VC funding for SaaS start-ups is harder than ever to secure. How should SaaS CEOs manage it? (2026)

Hi, Jamie. Thanks for jumping on. Hi everyone. I'm Pete from Revelesco. Jamie, can you give us a quick intro before we jump in?

Yeah, sure. Thanks, Pete. And thanks for having me on. Jamie Strauss. I'm the founder and CEO of Digby, which is the sustainability platform for the extractive mining industry. And my background is, was originally in the city. So financing mining companies and projects around the world over the last 30 or so years before I got into this in 2020.

Thank you. And we're here today because you messaged me a couple of weeks ago and what we discussed was that there's this kind of perfect storm right now and 3 things, 2 of which we've been dealing with for a while. Someone even said to me the other day about AI, they said, that's not really new though, is it, Pete? I'm like, of course it's new. What Alok Shah called in his Substack the other day, the global intelligence crisis has just ratcheted up another level because the quality is so much higher. So we've got this terrifying certainty that there's this intelligence crisis, even though we see so much value in AI, we also see the threat. And then the second thing, which has been going on for a while, is there's a contraction of capital that's associated to AI, given that the cost of running a large AI business is so high. Everyone, VCs, private equity, they're throwing money into that, which means there's just less capital available for the rest of us. There's diminishing resources. And as if those 2 things aren't enough, Then we've got geopolitical chaos on top of it. For some time now, my clients have been asking me, what are your other clients doing about Trump's tariffs? How are they advising their clients? It's indirectly affecting SaaS businesses. But now we've got a war as well. And it's not just a war in a part of the world that we don't often think of or go to. It's affecting where some of us have worked or had offices, have friends, you know, stop over on our way to Asia or whatever. So this is terrifying. terrifying certainty around AI. There's diminishing resources, and then there's the terrifying uncertainty of, is this all going to be over in a week? Is this World War III? What is the impact? And so when you layer those 3 things on top, for any SaaS founder who is not thinking really hard about how to deal with both the risk and the opportunity, then I'm not sure what they are thinking about. So I wanted to ask you as a founder, where are you seeing the the risk and the opportunity inside this perfect storm for you?

Yeah, it's a really good question. And I think sometimes events can sometimes overtake the reality of life. And, you know, what I mean by that is this war, you know, is an event that has happened very, very quickly. But the reality is, and I've been really doing what I've been doing for the last 4 or 5 years now. Well, since 2021, really 2020, 2021. And we pretty much saw from 2022, we were with the peak of the VC investment into software companies. And since then it's been pretty much a consistent decline and engagement with the software companies as AI has been ramping up. And so I wouldn't think this diminishing resources, it's accelerated very quickly, but in actual fact it's been an ongoing trend for some time. And I think why that's relevant, and particularly from my background in finance, is quite often trends, or typically trends end, whether or not it's an increasing trend or a reducing trend, in an acceleration of that trend. And if we try and then overlay with where we are today is finally we've seen culmination of all of these years of work being done to get AI into the commercial spectrum. And suddenly there's been this acceleration and this huge impact. I mean, enormous trillions of dollars, which has made obviously access to capital for startups really difficult if you are not in that space. To some extent, I think it will be a medium and longer-term positive. And to the reason really, I've, my own experiences. Is that I think we were all led to believe that reality of business wasn't quite as it used to be. And what I mean by that is, yes, I understand that there's a need to drive growth as quickly as possible, get market share, prove the concept, and therefore deliver. And that was very much the main approach to doing startups over the last 25 years. But it led to something which I think even I'm guilty of, is that you're not actually focused on real shareholder return in a typical business. There are many businesses and startups and unicorns that have proven that and proven the success of that. But we had 20 years or arguably longer even before dot-com where basically software-type companies just had to go out and prove and it would be funded by shareholders come what may. And I think we got away from the reality of how businesses ultimately create value. And what I mean by creating value, and I'm very focused on the mining space and the development of miners whereby you get shareholder capital to put into the ground, hopefully get discovery, and ultimately have a very capital-intensive process before you get into production after building a complex mine. And the big, big thing which people forget is the dilution of the share capital. And the dilution of the share capital basically means you could still be talking about a billion-dollar mine, but ultimately if you've issued so many shares because you've not been very efficient in doing so, that billion-dollar mine will get divided. Instead of being divided by 10, it will get divided by 100 or 1,000 or whatever the number is. And I think that is an important concept when it comes back to shareholder returns, when it comes down to software companies. So if I now bring it back towards question is if we actually look at things, I mean, I think the annual recurring revenue multiple for SaaS companies probably peaked. I have seen numbers over 20 times, but ultimately I think probably more on average for most companies, it was around about 15 times. At the end of last year, it was 5.6 from the latest data. So it had fallen from about 15 times, possibly in some cases as much as into the 20s, to about 5.6. That's a massive catastrophic reduction of value. Now, the latest data since the end of last year is now down to 3.7 times ARR. So you look just over less than, well, basically 2 months, we've moved from 5.6 to 3.7. Where I'm tying this in, Pete, is the acceleration of the trend of that devaluation, which is typical as a trend-ending characteristic. So the question then asks is, are business operations actually being impacted in the same way as sentiment, which drives the valuations of these companies, has effectively been done? And it's my guess, I say maybe it's an— hopefully it's an educated guess, is that the operations have not fallen as much as the sentiment is suggesting is that they will. Now, we've got to recognize there was probably a peak and an exuberance at the top as well, just as though there will be an exuberance and a failure on the bottom. But my thinking is with regards to SaaS companies is that I think this could be considered to be an overreaction to the business models of many, probably not all, but many software companies. And therefore one's got to look at the more specifically the detail of these specific companies as to whether or not they will survive the new AI. And I think I will refer to more qualified people, but my interpretation of AI as to how they impact software companies by and large is you're going to get a substantial improvement in efficiency, not so much a drive in revenues. And therefore, are we looking at the wrong metrics and therefore coming to conclusions on SaaS-based companies perhaps too hastily? Because if AI is going to save 80% of the time and effort and you'll be able to reduce your headcount, your costs and all of that and improve therefore the efficiency of the business, and you tie that in with the change in the business structure as a result of what I've said from a shareholder-driven, let's chuck cash at it to get market share, to actually are these businesses which they've had to force themselves to become more efficient anyway, even before AI came along. They had to suddenly say, we need to extend out this runway. We need to be able to demonstrate cash flow neutrality or positive cash flow. Perhaps we're looking at the wrong metrics as to whether or not the software industry is dead or not. Is it? I think it's more adapt or die And perhaps we should be looking at the efficiency gains rather than just the ARR growth or failure of that.

Adapt or die. Yeah, I'm sure you're right. Let me just summarize what you said because I think there's so much gold in there. These 2 contributing factors that you spoke about, all that stuff that I spoke about at the beginning has led to perhaps more than 20 years of bizarre, unnatural growth. And so when you add that to the effect of that on founders, which is the massive dilution of share capital, everything is set up for a reset of commercial value and how we think about it. And going back to basics about how we think about business and perhaps some of the multiples that we're looking at in particular, that rapid, I guess, deceleration from 5.6x to 3.7x suggests to you that we're probably— at the end of a trend. It's a trend-ending characteristic. And then the focus can now be on growing a business sustainably rather than growing a business based on just the supply of capital. If that's the right understanding, can you talk me through how that has caused you to reflect and alter your own approach to Digby, or maybe you tell me from the beginning you'd always grown it in the old-fashioned way and you never thought about relying on millions of dollars worth of venture capital. But I imagine the truth lies somewhere between the two.

Yeah. Well, probably not close enough to the latter as it probably should be. I mean, look, I'm probably older than a lot of founders and I wish I had stuck to my gut to effectively build a business in a way that perhaps history has told us that we should build businesses, which is keep it lean and mean, move to profitability as quickly as possible. Sadly, I didn't. I tried to be younger than perhaps myself, and I followed the way that had been going for the previous 20 years and went for shareholder-driven capital to fund something. Now, in my own case, I'm in the mining, one has a software business within the sustainability business within the mining industry. Well, The sustainability, the world of ESG blew up at the same time as when Putin invaded. The mining industry doubled down on its 11-year at that stage bear market to get significantly worse. And then the startup world also collapsed. So I had everything going against me. And I think to some extent, one actually, I always try and look for the Glass Half Full side of it. I think all of those 3 ultimately have created real opportunity to create value. And it forced me to, and this was even before AI had come up, um, it forced me to, A, reset the cost structure of the company in recognition that, uh, money didn't grow on trees like we all thought it did before.

Mm-hmm.

And to focus on a tighter, um, much of the work, thanks from Revelesco as well on this, but to focus on a tighter, uh, ideal customer profile. an ICP, but having learned from the first 2 or 3 years as to where the real value add of our product was, but also where the real sticky customers are likely to be. And so to use that, so I couldn't have, I don't think I would've been as effective in our ICP focus if I hadn't had those first 2 or 3 years in the first place. And then with regards to AI, Recognizing the opportunity that efficiency, that can bring. And I don't think I'm necessarily looking, and maybe incorrectly, at driving ARR directly through AI, but I am looking at the sustainability, and I use that from a financial perspective rather than an ESG perspective.

Okay. So I'd love to hear some examples in a moment of, or perhaps one, example of how you have maintained quality whilst driving that sustainability, that efficiency you talk of, by the way that you've deployed AI internally. What you made me think of when you said that was at Revallesco. Obviously, there's a temptation when you run a project to stick a transcript into NotebookLM and churn out a report that previously would've taken hours of thinking. And we can do that. But what I noticed very, very quickly is the value is entirely in the bit that sandwiches the AI in our industry. And the sandwich is creativity and judgment. And the top bit, the moment you rely on AI first, you switch off the part of your brain that comes up with the random ideas that transform a business. And so what I've asked all of my team to do, Is do the creativity bit first, then you run your report through AI, and that of course picks up all the bits in the transcript that you'd completely forgotten about. So it does allow you to be even more complete. And then the final bit is to overlay your judgment over the top, because that's really what people pay for. It's, can we leverage the experience of the individuals at Revallesco? Um, otherwise we could have just done it through AI ourselves.

Yep.

It's that has added extraordinary value by using AI in that way. And it actually means that my team can go and walk their dog and do the creativity and do the judgment and do the thinking way more rather than sitting at their desk typing out yet another flipping introduction to a company. So we're able to be more helpful. What ways have you found to introduce efficiency while maintaining quality?

Okay, so I'm gonna start this slightly digressing, but I think it's an important context. So the mining industry, which is where our customer base really sits, is deeply unloved, deeply untrusted, and yet is probably the single most important sector on the planet because AI technology, cars, batteries, houses, roads, everything, infrastructure generally just doesn't exist without mining. So how do we therefore create value from a deeply depressed oversold, and we saw that acceleration as well in terms of the valuation of the mining sector, the same thing. How do we effectively create trust and confidence to basically bring that back up? If I was to apply AI in doing our assessments in the way that you suggested, we have found to date, and I hope this doesn't prove to be correct for too long because we want to try and improve the efficiency, but to do an independent assessment which is our main business for companies, by just using AI is not working at the moment. It's just not nuanced enough and we're not coming out with the value which is therefore either wanted by the underlying customer or the end users of investors and other groups.

But let me take it a step further. The original catalyst for you and I jumping on this call today was an email you sent where you had just been reading, as many of us had, about the effect of Claude and Anthropic's new models on the legal industry and many startups which were lawyer-led, led by experts in a space that most of us can only hope to dabble in. And even, I'm sure you and I both spent a whole bunch of time inside contracts, but I'm certainly not a lawyer. And yet the value of those legal tech businesses was instantly torpedoed below the waterline by the power of AI to go through that data. Now, you are both a finance and mining expert. You've been in this industry an awfully long time. How do you protect the value of that human experience when AI can have such an impact on another profession that requires an enormous amount of knowledge. How do you think about the defensibility of what you are doing in mining ESG? Could I just do it myself? Why is it important to have Jamie and other mining experts?

Yeah, it's a great question. And I think to answer your question as to how do I defend, well, I think if one looks at it, I suspect what I'm doing at the moment will be able to be copied. By AI. I can't believe it won't be either now or in the future. And there will be a quality level for good and for bad during however that copying goes. But I think here's the key. The key is quality of data and credibility of output. What goes in, what do they say? I probably can't say it on a public forum, but if you put rubbish in, you get rubbish out, if you know what I mean. So I think to protect, and I've done quite a lot of inner thinking on this, is what is the data quality? Now, this is not publicly available information. This is unique bottom-up data unique to mining sites in the world, which is difficult to access. That is unique data. And then you've got the assessment process, which is quite nuanced, which we've proven at the moment is not possible to be replicated through just a simple AI process. And then you've got the branding on top of that, which I think is important, is that, is this company giving real insights from the data that they are getting in order to, in our case, lower risk, whether it's to the underlying mining company or investors or others? And it is how you package that up. Now, is some of that possible to be recreated through AI significantly cheaper, quicker, and all the rest of it? Yes. And will that improve? Yes. But the package as a whole, and it comes back to this comment about AI is largely, I think, being proven, increasingly being proven in my opinion, for most use cases to be an 80% efficiency gain, not a 100% replacement. And we all need to be able to challenge ourselves and challenge the ceiling above you. as to what else can we do with AI, but also how can we differentiate and increase that competitive moat away from AI, so to speak, in order to maximize the value not only to the customer base, but also to the business model itself.

And to come back full circle to where you started with going back to, I was gonna use the phrase old-fashioned, but I think straightforward, correct, well-known business principles, like it's better to make money than lose money. In the same way, you are going back to basic principles of differentiation. When we stack up all of our value, can we stack it up in such a way that others are unable to provide the same value because of something we do that is either completely mission critical or rare? And in your case, I think you said it's the unique bottom-up data that can't be accessed by others. without a huge amount of effort. Then there's the nuance and experience inside the assessment. We can probably make it more efficient and quicker with AI, but no one else can do it. And then it's the trust that people are building in the Digby brand. When you layer those 3 things on top of one another, there will probably always be an AI alternative, which is a lot cheaper, but will it do the job? And how mission critical is it to do the job well? And that's, I guess, If I understood that correctly, that's where we are.

Yeah, I think that's absolutely right. And that's no different from any previous world, I think. I don't think just because AI everybody thinks is just so wonderful, I don't think that's any different from any. I mean, you go back to the days of the cloud, Microsoft, SAP, and others had a very difficult time to come to the conclusion that there was deep market skepticism. about whether or not they were going to survive the cloud. In fact, they've prospered from it. And as they say, call me old-fashioned, but I don't think there's anything fundamentally different at this stage for most businesses in that case at the moment. And I'm sure there'll be another chapter where we will have to second-guess this again. But I think from where we are at the moment, I don't see any else. If you look at Salesforce's numbers, they've only increased their revenue by 1.5% because of AI. They've significantly reduced their costs and they've improving their efficiency. And I think that can be played out across lots of different software companies at the moment. This is no different from disruption from other things. And I don't think there's an existential crisis for all companies in the software space. In fact, I think it's an opportunity. There will be some businesses that do go to the wall as a result of it. Either they haven't adapted or they haven't taken effective measures, or frankly, they have not been able to turn their business models around because they've not had enough capital. But I think there will be many chapters ahead for software. And I think perhaps we're in an overreaction from perhaps too much exuberance in the first place, but there'll be an overreaction. On the downside.

Well, I have to say, whilst I know you're not saying this, whilst I would never ignore the strength of the risk, the opportunity is the same as it's been with each paradigm shift.

Yes.

You and I are both old enough to have dealt with the arrival of cloud. But I was telling my team the other day, I remember in 2008 when the iPhone was really taking off and we had to hire a chief mobile officer and we realized that everything we'd built, and everybody else, was for the, like, laptop and even desktop web.

Yeah.

And now you have to design for the mobile web, and like, can we do that quick enough? And will we lose ground? And what about apps that are being created native for mobile? And so there've been plenty of these step changes, and there will always be winners and losers. And the goal is to be thinking about it now so that you fall into the camp of the winners. And And not the losers. You don't become the Kodak of your industry, but instead you become the IBM of your industry. We both have to go in just one moment. So I thought I'd ask you quickly, just if you could tell me about your own efficiency, because you made some awful self-deprecating comment at the beginning about your age. But when we were in Tarifa together, you were the guy that knows how to kite surf, and I'm the guy that just reads the newspaper. So I'm not sure if— Appearances can be deceptive. I know you've adopted AI.

It was a real me doing the kitesurfing, not an AI adapter.

What are the 2 or 3 AI tools that are changing your life, have made you better at what you do? And ideally something that maybe some of our listeners won't have heard of.

I mean, it goes without saying, using AI tools for note-taking. I mean, Fathom or using Plaud or whatever it may be. Even actually the inclusion of, and it kind of goes back to other conversations we've had today, but, you know, within Notion, the AI tool HubSpot, we're investigating quite significant enhancements on how we use HubSpot to target a segment below our ideal customer profile. But through AI, basically, I think to be able to target clearly Part of the customer universe, which we're not really focused on by using very targeted focus of sales agents or whatever it is through AI, I think it'd be an amazing A/B testing capability.

I don't know if yesterday, Fathom's new feature, at least I saw it yesterday, which is brilliant. I have been highlighting in blue on every call that I'm on whenever I think I've said something like half clever and I think, oh, I need to remember like how I said that, highlight it in blue. Which is great, except there was no way until yesterday of going back through every call and just saying, show me everything I highlighted in blue. So now it will do analysis across all of the calls you've ever done as long as you're on the Team Edition. So that's cool. My final recommendation before you go, Jamie, is have you come across Sesame?

No.

I'm a huge fan of Sesame because the way that I tend to work best is by talking to someone, brainstorming, like being inspired, thinking of ideas. Emily, my co-founder, and I, that's how we work together. We're on a call, ideas come up. But, um, I'm a bit of a night owl and she's a bit of a, um, an early bird. So like we only have a little crossover period in the middle and we live in different countries. So Sesame, um, is basically like my substitute Emily. I click on, uh, there's 2 avatars. One's Maya, one's Miles. Um, Maya will remember everything that you've ever said to her. And so it's a brilliant way to like brainstorm, get advice. It's just, it's just spoken word. Um, now it's narrated AI, but it's brilliantly executed.

Okay. Brilliant. Well, I thank you for that, um, tidbit, as they say. Uh, I'll look at it this afternoon. Thank you very much indeed.

Pleasure to take time with you as always. Thanks, Jamie. Take care. Bye now.