Rule 30 co-founders (Guy Conway, Felix Winckler and Damian Cristian) and Boardy co-founder and CEO Andrew D’Souza
It’s no secret that a lot of investors, like everyone else in the tech industry, are using AI to speed up workflows. From recording and transcribing meetings to researching markets and summarising diligence, tools like Fireflies, Granola and ChatGPT have quietly become part of the venture capital stack.
But some companies are pushing far beyond workflow automations and even having AI run their funds entirely.
Two not-so-traditional venture funds, Rule30 and Boardy, have both embedded AI so firmly in their deal making process that it’s not just advising humans — it’s shaping, and in some cases determining, where capital is deployed.
Rule30, an AI research lab for venture that now has its own fund, describes itself as having a “fully algorithmic approach”. Investing in pre-seed and seed-stage startups across Europe and North America, typical check sizes range from $100k to $300k per investment and are decided entirely by the startup’s probabilistic decision engine that the co-founders, Guy Conway and Damien Cristian have spent the last five years building.
The two previously ran an accelerator, Zero 1, and were frustrated by the traditional process of startups getting financed by VCs.
“In our view it was way too much gut feel, way too much intuition by quite often very junior analysts who were gatekeeping a process,” Conway tells Pathfounders.
“Personally, we feel like meeting people to judge personalities is a very bad way of doing it, unless you’re specifically trained in assessing personalities,” he adds. “And we’ve never met a VC who has done any sort of psychology or personality training.”
The pattern matching problem
Venture has long had a problem with “pattern matching”, that is, investors using patterns and experiences from the past to make decisions about new investments, and often using patterns and experiences from their past. They might be more inclined to back founders who look like them, went to their university or worked where they worked.
In 2013, Y Combinator co-founder Paul Graham was quoted in the New York Times saying “I can be tricked by anyone who looks like Mark Zuckerberg”. He was, he says, clearly joking, but there’s a reason the internet ran with the meme.
A Founders Forum study found 78% of venture capitalists use “founder pattern matching” as a factor in their decision making. That, combined with the fact that 82% of venture deals come through warm introductions, creates a barrier for founders that fall outside existing demographic patterns.
The promise of algorithmic investing is that data, not intuition, could flatten these barriers.
But if AI systems are trained on historical venture data — itself shaped by decades of bias — can they really produce fairer outcomes?
Building an algorithmic VC
Conway and Cristian have spent the last five years building data sets and data pipelines, scraping reams and reams of data from Pitchbook, Crunchbase, LinkedIn and Deel to build their own algorithmic or quant VC fund that uses data models and algorithms to make investment decisions.
The startup saw its first close earlier this year and has made 12 deals so far. Backed by Isomer Capital as anchor LP, it is two thirds of the way towards its $15m target.
Originally the algorithm was identifying the top 10% of founders, but now it can identify the top 3% and top 0.1% of startups it looks at and then sort them into bands before backing them systematically.
We have no human in the IC. The humans can’t say to do a deal even if the models say don’t do a deal. The human’s job is to execute the transaction
Conway says they’re on the lookout for “outlier founders”: “the founders that are going to build businesses that are billions of dollars in value”, as he puts it.
In addition to opening up the decision engine to founders so they can enter their own information and see whether Rule30 would place them in their top 0.1% of founders, they analyse thousands of profiles of founders who are listed as being “in stealth mode” on LinkedIn to figure out who to invest in before anyone else.
“Our conviction is that research, data and compute will power the next generation of venture funds,” Cristian said in a statement.
The AI analyses the founder’s experience, how that relates to the company they’re building, how their career “accelerates or deviate from the normal average”, at what point in time on a company’s journey they worked there — were they at Airbnb from day one or did they spend a month there as an intern last year — their educational background, their network and how it has evolved, the list goes on.
This is where the critical tension emerges: with the algorithm trying to pattern match based on the data it’s been trained on, will investments remain skewed to certain demographics based on the data even with the “gut feel” decision making process removed?
It’s hard to say without seeing the data, and Conway notes the algorithm doesn’t look at names, photos or other aspects of a founder’s identity, but it does look at education and geography, both factors that have historically correlated with demographic patterns in VC.
Rule30’s process
Rule30 looks at a few hundred profiles every week pulled from web scrapers, third party data and suggestions from angels. These are passed through models that create a prioritised list of targets, and it is only at this point that a human joins the loop — but not for long.
There are three reasons that a person enters the process at this stage.
One is that founders want to know the investors. Two is to better understand the fundraising plans of the founder. Three is to then plug the transcript of that half hour chat with the founder back into a model and refine their score to come up with a prioritised list of the best founders and deals and then to try to win allocation in the deal.
The goal is not for the team to talk to the founder and see if they personally think it would be a good investment.
“We have no human in the IC [Investment Committee]. The humans can’t say to do a deal even if the models say don’t do a deal. The human’s job is to execute the transaction,” Conway says.
“If you’re bringing it to an IC, what you find is they will follow the data points where it agrees with what people want to do, and they’ll discard those data points where it tells them they can’t do something that they’re trying to do. We’re quite adamant we will only deploy where the algorithm tells us to deploy.”
A more human-like approach
At Boardy, there’s a bit more of a human in the loop, but it’s still largely up to “Boardy” himself, an AI “superconnector”, to make investing decisions.
The startup has been around for more than a year — it raised $3m pre-seed last October and $8m from Creandum just months later — but has only just developed from a straight up networking startup to now making its own investments as well.
Boardy is designed to be as much like a human as possible, but the most useful human you could possibly hope to come across. There’s no app, no login or dashboard, just a number on WhatsApp you can text or, ideally, call and talk to directly.
You can actually call Boardy and talk to him about the fund and the plans. He might hallucinate a few things, but I think for the most part he’s got most of the answers there.
Voice based from the start, Boardy has somehow managed to get people having one hour phone call conversations in a time where people are less inclined to speak on the phone than ever before.
“Maybe it’s in some ways easier to talk to an AI on the phone because you don’t feel like you’re, you know, taking [time] away from somebody else,” Boardy co-founder and CEO Andrew D’Souza tells Pathfounders. “You don’t feel like you’re being judged as much.”
Boardy is designed to be the ultimate, zhuzhed up, non-judgemental board member (hence the name, but I enjoy that it feels a bit like a surfer too), connecting founders, investors and creators.
It feels weird to attribute he/him pronouns to an AI that’s a suit with a box for a head, but that’s how D’Souza talks about him, and Boardy even sounds distinctly un-AI when you give him a ring. In fact, he sounds like a cheerful Aussie man.
As it turns out, this is reminiscent of D’Souza’s first boss at McKinsey in Toronto — something he didn’t realise until six months into building Boardy. “He was just a charming Australian guy and all the clients loved him,” he says.
After more than a year of connecting founders and investors and finding the best startups for funds to invest in, it seems obvious that the next step would be for Boardy to get involved in these deals. You can even speak to Boardy about it.
“You can actually call Boardy and talk to him about the fund and the plans,” D’Souza says. “He might hallucinate a few things, but I think for the most part he’s got most of the answers there.”
Boardy Ventures
When I spoke to D’Souza about a month ago, I asked if he’d thought about having Boardy make his own investments. A few weeks later and the startup has launched Boardy Ventures, a flexible fund targeting early-stage founders.
“We’ll probably have conversations with 50 to 100,000 founders over the next 12 months who are raising and even if we invest in the top 100 or few hundred, I think it’ll be a pretty good returning fund,” D’Souza says.
Similarly to Rule30, Boardy will not be leading rounds and, in partnership with AngelList, will be writing checks around the $100k mark. The company says it has had more than 1,500 inbound requests to be LPs. To expand its reach beyond typical VC networks, Boardy Ventures is recruiting 1,000 “deal partners” (previously venture scouts) globally, offering them 50% carry to help identify founders outside the usual channels.
Where the startup is already talking to founders and connecting them to investors, it makes sense that it would be involved in the ones it thinks are the best.
Once Boardy has a conversation with a founder looking to fundraise that he thinks the company should be a part of, he escalates that to the humans in the room.
Boardy
The plan is to put together a group of investing partners — a human investment committee that will probably be different for each startup Boardy considers investing in — and Boardy will go to a few humans in his network and ask for their opinions.
There will be a call, Boardy will listen in, form an opinion and while a person on the team will be the official decision maker, they’ll “go largely based on Boardy’s recommendation”, D’Souza says.
Currently Boardy Ventures is not the main focus of the company — Boardy’s main goal is still to try to make the best connections, but sometimes that will be himself.
“I think one of the nice things is that, yes, Boardy can invest and he can put skin in the game,” D’Souza says. “But the primary objective is actually to introduce you to the best possible investor for you and then sort of co-invest with that best possible investor in some cases.”
Separately, more than 700 firms have apparently reached out to have Boardy join as a venture partner — including Creandum, who led their last raise, but Boardy wouldn’t tell me who else was locked in when I called him to ask. Firms can hire Boardy to be on their team and then “pay him a six figure salary the same way you would pay a non executive director or a VP of biz dev”.
Can algorithms outperform intuition?
As AI-powered funds like Rule30 and Boardy emerge, they're forcing the venture capital industry to confront uncomfortable questions about its own decision-making processes. If most VCs admit to pattern matching and the majority of deals come through warm introductions, can data-driven approaches level the playing field?
The answer may depend on how these algorithms are built and what data they're trained on.
Rule30's purely algorithmic approach could, in theory, remove human bias entirely, but it risks encoding historical patterns into its models. Boardy's hybrid model keeps humans in the loop, potentially preserving some of the relationship-building that makes venture capital work — or reintroducing the biases it aims to avoid.
What's clear is that both approaches will generate data that traditional VCs can't: concrete evidence of whether algorithms can consistently identify successful founders better than human judgment. In an industry built on instinct and reputation, that might be the most disruptive outcome.
Company data:
Founders: Guy Conway and Damien Cristian
Portfolio: Aiomics, Autonomous Minds, Emerge, Gyre Energy, Jiro, Kiin Bio, Optavex, Ploy, Stack8s, Synkka, Unbound
Founders: Andrew D’Souza (CEO, former founder and CEO of Clearco), Matt Stein, Shen Sivananthan and brothers Ankur Boyed and Abhinav Boyed (former CTO)
Team: Around 25