Press Release

CFO Farhan Naqvi Joins Bloomberg Panel to Discuss the Future of Private Capital in Technology


Mandeep Singh (00:00): Very exciting to be here. So I lead the tech research at Bloomberg Intelligence and I’m going to show. Sure. You know, a shameless plug right now that we did $1.3 trillion forecast for generative A.I. by 2032. And this is a report that’s available for Bloomberg subscribers. It goes into the depth of, you know, the different parts of the Gen AI stack. And I would be happy to talk to anyone about it. But regarding the panel today, I think it’s exciting to talk about the role of private capital in this wave of technological innovation driven by generative AI. Because at the end of the day, you know, that’s what’s going to keep this going in terms of, you know, kind of widespread adoption. So I’m going to start off with Christine. Christine, you lead the IPO market, the tech IPO market at Barclays. And given what we have seen from, you know, the blow ups in 2021 when it comes to the tech IPO, especially on the software side, where are we now in terms of the broader IPO market when it comes to tech companies?

Kristin (01:20): Well. So there are a couple of things have happened in the last two years primarily. We had a major valuation reset as it relates to technology companies in the public market, and we had a major shift from a focus on growth at all costs in some cases to profitability. Because of that, I think it you know, the companies that were sitting in the backlog that we’re looking to go public have had to take a bit of a pause and figure out a business model fit, make sure that they can, you know, prove unit economics, prove a path to profitability, get profitable, and and also kind of get a more sober view on what valuation looks like, especially for the venture backed businesses. They may have raised that, you know, a significant premium to where they could ultimately get public in today’s in a kind of more normal valuation environment if you look over history. So with that, I think we look we’ve seen very few technology IPOs over the last couple of years. Part of that is companies getting ready, getting to scale, getting to a financial profile that the market would accept, and also getting comfortable with the new valuation paradigm. I think we’ll start to see that improve because we’ve now been, you know, two years or so without a number of IPOs. And so a lot of these companies have gotten to a significant scale and will be poised to hit the public market. Today is, you know, we’re in a pocket of uncertainty right now. We’re leading up to the election. We have, you know, some time to see kind of when rates will start to come back down, how many cuts we have this year, if any, and then what that looks like next year as well. So I think that most companies that are in the backlog have opted to wait for a little bit more certainty in the macro before they tap the market, given that they’ve waited a couple of years. That being said, there have been a few IPOs that have gotten out and they have strong gets, you know, strong conviction from the shareholders that are coming into those IPOs because they’re not buying it for a quick pop. They’re buying it for, you know, to own the company long term because the performance of IPOs this year, if you average all the ideas that have come public this year across all sectors, up 6%, that’s not exactly like running away. There’s no FOMO chasing IPOs. And so it’s it’s really fundamental buyers investing. But I think that we’ll start to see a little bit more momentum next year.

Mandeep Singh (03:57): And we’ve seen a couple of good ones. Our rubric, I’m sure there are others that are there. Farhan you have an investment background and you’ve also done M&A deals. From your lens, you know, given we are talking about AI and tech being synonymous right now, what is it that makes for a good business model when it comes to these new crop of AI companies?

Farhan (04:25): Well.So I view this as being split into different dimensions. One is infrastructure level investment. You’ll see Iridium companies which are building, actually building the models, garnering a lot of investment. And then there’s the other part, which is actually building applications for business use cases. That part is still not as robust. So most of the money that has been going into the sector has been going into the infrastructure level investment part. As the market evolves, the technology world will see that shift happening where businesses which are actually using AI tech to further their business use cases will start getting investments, valuations that they deserve.

Mandeep Singh (05:13): And so what valuations do these companies deserve, Kristin, given, you know, there isn’t a clear business model given, you know, every day you talk about models getting bigger and, you know, there is a lot of investment a company has to make to train these models.I mean, are they at a point where you feel okay when they go to the public market? Investors would be like there is a path to profitability, which wasn’t the case back in 2021. So you think what would help these companies get there? And just what’s what’s your perspective in terms of the evolution?

Kristin (05:55): Yeah, well, there’s a reason why this room is so full. And I’m I said something in her opening remarks that she said, We’re trying to get our hands around it. Right. And I think we’re all trying to get our hands around which companies are investable, how to think about, you know, who’s going to win. And so most of the investment is happening at the venture level where you can take a portfolio approach. You can buy eight or nine different companies and and then you’re not necessarily picking a winner, but you’re hoping that one of them is the winner. And and the other investment that’s happening in a more meaningful way is because it’s kind of like winner agnostic as on the chip side, right? So we’ve seen a number of these chips companies really take off with the explosion of of AI in these technologies. And so I think to get to the point where you’re not just taking the portfolio approach and the early stage investments happening, we need to see those exits start happening and we haven’t seen that yet. So we haven’t seen really a strong strategic bid for these venture backed A.I. software businesses because I think it’s too early to tell who the winners are going to be. And so we need to see that play out a little longer.
And once we start seeing a strategic bid for some of these businesses, it’ll give, I think, investors a little bit more confidence in terms of what areas and who the winners are.

Mandeep Singh (07:18): And just to hone in on that. So is there a difference in positioning for venture backed versus the private equity guys here?

Kristin (07:25): Yeah. So the venture backed businesses are very early.Right. And so it’s concepts and then it’s proving out market fit. It’s proving out, you know, customer adoption. I think in your case, right. A big part of the business is showing when the technology, when you can’t totally get your arms around the technology differentiation, it’s who are your customers, What’s the net retention look like? And proving out the kind of end market more than it is. What what are the specifics of the technology? And so that’s the part that a lot of the venture investments that are happening are kind of pre proof points like that. Private equity typically are looking for businesses that have a proven path, proven profitability. It’s something that they’re just taking investing to to scale or to improve the financial profitability of the business. And so that’s good. That will happen at a much later stage or they’re buying some of these technologies to put into their existing portfolio companies. But that’s more of a strategic buy, and we haven’t seen that yet.

Mandeep Singh (08:26): And to dovetail to that, Farhan, I mean, the market has been very good at sniffing out, you know, where the next wave of AI is going, from chips to power shortages to data center demand and upgrades. From your lens, do you care about these on a day to day basis at your company or when you are looking at talking to investors?

Farhan (08:55): Well, so if. Don’t take this as a humblebrag, but we are one of the few companies which have been able to actually build our business use cases so far, moving away from the infrastructure part to actually building use cases which clients of ours can can deploy. So to take a step back, we are an enterprise, our platform for learning automation and workflow automation. We enable our clients to build and deploy AI to scale. So our clients in oil and gas manufacturing, they’re using our platform to build and deploy apps for their own use. They’re driving efficiencies right now and they’re moving to the phase where they’ll start generating revenue pools or products which can which can contribute to their revenue topline. Now, moving away from just efficiency focus that is being used by AI apps right now.

Mandeep Singh (09:55): Yeah, I mean, so I guess when it comes to how companies are shifting like, it’s not going to happen all of a sudden that, you know, you upgrade everything, or you pivot to this new way of doing things. Like from a market timing perspective. How do you think, Kristin, companies are going to say, okay, this is the right time for us to go public because there is this nice tailwind when it comes to enterprise spending or just, you know, how like the investors are thinking about these companies like. Is there a sentiment that you track or anything that helps you determine from a market timing perspective?

Kristin (10:37): Well, I mean, one thing we’ve learned from 2020 and 2021 IPO classes is how important scale is, because the companies that were smaller scale, less liquidity in the public market were impacted much more meaningfully than those that had liquidity. And that’s just because it was kind of like the top ten investors, one or two start to sell and you see the stock, you know, impacted meaningfully and then everyone runs for the hills and then you have a number of orphan stocks. Even if the companies are performing, it’s hard to it’s hard to have the confidence as an investor to get back in. So scale matters a lot. And I think we’re going to it’s going to be some time before we see really scaled assets. And I mean, a lot of these companies are growing very quickly. So maybe it’s not as long as we’ve seen historically in other trends, but I think it will take some time for these businesses to get to scale and have the proof points around economics. I mean, you’ve got scale growth, profitability, all the things that really do matter. But that’s what that’s what the market needs to see and scale. We’re talking a revenue scale of 250 to 300 million, at least in the year that you’re selling off of, which is the forward year is really, I think that the benchmark right now for investors to participate, at least on the software side. Would you agree?

Farhan (11:55): I couldn’t agree more with that. So it took us about $400 million to be able to start thinking about putting the IPO process in motion.

Kristin (12:02): Yeah. Yeah.

Mandeep Singh (12:04): Anything you can share around the state of tech unicorns?

Kristin (12:10): Well, the interesting thing is a lot of the companies that are private in our backlog kind of streetwide to go public, a lot of them are software companies. And we’ve seen in the public market a number of companies have challenges with, you know, elongated sales cycles. We’ve seen vendor consolidation. And so I think that if you look at the companies in the backlog that are unicorns, a lot of the software companies are going to wait a little bit to have investors have more confidence in the enterprise spending toward software in general or be able to show metrics that they’ve been able to withstand either price increases. They have net retention numbers that are significantly better than some of the companies that have been challenged in the public market. As it relates to unicorn valuations, I think that, you know, that was something that was such a focus because there was a competition for engineering and sales talent, especially in the Valley. I think that has, there’s a lot a lot more sophistication around what unicorn status really means from a lot of the employees. And that is, you know, because some of these companies would have unicorn status but really have ratchets or some sort of synthetic instruments that got them to the unicorn status but didn’t necessarily persist if they were to go public, you know, once they were going to get valued for an IPO. So that’s the hype around unicorn status, I think has come down pretty significantly last couple of years.

Mandeep Singh (13:36): And for a hunt for a company with 400 million in revenue. Like, how do you think about tech spending? I mean, in general, I mean you’re a CFO. So when you talk to your, you know, tech lead or CIO, how is it that you’re thinking about investing in your business to upgrade and just overall the tech stack that you currently have.

Farhan (14:04): So. One. One thing that you’ve realized, very pertinent over time is, data is the lifeblood of any, data is what brings or makes models. And one thing that I think we did well was identify this early on, and we put in motion a process where we had actually procuring data. So a lot of our spend is focused on data purchases to make sure that the the models that we built ourselves are actually good, good to be deployed at our client’s end and other than, your spend on on infrastructure on compute power that that follows the trends that you’ll see across the market. Like there are not going to be a lot of variations between players on how much are they able to squeeze or increase their spend on that.

Mandeep Singh (15:08): So your infrastructure guys in coming to you and saying, oh, the supply shortages that they had to pay up for their dues.

Farhan (15:16): They always are. I don’t get surprised by that anymore. But yeah, so that’s a constant and that is something that we always have to factor in when we think about where do we actually make the investments. But what I’m saying is that is consistent across any AI company.

Mandeep Singh (15:32): Is there a feeling right now that you are going to be left behind in AI if you don’t allocate more capital or no? I mean, you’re saying

Farhan (15:41): I really hope my clients have that feeling if they don’t have it yet. But so there’s definitely excitement. People want to try out the technology. They want to understand what this has to offer, and they’re taking baby steps in terms of adoption. So they’ll make a few investments, try it out. And if that works for the business, they’d expand the use case. Yeah, that’s how we have seen this panning out.

Mandeep Singh (16:08): And Kristin, when you’re looking to do TAM analysis for the companies you’re looking to take public, how are you even thinking about a TAM for something like Gen AI? Are you throwing trillion dollar numbers or are you more realistic here?

Kristin (16:24): Yeah, over time. I mean, this is a math of decades long shift in innovation in it and huge tailwinds. I think it’s hard to put a number on it. I would say just anecdotally, kind of adding to what Farhad just said, anecdotally in talking to some of the large cap clients that I cover, we cover there are two ways, kind of two ways they’re approaching the efficiency piece of AI. One is top down. What are the what are the point solutions that we can plug into our existing processes to improve efficiency? That’s a that’s an easy sell, right? Because of call centers, whatever it might be. Anywhere you can add automation and improve margins very quickly. That’s an easy sale. I think what you said at the beginning, Farhan, when you mentioned it’s more than just the efficiency, it is actually the innovation piece of it and accelerating innovation. That’s the piece that I think investors and companies are trying to figure out because they don’t want to be left behind because they don’t, they’re worried. Now even these large incumbents are worried about new entrants scaling so rapidly because of the because of the technology advancements. And so that to me is where the, you know, the board meetings are. What do we need to be worried about? Because the easy sale is on efficiencies.

Mandeep Singh (17:41): And are you seeing efficiency for hand in the OpEx savings that you know, folks are able to

Farhan (17:51): Our organization? Yes, But no, she’s she’s absolutely right. The focus right now is on capturing efficiencies across the cost buckets. But once a client has has done that for a bit, they very quickly realize that they can actually use and leverage the same tech for enhancing their product portfolio. They can they can build newer products, which adds directly to their top line. So the the the coverage area where they can deploy this technology expands from just being focused on capturing efficiencies to enhancing revenue enhancing top line.

Mandeep Singh (18:30): Yeah, I mean, right now we are at a stage where we have generalized large language models that can, you know, do everything, but everyone is expecting there will be more specialization when it comes to LLM’s. So how do you think it will be disruptive to your area of operations and learning? I mean, we I think education is where it all started, you know, And so maybe, you know, so on that.

Farhan (18:57): So we as a company, we do not use LLM’s we’ve built SLM’s or small language models which are specific to certain sectors, and that is what we deploy for our clients. So LLM’s that is what drives the actual usage for those client. If you start with an LLM, a generic LLM and try to deploy that for a client in the Oil & gas space and then also same LLM for a client in the medical or internal space, it’s a lot of work and the results are not as effective. So the approach which we’ve realized over time works is you create smaller language models which are vertical specific and in some cases client specific and deploy them. That’s what drives the usage, that, that’s what drives the ROIfor the client in the end. Yeah.

Kristen (19:53): Would you call it? Yeah, I would say in the quality of the data that they have is the really the. And back to your point on data matters and the quality of the data that they’re putting into these models is really what is going to drive the best results.

Mandeep Singh (20:07): And so we do a CIO survey every six months. Bloomberg Intelligence. And one of the things that has come out for the last two times we have done this survey is the dependency on Hyperscalers, the cloud service providers. You know, how much sway they have when it comes to the compute, the development of large language models. How are you advising young companies that you’re working with to take public in terms of, you know, their positioning in these markets where the hyperscalers have so much dominance?

Kristen (20:40): Well, I think I mean, the reliance on hyperscalers is massive. And that’s no question. I think the best advice for young companies is to have diversification of partners. And I’m sure you guys are seeing that, too. Like it is, it’s it’s like any I mean, it’s like any boom in technology. It’s you know, we saw this with chips back in the day when phones were first coming out. Like any reliance on one particular vendor is going to put you at a disadvantage. And over time, especially from an investment standpoint. So having a diversification of vendors is the is the safest way to kind ofnscale up.

Farhan (21:16): A lot of the value in this whole chain has been going to the hyperscalers so far.

Mandeep Singh (21:23): Yeah. And do you think from your perspective, the reliance is not going to go away when it comes to the IP that you want to maintain or is what you do on cloud?

Farhan (21:33): It will take time. So we are totally dependent on our clients choices as to which cloud environment do they want to deploy on. And that dynamic, if it shifts, it’ll take a long time. Right.

Mandeep Singh (21:49): And so, Kristin, we have 3 minutes left. Perhaps, you know, you can sum it all up in terms of your expectations, given you mentioned this is an election year. So, you know, things may take a while to play out. Maybe you have a 12 month outlook or something along those lines.

Kristen (22:07): Look, I think I’m I’m bullish and I’m optimistic probably by nature, but I am bullish on kind of a post-election. It’s hard because we have a smaller window, but if we start to see some rate cuts, investors continue to get more confidence that we’re moving in that direction. It makes the funding environment easier for all of our clients. We’re seeing a shift in the way that public companies are financing themselves. One one interesting shift recently is refinancing straight debt with convertible with convertible paper, because they’re able to have pretty significant interest expense savings. And so there are different financial markets that public companies are using or financial instruments that public companies are using right now that are working but will continue to get more, you know, more attractive as rates start to come down. And we have certainty around the political environment and then we start to see, you know, they IPO’s, you know, come out once we have confidence in the secondary market, obviously it makes new issues easier and then you have more confidence in the private market, right? So it’s easier, it’s easier to put money to work in the private market when you see exits in the public market or strategic activity and that is from in AI, in software, in chips and kind of every single subsector within technology.

Mandeep Singh (23:32): Farhan.

Farhan (23: 34): Oh, so we are not going back to the zero interest rate all I think. But it it’ll take time for these things to stabilize. As Kristin mentioned, election year played a huge role into how these things find out how investors look at it. So from my perspective, it’s just wait and watch now.

Mandeep Singh (23:54): And from the enterprise side of the equation, given you’re running a company.

Farhan (23:59): Enterprise side is not as dependent on this, especially when it comes to technology spending. And for for products like this or for technologies like these, the sales cycle and the spend cycle is fairly well set. So it doesn’t change dramatically in the election year. You would see some tailwinds, but not not a lot.

Mandeep Singh (24:23): So won’t have any impact on your network?

Farhan (24:26): I wouldn’t say I hope not. I wouldn’t say any impact, but not not a significant impact.

Mandeep Singh (24:33): Got it. Thank you. Well, this is all the time we have. I want to thank my panelists and to you guys for being a great audience. And special thanks to Mubadala our presenting sponsor of Bloomberg and best for making this session possible. And now if you can, please make your way to Hall two at the far end of the hall for the start of the mainstage program. So thank you all.