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From Data Reels, this is FPA Today.
Welcome to FPA Today.
I'm your host, Glenn Hopper.
Today's guest is Saya Nath, a partner at Jump Capital in Chicago, where she leverages her technical expertise and deep passion for supporting founders.
With a background in computer engineering and early experience at Boston Consulting Group, Saya has developed a strategic and empathetic approach to investing.
At Jump Capital, she has led significant investments, including Series B rounds for Koji and New Era ADR, and has played key roles in supporting innovative companies like Re -curate, Obsess, Asano, and Indiso.
Saya's focus areas include data modes, risk management, and the future of work, particularly how technology and AI are transforming the way we work.
Her technical background combined with her consulting experience at BCG allows her to connect deeply with founders who have unique domain expertise, guiding them as they build solutions from the ground up.
Saya's investment philosophy is shaped by her family's entrepreneurial background, which taught her the importance of empathy in business.
She believes in a personal first, professional second approach to networking, and is passionate about pushing boundaries and taking on challenges that lie outside of her comfort zone.
Saya, welcome to the show.
Thanks so much, Glenn.
I'm happy to be here.
Glad we're finally getting to do the show.
I know we've been talking about several different ventures for a while, so glad to be sitting down and talking with you.
So I have a list of prepared questions here, but I do want to dive a little bit more in with something in your bio and that we've talked about before.
It's your personal first, professional second approach to networking.
Can you expand on that a little bit?
Yeah, it's a good question.
I think the view that I've always taken, I've always watched people kind of build companies from the ground up.
So my father was an entrepreneur my whole life.
He still is, in many aspects, building new businesses all the time.
I come from a family of entrepreneurs.
And so I always really noticed the grit, the determination, the passion that all these people had for building things from the ground up.
And I think sitting on the other side of the table, it's really easy to critique all the potential risks of building a business or how to evaluate a company.
But to be honest, if we knew all the answers as investors, I would probably just be building something myself.
And so I think given the personal lens that I've had to the entrepreneurship journey, I really try to have a deep appreciation for all the challenges that founders face for the fact that they're not always going to have all of the answers.
But our job is really to help them potentially identify answers to the pattern recognition we have as investors.
But that does by no means means that we will always have the right answer either.
And so I think the most important thing for me is to go into every conversation with an open mind, have a perspective, I think that's respectful of founders times is to actually understand the markets that they're building in and having a viewpoint on what they could be building, but always be open
to being wrong. Because again, I think a lot of times we are going to be wrong and being able to have those intellectual debates and come into those conversations with a sense of empathy and open mindedness is how ultimately I think the best ideas will grow.
Yeah, that's so interesting.
Because I spent the bulk of my career in later stage private equity backed companies.
And thinking about the difference between the timing of VC investments versus when private equity comes in, and you are you're investing in the management team, the leadership as much as you are the product, because they don't have the, like a PE has years and years of financial data and they can show
all that and you're going on an idea.
So you've got to have that relationship and trust in the management team and the founders.
So that's, it's interesting, it's, you know, there's all your business school stuff that you apply, but there's also being able to obviously the business idea, but being able to read the management team can these people execute can they make their vision happen and all that.
So I can see that being a strength in VC.
Absolutely. I mean, there are so many stories of VC is just getting markets wrong, right?
I mean, a lot of what we do is trying to predict the future.
And so we're probably just as good or bad as the next person would maybe a little bit more information to go off of.
But yeah, I think you have to trust your gut on the people that you want to build a relationship with, and you want to build something with and to your point, have a high level of confidence that they can go and execute on that even when markets get rocky or the tides change.
Yeah. So interesting times for VCs, the past couple of years, and especially VCs and AI these days.
But tell me about Jump Capital and your specific focus there.
So Jump Capital is an early stage venture firm.
We largely focus on seed series A investments, a little bit of series B as well as you called out initially in my bio.
And at a high level, we invest across fintech application software and infrastructure.
But one of our core pillars is that we're very thesis driven.
So within kind of these broader segments, we try to develop pretty deep perspectives on specific micro trends or micro segments that we're actively interested in.
And that will drive a lot of our investing strategy and how we think about going and finding new opportunities.
I'm a partner here on the investment team, I've been at Jump almost four years now, I spend a majority of my time on the application layer, I spend a little bit of time on infrastructure, just given my technical background, but a lot of what I orient around is application software, particularly I spend
a lot of time around risk and compliance related workflows around workflow automation of legacy functions.
So the CFO suite and automating different workflows around finance is certainly a big component of that.
And then I spend a lot of time just generally on verticalized SaaS and verticalized AI applications.
So you're obviously singing the song of my people now with talking about automation in the CFO's office.
And I don't know if you want to talk about any of your specific portfolio companies or what, but as far as what you're seeing out there right now, how do you see AI transforming the role of CFOs in today's corporate finance landscape?
Yeah, it's a good question because it's one that can really go in so many different directions.
I think there's so many different elements of the finance stack that AI has the potential to help automate or create more efficiency around, whether that's generative AI or traditional AI, as you rightly pointed out.
But I think for me, what it all boils down to the largest impact is that AI is going to help improve the quality and speed of decision -making, which I think is imperative to CFOs because what we've seen and heard over the past few years is that their roles are expanding very dramatically within organizations.
So CFOs are becoming a lot more cross -functional.
They have a lot more strategic imperatives than they used to.
And their general scope of work is increasing.
So as that happens, how do you actually enable them to make decisions that are more accurate, more impactful, and do it in a timeline that makes sense?
Because I think one thing we hear a lot from finance leaders is, by the time I can actually react to any of the information I have, we might be behind a quarter already, or it might not be enough time to actually make a dynamic decision off of that.
And so just clicking into that a little bit deeper, I think there's maybe two different ways that AI can power this change in decision -making.
First is when we spent a bunch of time, and we meaning jump, talking to a bunch of different finance leaders within organizations and really understanding what their challenges were and where they saw opportunity.
What we heard is that one of the biggest time consumers for people on finance teams is just collecting and processing data.
So between getting the right data from the right people across the organization to aggregating and cleaning that data, to actually getting it to a place where you can make sense of it and glean insights off of it, that can just take a really long time.
It's manual, it's inefficient, it's obviously, oftentimes, coming just from in -person conversations where you get one number from meeting with somebody for 30 minutes.
And so I think one of AI's most powerful capabilities is the way it can actually handle data, right?
Whether that's structured or unstructured data, qualitative or quantitative data, because finance inputs, as you know, come in so many different formats.
So I think just being able to gather all of that across an organization, across different systems, and being able to clean and use it much faster is a key benefit.
The second thing I'd say, which is where this all becomes a little bit more interesting, is actually helping finance leaders move from reactive decision making to a more proactive approach, which I think is something that's always top of mind for finance leaders, are always asking how they can do that.
And obviously, speeding up your time to insights using AI is a big piece of being able to make decisions faster.
But I think the other thing is that AI and LLMs, specifically, are making it easier to run more complex scenarios, right?
Or complex analyses, I should say.
So it doesn't take as long now if you want to test what changing one certain variable in your model looks like, or what planning one scenario looks like.
And what gets really interesting on top of that is the technology's ability to learn and elevate patterns that you wouldn't necessarily otherwise, right?
And so AI can actually start to provide logically sound recommendations on things that you should be spending more time on as a leader or as a CFO that you might have not understood yourself or would have had to go through lots of data over the course of a couple of months to learn that specific insight.
So I think those are two elements where the decision making capabilities that can be powered with new age technology get really powerful and very impactful for the organization.
Yeah. And you covered a lot there.
And it really it's, so the way I always look at this is you have to have BI before you can have AI.
We've been talking about digital transformation for my entire career, which started like in 1961, or whatever.
We've been talking about the idea that we've got to digitally transform our companies and people have made strides.
Maybe there is more sort of democratization of data than there ever has been before, but there's also a lot of messy data and not identifying sources of truth.
And it's just not having that foundation.
And among CFOs right now who I talked to, there's this real fear of missing out on the AI boom, but with some of them, they'll come in with these grand plans.
And then when you look at the data, man, we got to back up here before we can even, because it's garbage in, garbage out on using AI.
And then there's also among a lot of companies out there, the AI washing of what they do.
And truthfully, AI is not the magic wand.
We don't need to just throw that at everything and think it's on its own, it's going to fix it.
So I guess thinking about that, where a lot of companies and from enterprise down, not having really the best data structure in place to start using AI solutions.
But do you think, what area would the most impactful automation tools where CFOs should be looking?
And then what strategies can CFOs adopt to better integrate AI into what they're doing?
And I think it's wrong -headed maybe to think you're going to start with AI, but you have to start with the data.
But what tools are out there?
Or what steps should people take even before they're looking at applying AI to something?
Yeah, I think it's really interesting you brought that up.
Because I think historically, for folks in this function to adopt a lot of the automation technology that's existed for some time, AI has been around.
To your point, they've had to have these fairly sophisticated data structures in place, right?
So they manually would have to have engineering teams that are pulling data from a bunch of different spots and putting that into a data lake or a warehouse and building some schema on top of that and cleaning all of it before it can actually be ingested by an application or a BI tool to give you any
sort of relevant insights, right?
And so for anyone that's not a very large organization, that's a pretty heavy task.
And even for the larger organizations, engineers have so many competing priorities that this one might get knocked to the bottom of the list.
And you say, okay, just have your finance team do all of this work manually in Excel, we're not going to build some robust data warehouse around it, or data structure, excuse me, what I think is so interesting about the new wave of automation that we're seeing is that this ability to manage data and plug
into different data sources is almost the key underpinning.
So all of these applications are being built on top of this infrastructure layer, versus just being these pure play analytics tools that require you to do all the configuration work on the back end, right?
And so a lot of the new tools that we're seeing, they're plugging into all of your different data sources, they're doing that aggregation and normalization for you.
And they're using the power of things like LMS to do that in a low cost manner internally, still plug into your bank transaction data, or your ERP data, maybe your CRM data, depending on the use case.
And of course, there's challenges to that, you know, when your entire business has been built on top of NetSuite, which has custom code, and it's not very hard to pull data out of, there is some customization potentially required on the new solution vendor side to actually be able to understand that data.
But we're seeing a lot more of these companies being willing to take that onus on in order to power their application, versus relying on again, these companies to be at a level of sophistication that they might not be.
So that's what I'd say at a high level, I think a lot of the impact is coming from solutions that are building that foundational layer.
Now on top of that, there's I think a lot of different use cases you're seeing emerge, that can really help CFOs improve efficiency and decision making and all of these things we've talked about.
I probably won't be able to do the full landscape of categories justice, but maybe I'll give you two or three examples of categories that we've seen that we're really passionate about that are kind of delivering immediate impact.
The first one I would say is spend management.
And so I think it's no surprise to anyone that the number of SaaS tools and vendors within an organization has just proliferated so dramatically over the past couple of years.
And managing that spend has become really, really difficult.
And that might be because purchasing is decentralized, that might be because there's so many new business models that have emerged in high volume over the past a couple of years.
So for one tool, you might be managing usage based spend, and on the other, you might be managing a number of seats and the other might be a flat rate over six years that you're paying.
And so there's just a lot of complexity now and actually managing and understanding what all of your different SaaS spend is.
And so there's a lot of overage, I would say, or overspending in organizations simply because it's hard to know what you're spending and where on some of these SaaS tools.
There's a lot of tools that have emerged and have existed to manage your SaaS spend.
I think one granular category within that that we're really excited about is that of cloud cost management.
So what's being called as FinOps now.
Historically, cloud cost management was really just an engineering imperative, right?
How do we optimize our costs for the resources that we need to use?
But what we've seen and heard from financial leaders is that as cloud costs have absolutely ballooned over the past couple of years, they're now often the second highest line item for a lot of growing technology companies behind headcount.
On top of that, cloud costs are very complex, they're very opaque.
It goes as far to say that the CSPs benefit from them being complex and opaque, but they're not going to really change those cost structures anytime soon.
Because of all of those reasons, finance has gotten very involved in that management.
So we're investors in a business called Turnery, for example, shameless plug, that does exactly this, right?
So they provide a finance -configured view on all your cloud spend at a very granular unit economics level, which we're seeing a lot of appetite for.
So I think you'll start to see more and more of these spend management tools in more pointed categories pop up that will eventually bubble up into this aggregate, a spend management tool that you may already have in place.
The two other areas I'll quickly call out.
One is that of cash flow management.
So as you know, cash is king.
And I think it was an especially tight macro environment for a couple years, this was the metric that we heard was most top of mind for leaders.
So I think historically, again, this has all been done in Excel, or you might have some financial planning or management tool that you kind of had to configure to give you the right cash flow tool.
You're starting to see a lot more solutions specifically configured for that use case and helping to automate all aspects of that, whether it be payment alerts or expected versus actuals reconciliation.
I think this was a category or a use case that was a little bit underwhelmed over the past couple of years.
And people just thought they were doing a fine job at it until again, the macro got really tight.
And people needed to have a more granular, more dynamic real time view of their cash.
And they said, Okay, I don't actually have the systems or tools in place to do that.
So those are some of the top of mind areas that I think can deliver the most immediate impact to finance leaders.
But of course, you're seeing so much happening around forecasting and scenario planning around tax automation or compliance automation.
So to answer your other question about what can a company do to really integrate either AI or technology within their organization, I think you need to take a phased approach, right?
And I would develop a framework to really prioritize what the different areas of adoption could be for you and how you would roll that out.
If it were me, the framework I would use is identifying what the biggest bottlenecks are within your organization, either from a time and process standpoint, or a volume standpoint, or just a talent standpoint, right?
So I briefly mentioned like tax and audit.
And the reason that that's really interesting is because that's where there's just a ton of talent pressure.
There's not that many tax professionals or audit professionals graduating from college these days, most of them are going on to do other things.
And so that's one kind of metric or variable I would use in the framework to understand where should I prioritize adoption of technology.
And the second is overlaying that with some level of risk and ROI analysis.
And what I mean by that is the big question that a lot of leaders trying to implement AI have right now is it's really hard to measure the impact of this.
I don't know what the ROI is.
And if I don't know what the ROI is, it's going to be really hard for me to keep getting budget to keep implementing this.
And so there's these small use cases that have very immediate, very measurable outcomes.
They're very efficient.
So they don't, that's where I would start.
And those typically are also going to be the ones that the use cases that are maybe a little bit lower criticality or lower risk.
And so you can get over that adoption barrier a little bit faster.
Yeah, getting those small wins early so that you can, then it's just a matter of it's a win for change management as well, where you can say look, we low impact or low risk, we were able to do this and it gets people the appetite to do more implementations.
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I don't know how much you're seeing this because there are so many SAS tools out there.
And I've spent the past several years working with a lot of SMBs, under 50 million a year revenue kind of companies.
And a lot of these tools have just been, they've been priced out of the market for SMBs.
Their enterprise level, yeah, it'd be great if we had that.
So the SMB space doesn't have the tools, they don't have the teams and they don't have this.
So there's really, I think I'm hearing more from these SMBs.
Their real FOMO is like, we don't even know what to do, we can't even get access to what the big companies were using five years ago or whatever.
And I wonder if there's not an opportunity.
And I don't know how much you're seeing from companies that are pitching to you guys and looking for funding that how many are targeting because it's much easier when you can sell an enterprise product for tens of thousands of dollars a year versus the 1995 a month per user or whatever.
But do you see some of these tools out there now and some of these early startups that are focusing on these on the smaller businesses to kind of try to help get them bridge the gap that they couldn't do maybe on traditional SAS?
Yeah, I think you're absolutely right.
I'm seeing a lot of that, especially in the accounting and tax management space.
So there's a lot of these accounting for startup businesses, you're starting to see it proliferate into other financial functions.
But to your point, it's definitely not as broad today.
And I think part of the reason is a lot of folks have gone to market with this perspective of we're going to rip and replace your existing ERP, we're going to be the new age net suite.
And these are massive implementations that people pay a lot of money to both buy the software, maintain the software, get the consultants to maintain the software, whatever it may be.
And so these are huge contract values that you're able to go after, and even at a 50 % discount of an ERP installation, you can make a really big business really fast.
So I think that's the bias that a lot of the companies have, which is the huge opportunity and the longer tail and certainly seeing more folks build for that.
And that's one of the I think the beauties of generative AI is it's made software development cheaper, faster, easier, you can iterate a lot quicker.
And so if your R &D is cheaper than inherently, you can actually go to these smaller businesses because you don't have to charge, you know, these massive contract values in order to cover your costs and generate any type of software margin.
So I think that's been a really interesting phenomenon.
The other kind of thing all there on to that is we're starting to see a lot more of these verticalized financial solutions.
And so I've seen a lot more tools pop up for farmers, for example, so finance or accounting for farm management.
I've seen a couple pop up specifically for D2C brands, which you might think on the surface, why does that need to create a tool.
But in D2C brands, for example, marketing spend and inventory management are like the two biggest drivers of their costs, right?
And the there are specific nuances for those types of businesses that general purpose financial tools might not appreciate.
And I think historically, the market has either been there's these huge enterprise softwares that require a lot of configuration and customization to work for your business.
But if you're buying that type of software, you probably have the money to invest in that customization.
Or you have to use something like a QuickBooks or whatever is akin to a QuickBooks in a specific use case, which is great for a lot of people, but it lacks a lot of nuance that specific verticals might need.
And so I think there's a really interesting opportunity at the intersection of that.
So creating software for medium sized smaller businesses, but also within specific verticals that appreciate complexities that aren't general purpose across all industries.
Yeah, it's amazing how many businesses I work with who are you know, whether they're in manufacturing or any any industry where you have to manage inventory, and they just don't have adequate systems to do it.
And it's because they're, they're so expensive, or you know, they don't have moving to a full ERP from from something like QuickBooks, they need to do it sort of, but then when they look at the expense and the time it would take them not having the resources inside.
And it's, it's interesting right now.
I mean, I don't this this seemed a little premature to me.
But like, Klarna, their announcement that they were ripping out Salesforce and Workday and they were going to use AI to drive and do that.
That's it'll be interesting to see how that happens.
But I think you could replace this huge piece of software that that quickly internally when I don't know, it'll be interesting to see what happens there.
But I wonder though, you know, that was a kind of an extreme big example.
But I wonder if you know, you're an SMB that has, you don't have a full dev shop, but maybe you've got someone who could work as the architect and if they can, you know, 10x their productivity by using gen AI to help them write code and, and architect systems if you might see more of these sort of just
custom solutions in that space.
Yeah, I absolutely think so.
And I think it's something we're already starting to see.
But I'm excited to see how the category pans out.
The other thing I think about is there is a temptation, I would think for SMBs also, to just kind of go all in on generative AI, like not fully understanding what it's how exactly it works.
But it just seems like this magic box where you throw information into it.
You know, hopefully, they're keeping in mind proprietary, you know, data security, and all that when we're doing it.
But I think sometimes, I mean, whether it's using the data analyst tool and chat GPT or some of the other responses you get out there that are only going to keep getting better, there's going to be that temptation for people to throw all their data in.
And maybe they're not in the right kind of, you know, enterprise or team environment where their data is not being used to train the models and all that.
What kind of risks are you concerned about with people using generative AI and finance?
Yeah, I think well, you hit one right on the head, which I'll get to.
But I think the good news is that I think the risks associated with implementing generative AI and finance are largely the same as the broader risks associated with implementing Gen AI.
And I say that's good, because I think what that means is most organizations are already thinking about how to solve for this at a company wide level, right?
I don't think it's an extra barrier, specifically that finance leaders will have to overcome in order to implement solutions.
Again, I think this is already an organization wide initiative for a lot of folks thinking about AI holistically.
The catch to that, however, is that just given the nature of data and finance, how sensitive it is, how critical it is, the impact or size of that risk can be a lot bigger.
But what are those risks was your question, actually, I think there's two main ones that I think about, and there's a lot of risks that you could potentially talk about.
But I think the two that are most related to how AI can be used in production are privacy and accuracy.
So with data privacy, you started to get to this.
But when you're dealing with highly sensitive financial data, you want to make sure that wherever you're feeding that data is secure, right?
You don't want it to be stored in public clouds, you don't want organizations to be using your financial data to train their models and inform other companies off of, you want to make sure that that data is only being shared and accessed by the right people, not just externally, but internally within your
organization as well.
And there are technical kind of approaches to mitigate this, you can deploy your AI on premise, for example, if that's feasible, that's what we're seeing most large organizations do, you can implement different access controls, there's kind of net new security solutions coming out to help overcome a lot
of these risks. But I think privacy is the one that's most top of mind for me and for most people that we talk to, because I think it's almost the easiest one to accidentally breach if you're not careful.
Accuracy on the other side is more of a performance risk, right?
And so it might not, you know, result in some non compliance or some regulatory exposure, but it can result in reputational exposure, or you just making the wrong decision based off of garbage data.
And so while generative AI can be really good and really powerful, it's never 100 % accurate, especially out of the box.
And again, because of the type of data you can't really afford to be off by any real margin in most financial use cases, right?
And so, again, I think to solve for accuracy, there's a combination of technical and process or people driven approaches that you can take.
So there's different ways you can think about building your AI systems to be more accurate.
There's different testing tools out there and different modeling tools to really evaluate the accuracy of your data.
Say a lot of those are probably suited for more robust AI organizations.
So people really building infrastructure from the ground up where they have a whole team of developers focused on AI builds, and they need to have all of these different testing and evaluation tools in place.
I think really what you're going to see more of, at least in the near term, is just these more robust kind of evaluation processes where people are doing audit reviews and gut checks on models at some regular cadence.
You're likely going to have a human in the loop for all critical tasks, especially in finance for the foreseeable future, because even if AI can get you the right answer 90 % of the time, that's not good enough.
And so you're always going to need a human to be doing the quality checking on top of that.
And I think that's what we see the most today, and we'll continue to see.
Yeah. And I think that's an important point because I think about SOC 2 and all the compliance issues, or standing up for audit.
You can't just tell the auditor, we threw the numbers into the magic black box and it spit this out, and this is what we got.
You've got to have that sort of explainability around it and everything too.
So that's a very key point.
And so I'm sure you see all kinds of pitches that maybe a lot of things seem, if you could do this, it seems really cool.
But I also imagine, as part of your risk assessment when you're looking at companies to invest in, that keeping those regulatory challenges in mind are important to you.
So I'm wondering if you...
I mean, we kind of hit on some standards, but are there some key regulatory challenges that you think about with some of what you're seeing people trying to apply this technology to, and how companies, whether they're the company that's designing the software or who's going to be using it, how they
could stay compliant?
There's two different regulatory aspects that I pay attention to.
So one is all the net new AI stuff that's coming out.
So there's the EUAI Act, there's the New York Local Law 144 around bias, whatever it's called.
And there's a couple of these other ones popping up nationally and within states that are very AI specific.
So they're incredibly top of mind.
People are talking about them.
I think they'll be a little bit easier to stay compliant with because a lot of them are not actually structured or being enforced today.
And so people kind of have time to go and adopt the right guardrails around that.
And there's a lot of companies popping up to actually help you do that.
Right. And so just how we saw integrated GRC tools become widely adopted in organizations over the last decade, there's this whole new slew of AI governance specific tools popping up like Credo AI or PEGSI or Trustable, or there's honestly a lot of them that are tackling this problem.
So that's one side.
The side that worries me a little bit more that I think folks need to be really smart about paying attention to is the regulations that already exist but need to be revisited as you start to bring in more AI implementations within your organization.
Right. So obviously there's privacy ones like GDPR and CCPA.
There's a lot of stuff around how you handle consumer data.
And then there's industry specific regulations like the Fair Lending Act or there's a bunch of insurance that say, how are you actually making decisions based on people's data?
Or if you're a financial advisor, are you giving financial advice with AI or are you just using it to inform your decision?
So what's really tricky about a lot of these regulations is they've always existed.
But because there's all of this AI conversation, I think regulators are getting a lot more scrutiny around making sure people are compliant with those rules.
And even if your software or your business was compliant with those regulations four years ago, maybe with your new implementation of technology, you're no longer compliant or there's new risks you have to consider.
So I think that's a little bit of a trickier framework that people need to have a really robust approach to solving for.
And again, that might mean net new technology solutions.
I think that the hard part there is a lot of the new emerging solutions you're seeing pop up are focused on new AI regulations.
And a lot of the existing GRC solutions are not really well versed in AI or understanding AI.
And so there's not really as perfect of solutions as I'm seeing for that category of regulatory concern.
And that's where you're seeing a lot of AI ethics teams pop up or oversight committees to actually kind of run some of these processes manually and make sure that you have the right audit trail in place in your organization.
Yeah, it's going to be really interesting to see where that goes because I mean, it's the shortage that you mentioned of people going into accounting and the increase of the technology.
It seems like it's sort of this perfect area where the technology can make up for some of that shortfall, but the technology has to get a little more advanced, the regulations have to get a little more advanced to keep with it and all that.
So it's a very interesting time.
And I don't know, looking at the shortage of accountants in recent years, and finance hasn't had it as bad because I think a lot of maybe what people that used to go into accounting are going into finance, which I think typically has slightly higher salaries, or the people who would have gone into accounting
are going more into data science and BI because it's so trying to pull people in for the historical accounting skills is going to be hard.
And it seems like we're almost there with the technology, but there is this gap that we've got to cover in that.
And thinking about all that, I wonder, selling new technology that is still kind of opaque to the black and white mindset of CFOs and accounting people, it's got to be hard because on one hand, they're hearing, yeah, yeah, we need to do AI.
But when you start thinking about all the risks, a lot of the people I talk to say, I'm not ready, I'm not touching this yet.
So I wonder, I'm as evangelical about this stuff as anyone, I think it's great, but I also am caveatting everything I say, I'm a typical economist on the other hand.
But I wonder what your approach is or thinking with all the companies that you've talked to, how do you advise finance leaders who are hesitant about adopting AI and in these automation technologies, they've got the need for it, they've got this FOMO, they're getting pressured, to make the investment,
but there's also so many unknowns.
I mean, what's the play there?
Yeah, it's a great question.
And there's, I'll maybe actually take this in two different ways.
The first thing I'd say is on the people providing the solution side, I think a really important way, or potentially important way to give these folks that are hesitant to adopt comfort, is by almost thinking about some type of service provider model.
So, I'm a software investor, I'm not suggesting that I want to see a bunch of promotional services revenue start to pop up.
But we've seen a lot more companies kind of tackle or try to take on this partnership model or channel model where they say we're working with system integrators, we're working with the largest consultants of the world who can actually go and do the services work for you to help enable that trust and we'll
just be the technology provider.
I think that's a buying pattern that a lot of CFOs and finance leaders are used to and have typically done with historical technology.
We just talked about ERP implementations.
And so, I think there's some unique distribution models to help garner that trust and make people feel like they are getting that hands -on care and education without taking away from kind of your software margins as a business developer, right?
That's on the solution side.
My advice to the finance leaders who are hesitant is, of course, there's lots of risks we've talked about, it's understandable.
I would say start small though.
I think the biggest mistake is to not start at all.
I think that's been a big question for a lot of folks is, do I actually have to adopt this?
Can I wait? When do I need to do it?
And what I would say is unlike a lot of other technologies in the past that maybe had slower adoption curves or gave you a little bit more leeway in terms of when you could implement, the one thing about AI that I've noticed over the past 18 months, no matter how pro or con you are for the tech, is that there
is just a massive amount of excitement and interest here.
And it's come to a head pretty quickly.
So all to say that a lot of folks are already thinking about it.
A lot of folks wanted either function or organization -wide already.
But implementing that in a reliable, safe manner is not a trivial undertaking, right?
It will take time from a process standpoint, from an employee adoption and behavior standpoint, maybe from an infrastructure standpoint.
And so I think all of that means you need to start now to give yourself time to experiment and roll it out properly.
I think if you kind of wait for some market signal to start implementing, you're going to be in a situation where you actually have a lot of answers to still figure out when you haven't given yourself a ton of time to do that.
So again, what I would say is start small.
And that really just means gating which tasks to overlay with AI.
Start with low risk ones.
Again, the impact is measurable.
They're probably high volume tasks.
One example I'd give, and I know this is a DataRails podcast, it's not meant to be canned.
But when I initially started looking at AI and finance about 18 months ago, I forgot when the exact product came out, one of the and I don't know the current state of that product or how it's evolved, but the initial capability was really just a chat interface so that you could create a knowledge base
and allow people to interact with financial data and the source of truth a lot faster instead of having to manually dig through different systems and different records, they could just ask questions and get those back really fast.
And I think there's all sorts of implications on the power of that for collaboration and speed.
But that's an example of very low hanging fruit.
It's very low risk, you're only exposing that data internally.
It's a user interface that most of your employees are probably already familiar with.
So that dramatically reduces the barrier to adoption and the education curve that might be required for other AI technology.
And it's really quick to kind of measure if that's helpful or not, right?
You can look at how often it's being used, how productive your employees are.
But I think there's a lot of this low hanging fruit that if finance leaders can start to get comfortable with, they'll quickly be able to see a pathway to rolling out kind of more embedded technologies.
Yeah, I love that you said that because that is, I talk about that all the time, not specifically data rails, but even Oracle is real big on integrating, and I don't know where they stand right now, but on integrating AI just into the software.
And so I tell people unless you have a data science team and you have machine learning engineers, and you've kind of already leaned into AI, and you're doing not just generative AI, but also machine learning, statistical modeling, and all that, for most people in the SMB space, your experience with generative
AI is going to come to you from the SaaS tools that you're already using.
It's going to start being integrated with them.
But that's going to be significant because I think we talked about democratization of data earlier, and maybe this is a little bit of a bridge too far, but I think when you have generative AI integrated into these tools, and they're already tied into your source of truth, then you have democratization of data science
in a way in that, I understand that not everybody's going to know what K means clustering is or the specifics of it, but when you don't have to wait for, I think about a dashboard as being two dimensional, and maybe you can drill down a little bit, but when you want specific reports, you've got to put
in your report request and wait for BI to come back with it and however long it takes.
But if you can just interact with that data directly, that's powerful in and of itself.
So I say that all the time too, so I was glad to hear you saying the same thing.
That's actually something I think about a lot because I think that's one of the ways to really fuel innovation within the organization.
Because to your point, I think every functional leader or every functional team almost always has to wait for finance in some regard to get final approval of either investing any new technology or spending more on your marketing or whatever it may be, so being able to actually understand what the impact
of some of those decisions are, how they might have dependencies on other functions by yourself without having to wait for finance.
I'm not saying that the necessary approval cycles will go away, but actually being able to come to finance with a more informed decision on, okay, I ran this scenario myself, this is what I want to do, just reduces the back and forth time.
And I think, again, we'll empower people to be more innovative and creative themselves versus to your point waiting for the data to come back to them.
I love that you said that too, because I'm NDA, so I can't say the name of the company, but I've been working with a company in the FinTech space, pretty big company, and they have leaned very heavily across finance and accounting into generative AI, which has had great results from it.
I've talked to so many who are still taking this wait and see, wait and see approach, but in this example, one of the financial analysts I talked to there, he had some report he had to put together, I think they were using Google sheets, but he had to aggregate data from two different sources that were going
through a billing system change.
And there was this report he had to do every day, and it took him about an hour and it was going to have to go on through the end of the year.
He wasn't somebody who wrote macros and he didn't, wasn't a visual basic guy or Python guy or anything like that, but he just he said he spent 12 or 14 hours between, I think that he was using chat GPT and Claude, no idea what he was doing, but figured out how to automate, how to build in the scripting to
automate this report from every day.
That's one employee, one project, five hours a week, and then pretty good sized company.
So you imagine companies having this power and able to stretch that across the group.
It was pretty cool to see.
And for finance people to be leaned into that too, I thought, okay, well, this is interesting.
And what was great there is they have a policy, you have to use specific, the company teams account or enterprise account or whatever it is, and they have use guidance.
But the interesting thing is rather than saying to the employees, use it this way and don't do anything else, they're just giving them the tools, and then the employees are figuring out, they know their jobs better than anyone else does.
So they're finding these efficiencies just because it's a new superpower that they have.
Now we have all the risks and everything around it as well, but it was refreshing to see a company leaning into it with what I thought was pretty good guardrails too and human in the loop to check it and all that.
So you guys, you're investing in leading edge and bleeding edge, probably in some case technologies.
Can you speak to how Jumps using generative AI?
Yeah, we are certainly in the experimentation phase.
And so I'd say the most near -term use case that we're thinking about is how to use generative AI to help us generate board summaries and board reports.
And so our process today is incredibly manual, you get a board deck, whoever's the board member attends the meeting, take some notes, and then we have this kind of form that will fill out with a bunch of qualitative commentary and some quantitative commentary and send it out to the team.
And we do that because we take a very collaborative approach to investing.
Our entire team gets pretty engaged with different portfolio companies and we use different people's expertise to plug in where we can be value -additive.
So that level of visibility is highly required at Jumps.
But again, it's a time sink and the amount of times people are sending out notes saying, hey, this person's delinquent on board reports, and I'm definitely guilty of it, is not a small one, right?
Because it's not the most fun thing to have to do that.
And so for obvious reasons, we can't just use chat GPT and send it a board deck, because one, this is all confidential information.
And two, board decks, you've looked at plenty, I'm sure they can be dramatically different from company to company and how they're formatted, how the information is calculated, how long they are, there's just so many variables to what a board deck can look like.
You can't really train a model to be able to extract data from a structured input because it's not structured.
And so we're testing a few different generative AI tools right now to actually help facilitate that entire process.
And so we feed it a deck.
And we have these 10 standard things we want to know that are quantitative, we want to know a couple qualitative things about that, and actually spit out some assessment of how we should prioritize our engagement with this company or the areas that we should prioritize engaging with them on.
We think that'll just make us a lot more proactive in terms of, again, how we add value to our company.
So that's the most immediate and top of mind way that we're thinking about it.
But there's a lot of work we've talked about or experimented with when it comes to how we better leverage data for sourcing and elevating insights or how we do internal portfolio management.
So I think there's a lot of low -hanging fruit and venture.
We tend to be small teams and so you can imagine a lot of manual processes, but that's the most near -term way that I'm hoping we'll find a solution using generative AI.
Yeah, I'm working actually on a very similar project with a group that does a lot of real estate investment.
Their whole IR team, it's the amount of effort that goes into doing the quarterly reports for the investors and they're all different and different investors want different things.
But we're making progress and I think you're right.
I think that is an area where we are going to find a lot of automation.
So kudos to you guys for doing that.
I want to talk a little bit more about JUMP and then if we have time I want to back out and talk about just the macro world we're seeing now around AI.
So probably most of the pitches you get these days are some kind of AI -based, generative AI -based.
And I'm wondering how does JUMP Capital, how do you identify and support the innovative companies in FinTech and B2B, SaaS, the companies that come to you?
What are you looking for in these companies and what are you seeing that they need right now in these early stages as we go through this massive era of change?
So one, as I kind of mentioned when I describe JUMP, we do try to take a thesis -driven approach to most of the investing we do.
We're definitely not going to always catch everything by generating theses, but I'd say we try to make that cover a good majority of what we choose to invest in.
And even if we don't have a thesis before seeing something, we'll see a company and we'll try to really quickly get smart on the market and formulate an opinion and determine if there's other companies in that category we should be looking at if it is, in fact, an interesting category.
And the reason I think that's important is because that is how we have always operated.
And I think that allows us to not get very caught up with FOMO and bubbles and trends that are just temporal because the root of why we believe something is going to be successful hasn't changed.
We really listen to the market and people in market to really understand their problems, because I think the fact is in venture sometimes, because you're always looking at cutting -edge stuff, you're reading about the newest technology, it's really easy to disconnect from the voice of the customer really
quickly. And we're always pitching that to our companies and saying, you need to listen to the voice of the customer.
And that's honestly how we inform our opinions, too.
And I think that's very related to what you said about AI, that yes, we're getting so many AI pitches.
But I think by taking a thesis -driven approach, it's allowing us to have an opinion on where AI actually matters and is needed versus where AI is a solution to a problem that doesn't necessarily exist or isn't needed in that solution.
So that's one way that we try to dissect the noise.
And we're constantly putting out blog posts and other forms of content to really talk through those different ideas, and hopefully founders see that and get some sense of the types of things that we're looking for.
In terms of very concretely what we look for in companies, obviously, there are certain metrics around growth and momentum and product market fit and all of that that we look at, as all VCs do.
A couple other things I'd call out.
So thematic conviction we talked about, I don't think it's necessary that we need to have the exact same point of view as a founder on a product or how to build that product or even on the direction of a business.
But general alignment, not the problem that you're solving and the market that you're solving for is necessary.
The second thing that I think doesn't get talked about that much, but I'm sure it's something people think about a lot, is just if the market is ready to buy today.
So there's lots of businesses that could have been great businesses if they started five years earlier or five years later.
And so we spend a lot of time thinking about where are we in an adoption curve?
How high of a priority is this solution in a broader budget?
And is this something that people want to invest in today or we think is going to come to a head in 18 months or two years?
And then how does that relate back to what the stage of the business is and where they are in market or in their product development cycle?
And the last thing I'll just say is founder market fit.
I think every early -stage investor, as we talked about, has orient a lot around the team that you're investing behind.
I'd say Jumps founders tend to have felt the pain point very deeply.
It doesn't always mean that they come from that specific background, but have at least observed the pain point at a pretty granular level and then have the expertise necessary to tackle the market and execute against it.
And sometimes that means they need to have a really strong technical bend.
In other markets, it means they need to have a really strong commercial orientation.
So what we look for can differ a little bit, but I'd say the unanimous perspective or consistent perspective we have is that they have felt the pain point very deeply.
And that's usually what the solution is born out of.
All right. So I know we're getting close to the end of time, but I know you guys have to be watching this very closely.
And I'm thinking about the news from the tech giants right now.
I'm thinking about, so OpenAI is raising at $150 billion valuation now.
They're raising $6 .5 billion.
It sounds like they're going to be oversubscribed.
So even at $250 million minimum investment, they're turning people away who aren't investing in it.
And I think in other news, Three Mile Island is going to be spun back up.
And all the power is going to go to Microsoft for data centers.
And then I'm in Memphis.
And so the XAI big supercomputer came online here not long ago.
And just looking at the amount of spend that's going into AI right now, and that's on the frontier model level.
And then you have all these startups who are riding the wave and using that technology.
But as these models get better, if we're to believe that OpenAI's latest strawberry or 01 or whatever it's being called now, is really level two of their five -step plan towards AGI.
And three is just around the corner.
I wonder about a moat for these startups that are relying on the frontier, the foundation technology that is so expensive.
There's a lot going on in the space.
So how does the industry look at these companies that rely...
They're using generative AI, but nobody's going out and training their own models.
They might be fine -tuning them or whatever, but they're still riding on the back of it.
What's your concern for the industry or your focus or how you identify those moats or pick those investments?
Yeah, it's a great question.
And it's the key question.
Because AI is so unique in that the incumbents have such an advantage, and that hasn't always been the case in most technological revolutions historically.
I think there's a couple of things that we think about.
So one, I think this is where a specific configuration or verticalized domain specific approach really matters.
Because even if you make the argument that Microsoft or AWS, etc., has enough data to go and build curated verticalized models, they probably absolutely do, it's likely not going to be their priority.
I mean, you just mentioned all the things happening in compute.
There are so many tools that need to be built around AI management and AI pipeline management and around compute management.
That's so much nearer to their core business than going and building, for example, an e -commerce specific model or a dental specific model, whatever it might be, that I think there's just an element of it's not going to be their priority.
The companies that go and tackle that for the verticals that actually need that level of nuance will succeed because they have a distribution advantage, at least for the next few years where other companies aren't going to focus on this.
And then in 10 years, if AWS decides to go and build something, you'd hope that they built a scaled enough business by then.
The other element, which candidly becomes a lot harder to evaluate from a technical perspective is you're seeing a lot of interesting companies leverage small learning models, right?
SLMs, or do orchestration.
And that's where they're building their mode by saying, you don't actually need to send everything to GPT -4 or to this model or to the biggest model, you can actually use a bunch of open source components.
Or 80 % of this pipeline can just be done with traditional ML, right?
It doesn't actually need generative.
Only the last 20 % needs generative AI.
And through that, they're able to build a little bit of a cost mode, yes, but I think costs will get commoditized over time.
But more of a, almost like a latency and a performance mode, right?
Because if you can actually figure out the right models to do the right things and which ones can return things faster, and then create some type of orchestration layer where for certain use cases that differs, it's not the same orchestration for your entire product, you see some companies that are actually
starting to find technical differentiation that way because they're able to process certain analyses in a capacity or at a speed that maybe using a chat GPT or a general purpose model you can't, or will take a lot of refinement to do.
So those are the two areas we think about.
But candidly, the incumbents are incredibly powerful, we'll see what they do.
I think the other benefit here is in venture, you're on a time horizon.
So we're really thinking about what will the next five years look like.
And then if one of these large incumbents wants to come and buy our company, that's not a bad thing.
And so I also think there'll be consolidation and a lot of M &A over time as the incumbents think about prioritizing what do they actually want to build versus what do they want to just go and buy.
And we've seen a lot of the best AI talent leave these large incumbents to go start new companies.
And so I think that will also be a driver in fueling the buy movement versus just the build movement.
It's funny, we're right at time here and I could go all day and I just realized so the show is FP &A today.
And we just dove straight into AI because I was so happy to have you on and knowing just how much you keep up with the industry and everything that we just dove all the way into that.
And we didn't really talk about FP &A through those.
I guess we kind of hinted around it.
So I want to bring it all back home with one last question.
It's the question that we ask everybody.
And then I can say, well, we at least talked about Excel on this.
And it's funny because I've had authors and CFOs who probably haven't written an Excel function in years on there, but we ask everyone and I always love hearing the answers here, but what is your favorite Excel function and why?
Because as a VC person, I'm sure you've used quite a bit of Excel.
So I feel strongly that you would have an opinion or idea on this.
I do use Excel. I will say I don't use it nearly as much or for as much robust analysis as I did when I was a consultant.
And I'm probably going to disappoint you with a very boring answer, but probably the if function.
So when I was at BCG, I remember people would look at my Excel sheet and be like, what are you doing?
Because I would just create these super long nested if statements instead of taking what was probably a much more streamlined, cleaner formula to run the same calculation.
Because it was always really intuitive to me.
You asked why and I don't think I have a great answer.
But as you know, my background is in computer engineering.
So if statements either encode or actually writing out logic was so prevalent in the work I did that it was one of the few functions if and or that I just kind of knew what to do.
And I could write those nested statements for some of the other ones like VLOOKUP or whatever, you actually had to take a minute to understand it and practice it a few times.
And if was just always very intuitive to me.
So maybe I'm working harder, not smarter, but it works for me.
But it actually as someone with a development background, that does make sense that the ifs and I love ifs also.
And I remember I don't it seems like this is a million years ago, but it used to have a max on how many nested if statements you could have.
And that's gone now, I think so you can have as many as you want.
But I think with you know, CoPilot, we're not quite there yet.
But this latest release, CoPilot is getting better and better.
I think future Excel people instead of having to keep up with all the brackets and everything as they go through, they're just going to put in CoPilot, write this if you know, write these conditions or whatever, and it's going to go plug it in.
But and it will tell them about our old days of using slide rules and financial calculators and if statement.
I will envy them when you have to debug that like one very specific thing in this long statement that you decided to write, and it takes way too long.
So well, I really appreciate you coming on.
I do want to make sure if people want to get in touch with you and learn more about the work you're doing at Jump Capital, what's the best way for people to get in touch with you?
Yeah, it's a follower ad me on LinkedIn.
And then also follow Jump on LinkedIn, we have a great marketing person and she's not shy about making sure the world knows what we're thinking about, what we're excited about events we're hosting.
So Jump Capital's LinkedIn is definitely more active than mine, but people are more than welcome to connect with me.
All right, Saya. Well, I really appreciate the time.
Thank you. Yeah, thank you so much for having me.
It's been a pleasure.
