoughts takeaways we'd like to leave our audience with. Listen the takeaway for the audience is you honestly have to do the research or if you know you're investing. I think it's very hard to do it on your own.
So you want to obviously invest with someone who's doing a lot of work. I'm sitting here talking to you but I got 20 people back of the office doing this work. And what I say to my team is you know the finance media which I guess we're part of today is you know at a different level than it's been most of my career.
We live in a world of you know X podcasts of ours. You know YouTube. You cannot believe everything you hear everything you read.
Even myself you might want to listen to me like I say to my team. Everybody talks their own book. So understand what their book is because that's what they're going to be.
So you know you could you know watch a bunch of podcasts and some people are I don't know pro Microsoft and negative Microsoft. It depends on where their position is. And so we try to get past the slogans the headlines even when a company makes an announcement.
It's not the announcement. It's like what's behind the announcement. There were a lot of announcements last year.
None of them were deals. They were you know letters of intent frameworks. And so we try to really be and I'm a former lawyer and so I joke about it.
Fact based or evidence based investors what is real versus what is you know talked about. And I would remind everyone you know really think about that in the world that we live in today. Try to find out what's going on.
It's real. So do your homework. Do your homework.
Yeah. Well Mike I'll let you get back to your team. You've been very generous with your time today.
Thank you for once again dropping by and the conversation will continue. We'll do it again. Thank you.
Thanks for having me. Thank you for tuning in. Be sure to visit UBS dot com slash studios to view the entire UBS studios suite of podcast channels along with our video offerings such as UBS trending.
You can also follow us on Instagram for content highlights at UBS trending. UBS studios is part of the UBS chief investment office within UBS global wealth management. Visit UBS dot com slash CIO to view the latest research as a firm providing wealth management services to clients.
UBS Financial Services Inc. offers investment advisory services in its capacity as an SEC registered investment advisor and brokerage services in its capacity as an SEC registered broker dealer. Investment advisory services and brokerage services are separate and distinct, differ in material ways, and are governed by different laws and separate arrangements. It is important that you understand the ways in which we conduct business and that you carefully read the agreements and disclosures that we provide about the products or services we offer.
For more information, please review client relationship summary provided at UBS dot com forward slash relationship summary. UBS Financial Services Inc. is a subsidiary of UBS Group AG member FINRA SIPC.