Welcome to the podcast.
I'm your host, Jaden Schaefer.
Today, I want to talk about what investors are doing in 2026 when it comes to investing in AI startups.
And I think it's interesting because right now, investors are basically telling you what they aren't looking for anymore in AI SaaS companies.
It has shifted a lot, which is interesting for me, being someone who has my own SaaS company AIboxai, which I'm sure you've heard me talk about before because we recently did an entire redesign of the platform, where you get access to over 50 of the top AI models in one place for 899 a month.
But I think this is, broadly speaking, pretty interesting and important for the overall AI industry, because what investors are looking for here.
Number one means this is what people are building, but this is also what we're likely to see more of when it comes to updates inside of the AI industry as a whole.
So this is what we're going to jump into on the podcast today.
What I think is interesting is investors have poured billions of dollars into AI for the last few years.
This isn't a trend that is slowing down.
And I think all of this technology really has played a huge role in Silicon Valley's basically their priorities and a lot of what comes out of the global tech industry.
I think even in a market right now that is obviously very obsessed with AI.
I mean, if you, if you look at every, basically every single company that is raising money now is no longer just a SaaS.
It's like an AI company.
And so I think, while every company has kind of added AI to their sales like their pitch deck, basically for VCs, I think it's becoming a lot more selective on who's actually getting money right.
You can't just put AI on your pitch deck and get money.
So, according to one of the first interviews or kind of data points I got on what VCs are looking at as far as investing in AI companies today, that maybe they weren't in the past is from Aaron Holiday.
He's a managing partner at 645 Ventures.
And by the way, TechCrunch did a whole rundown where they interviewed a bunch of different people.
I'm grabbing some quotes there and also grabbing some data from the overall industry that we're tying together in kind of this episode in this report.
But Aaron Holiday, he's a managing partner at 645 Ventures.
And he says that the category is still getting the most interest are AI native infrastructure.
So that's vertical SaaS built on proprietary data systems of action that actually complete tasks, and platforms embedded deeply into mission critical workflows.
So basically, in other words, products that own something really essential.
And there's a keyword I think he said in here that I 100 agree with, And that is, he said AI.
That actually completes something.
So I think there's a lot of this, a lot of these startups that were like hey look, we have like a SaaS, we have a tool, and then we suck chat GPT on top of it.
And you could chat with chat GPT and it can give you like ideas about what you're looking at.
In my opinion, that's very, I mean, basically that's just the original SaaS.
It's not super interesting.
It might give you like some ideas or help you like troubleshoot or you don't need their customer support as much.
But what I'm talking about when I see AI, and what I think a lot of these investors are looking for, is AI that actually completes something.
In the past maybe I had to manually write a title and description for my podcast and And today AI, can you know, grab the transcripts of the audio file and do that for me?
And if that's actually like accomplishing something for me, it's useful.
Whereas if it was just like I don't know, a chat bar on the side where it's like you know, ask me what would be a good description for this type in your title and I'll give you some ideas.
Like that is not useful.
It's not automatically doing something for me.
And that example I just gave you is like very basic.
I mean, I think ideally you would upload an audio file and it would fill out all the data and automatically find the best time to post it and look at your calendar and blah, blah, blah, blah, blah.
Like it's just going through and automatically doing stuff.
That is what they're looking for, not a chat bar on the side.
Okay, so what are they less interested in?
What are investors less interested in investing in AI right now?
And what that is, is thin workflow layers.
So generic horizontal tools, light product management software and surface level analytics.
If an agent can replicate the core value quickly, I think investors are not seeing this as very defensible.
So maybe even some of my previous examples weren't the greatest because, in a sense, what they want tools to be able to do is have some sort of custom data set, some sort of deep integration into something that's super, super critical.
And it's not something that just like a chat GPT or Anthropic can replicate easily.
And the reason being, Anthropic right now is rolling out all of these new tools, right?
They're doing like Anthropic for finance and Anthropic for legal.
And basically you have a company like even Harvey AI, who's raised a ton of money, and I'm hearing anecdotal stories from people who are saying I've used Harvey for my law firm and now I'm using Anthropic just rolled out Anthropic for legal and I don't need this whole other tool.
I just use my Anthropic account, and all of a sudden, this is just as good as Harvey.
So It's really interesting what is actually being seen as defensible today.
So Abdul Abdirhan of F Prime added that vertical software without any proprietary data modes is no longer super compelling.
So they actually want you to have a data moat.
Maybe you ran an FAQ legal website so you have all of this data on legal FAQ questions.
I've seen some startups do this.
And that is like a proprietary data moat where maybe you have information that no one else has.
Or maybe you have information because you have a company and you can see what your users, you know what their behavior is.
And so that could be a proprietary data mode.
But basically you want some sort of data that your competitors can't just easily knock off and clone.
Igor Ryabinsky of Altol R Capital said that basically was arguing that shallow product depth is a really red flag, a big red flag.
He said, if your differentiation mostly lives in UI and automation, that's no longer enough.
The barrier to entry is dropped, which makes building a real moat a lot harder.
I think for new companies that means that building around you know true workflow ownership and a really clear understanding of the problem from day one is super, super important.
Massive code bases are not an advantage anymore, right?
Just being like look, we have this massive code base we've worked on for a long time.
Speed focus adaptability, all of those things I think I would argue are much more important.
And even pricing models are shifting a lot like these kind of.
I think there's a lot of software today that has like these really kind of set in stone pricing per seat or per subscription.
These are looking a lot weaker compared to the consumption based approaches where it's like look, we just need to use like X amount of tokens every month, which is kind of the approach that I'm doing at AI box, where you get an account and you can just get more tokens.
The more you pay, the more tokens you get.
We have a bunch of like apps and tools you can use.
And if you use them a lot, you pay a lot.
If you use them a little, you can be on a low subscription tier and pay a little for them.
Jake Sapper, who's the general partner at Emergence Capital.
He's kind of trying to frame the shift that's going on right now.
And he's like just showing you what it looks like through the lens of developer tools.
So he pointed to the contrast between Cursor and Cloud Code as kind of where things are going.
He said one owns the developer's workflow, the other just executes the task, which I think basically shows that increasingly developers are choosing execution over process right.
Like they don't just want you to own the workflow.
They want you to actually get the thing done, which is what Cloud Code is doing really really well right now.
It's the number one tool we use at AI Box.
So, the shift I think that we're seeing right now is a lot of big implications.
If agents are doing the work, then a lot of the traditional kind of workflow stickiness is becoming a lot less relevant.
In the past, getting humans to operate inside of your software was a pretty powerful moat.
Now, if an agent can perform the task directly, then owning the human interface doesn't actually matter that much, in my opinion.
I think you're looking at a lot of these integrations that you know.
You pay companies like Zapier and Bubble and Make.
I think a lot of those are losing their edge, right?
Because, as you have things like model context protocol, it's going to make it a lot easier for the AI agents to just go in and link directly to the software.
And so instead of me having to go and set up some sort of integration straight into my you know meta ads account that you know it's kind of create this automation and i gotta go tweak it and it's really complex, instead i could just go to my agent that's running on my computer, my like open claw or whatever, and say like hey, go to my meta account, this is the login, go change these things, go make these tweaks and and you don't need this kind of like integration, you don't need this zapier, because the agent is just taking over your control of your screen and the integration is just literally the computer being taken over.
Abdirahman also added that the workflow automation, a lot of the task coordination tools, are becoming a lot less necessary if agents simply execute the tasks themselves.
A lot of public SaaS companies are already feeling pressure as a lot of these kind of these AI native startups are emerging.
They have a lot more efficient models and architecture.
And I think Ryabinsky put it really plainly.
He was kind of saying what VCs are looking at.
And he said, the SaaS companies struggling to raise capital are the ones that can easily be rebuilt.
So generic productivity tools, product management platforms, basic CRM clones and it's kind of thin AI wrappers on top of existing APIs all fall into the category.
Now, are these not going to be successful companies?
No.
And this is perhaps what I think It is a really important data point outside of investors and VCs, because we just saw a massive exit in AI from a company that was, I think, CalAI just got acquired by my fitness pal that acquired them.
And CalAI just helps you track your calories.
It helps you lose weight.
And a lot of people are like, oh, man, this is just a thin wrapper on top of ChatGPT.
But guess what?
They had 15 million downloads.
They had over 30 million dollars in annualized revenue.
And really a lot of their unlock was that they really hacked the growth, hacking on social media, TikTok and making shorts.
And so I think, on the one hand, you have a lot of these people that are saying you know oh, look like you can't invest in these companies that are.
They're thin kind of rappers.
Well, if you have another angle, like if you have the growth kind of locked in and you can get 30 million dollars in annual recurring revenue,
Guess what?
You're going to get an acquisition.
And so that's what happened.
My fitness pal went and acquired them.
So I do think that there is some interesting points here.
And, you know, did they raise an insane amount of VC funding?
I mean, not necessarily.
That's not something that you have to do if you can scale it without it.
Although a lot of, you know, in a lot of cases, this helps you get get started.
So I think what remains a really attractive thing for venture capitalists and for people institutional money looking to invest is depth and It's kind of the ownership of workflows.
It's kind of the control of data.
It's a lot of real domain expertise that they'll pay for if you're an expert in a specific area.
I think investors are reallocating capital towards businesses that have a lot of those assets and they're taking it away from products that can be copied with very minimal effort.
I think in a world that is, you know, quickly becoming AI, first becoming different isn't just about kind of adding an automation.
It's about really owning something that agents can't replace easily.
So it's an interesting time.
A lot is changing right now.
Thank you so much for tuning into the podcast.
I hope this was super useful, insightful into what we're going to start seeing more and more coming out of AI.
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Catch you in the next episode.
