What VC's Are Looking For in AI Startups Today

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, AIbox.ai, 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 look at

I mean, 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 the one of the first interviews or kind of data points I got on what VCs are looking at as far

as invested in AI companies today that maybe they weren't in the past is from Aaron Holiday. He's

a managing partner at six 45 ventures. And by the way, tech crunch did a whole run down 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 six 45 ventures. And he says that the categories 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 a mission

critical workflow. 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 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 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. 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 AI 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 your, what they want tools to be able to do is have

some sort of custom data set, some sort of, you know, deep integration into something that's

super, super critical. And it's not something that just like a chat UBT or anthropic can replicate

easily. And the reason being anthropic right now is rolling out all of these new, 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 rated us a ton of money. And I'm hearing, you know, anecdotal

stories from people who are saying like, you know, I've used Harvey for my law firm. And now I'm

using anthropologists 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, you know, 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 mode. Maybe you ran a, you know, an FAQ

legal website. So you have all of this data on, you know, legal FAQ questions. I've seen some

startups do this. And that is like a proprietary data mode where maybe you have, you know,

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 Rebenzki of Altal R capital said that basically was arguing that

shallow product depth is a really red flag, a big red flag. He said, if you're differentiation,

mostly lives in UI and automation, that's no longer enough. The barrier to entry is dropped,

which makes building a real mo 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 page. 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 and 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 can 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 you see it's like just showing you what it looks like

through the lens of developer tools. So we point 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 is 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 mode. 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 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 and link directly to the software. And so

instead of me having to go and set up some sort of integration straight into my meta ads account that

you know, it's kind of create this automation and I got to 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 you don't need this kind of like integration. You don't need the Zapier because

the agent is just taking over your control of your screen and the integration is just literally

the computer being taken over. I do your men also added that the workflow automation and 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 Raya Benzki 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. I'll fall into the category. Now,

are these not going to be successful companies? No. And this is perhaps what I think 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, Cal AI just got acquired by my fitness pal that acquired them.

And Cal AI 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 a chat GPT. But guess what? They had 15

million downloads. They had over $30 million 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 wrappers.

Well, if you have another angle, like if you have the growth kind of locked in and you could get

$30 million annual recurring revenue, guess what? You're going to get an acquisition. 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. 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 taken

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 and insightful into what we're going to start seeing more and more coming out of AI.

Make sure to leave a rating or a review wherever you get your podcasts. If you could, it helps

us show a ton and as always, make sure to go check out AIbox.ai if you want to get access to over

40 of the top AI models for 899 a month. And of course, you also get 20% off if you get the annual

plan. All right, I'll leave a link in the description for that. Catch you in the next episode.