Introduction to Tango.vc
Investing in preseed and seed stage AI, machine learning, and robotics startups
I founded Tango.vc to back exceptional founders building in AI, machine learning, and robotics. Here’s who I am, what we do, and why it matters. In Summary:
I have deep technical experience with NYU, CMU, iRobot, Facebook, Dropbox, Lyft, and multiple startups
We invest in preseed and seed stage startups in machine learning, AI, and robotics.
We’ve backed over 70 companies. Our best investments include Cruise Automation, Stoke Space, Albedo, Freshpaint, Clone Robotics, and Lovable.
We support founders with experience in engineering, product, growth, hiring, and fundraising.
We write $100K to $250K checks
We’re raising and deploying Fund III targeting $25M.
We’re spending management fees to build software to help founders raise called Attention AI
Get in touch at ivan@tango.vc
My Background
Venture funds are about their people, and Tango is a solo GP by me, Ivan Kirigin. So here is a bit about my background and experience.
I’m an engineer by training, with a background in robotics and computer vision from NYU and CMU. I worked on tracking, surveillance, bomb disposal robots, and self driving at iRobot for the Darpa Urban Grand Challenge.
This was before deep learning started working in vision, and because I wanted to ship products I did a startup outside robotics with Tipjoy in YC W08, one of the earliest cohorts. The YC founders and partners are great and I love that community. I then worked at Facebook where I understood web scale, and then Dropbox, where I helped early growth from 4M to 100M users.
My second startup was a mix of ML and social graphs with YesGraph in YC W15. Lyft acquired the company and I helped drive growth by building an ML suite to automate a $250M marketing budget. Automating a job felt like a finish, so I also worked in Lyft’s self driving division, Level 5. It was fun to see what changed (perception!) and what didn’t, like 3D scene structure from motion.
My first angel investment was in Cruise Automation in 2013, which sold for over $1B in 2016. It’s wild growth and exit so fast, but it figures that the first company to compel me to invest was very high quality.
I left Lyft to invest full time in late 2019. After Cruise, how hard could it be, ha! After some angel investments, I founded Tango.vc in 2020.
Fund Structure
Both Fund I and Fund II were around $5M, which is very small by VC standards. Now I’m raising Fund III targeting $25M – email me at ivan@tango.vc if you’d like to become an LP.
Fund III is actually already active, writing $100K checks into preseed and seed stage AI, ML, and robotics companies. This is unique, where most funds wait for fully raising before deploying. It’s a quirk of our LP history, and we’ll start raising Fund IV the moment we’re done with this marketing period for Fund III.
I started without keeping reserves in Fund I. That doesn’t work because it requires LPs to be brave when markets are afraid. Fund II kept reserves and also made initial fewer investments (24 vs over 50).
VC Math
The best way to understand VC is to understand powerlaws. If you look at a portfolio of investments, a minority will make up the vast majority of returns. One might be half of all returns, and the next two might be more than half of the remaining. An investment must be in this top set to make a meaningful return.
It’s worth breaking down the math to really understand the economics.
Market Comparison: S&P real returns at ~8%, over 10 years, is 1.08^10 = 2.2X.
Compare to median VC returns: 15%, 1.15^10 = 4X (Funds are illiquid so should return a premium over public markets)
Top return: 4X fund * 50% of returns = 2X from top single investment
Management Fees: 20%
Investable: 100% - fees = 80%
Initial checks : Reserves :: 70% : 30%
The remaining math could be just abstract and based on fund size and return ratios, but it’s easier to understand with typical numbers. I’ll use our model for Fund III.
Fund Size: $25M
First checks: $25M * 80% investable * 70% first checks = $14M. With 56 checks = $250K
Follow on: $25M * 80% investable * 30% follow checks = 6M. With 24 checks = $250K
Ownership: First check $250K / $20M post Seed = 1.25%, Follow on $250K / $35M post Series A = 0.7%
Total Exit Ownership after dilution: 0.6%
Target Exit Value: 2X * $25M / 0.6% ownership = $8.3B
Multiple from seed to exit: $8.3B / $20M = 416X.
100X return is good. 1000X return is great.
If you’re interested in more detailed fund modeling because you’re an LP, get in touch for the full model: ivan@tango.vc
If you’re a founder who has complained about the home run bias of VCs, consider what assumptions you have, what specific things you’d change about this model, to make the math work. VCs that don’t believe in the powerlaw end up with bad returns because their top investments aren’t as large.
Our $100K investment in Lovable’s preseed is now worth the Fund II fund size of $5.3M, highlighting the real powerlaw. This deserves its own post, so subscribe to get it.
What We Look For
In brief, the technology, the market, the go to market, and the team.
The best way I know how to assess these is to ask questions until I understand each. This questioning also measures intelligence, grit, and communication. My process is a lot more like an angel than a venture firm with an investment committee. I can’t imagine a worse structure to find outliers than a committee.
Technology. It’s a double edge sword, with harder tech serving a barrier to both you and competition. I try to understand this deeply, and my experience in AI, ML, and robotics means I’m often going deeper than less technical investors. I’ve worked to build many products, so how product strategy connects here matters. For example, you build an ML system, where in the product experience do you get feedback to refine and improve?
Market. Large markets are wonderful, but growing markets that will be large can be even better. It’s great to have a customer understanding to know how this might play out. Founders will find it very hard to mime the experience of having talked to many customers to know what they need. Specifically with robotics, I know and believe the default pitch, which helps to understand diffs.
GTM. The best seed stage founders have both modeled what it takes to grow while also having direct, measurable experience in execution. Traditional channels work all the time, but they require understanding of the execution required. For example, a sales driven company is almost certainly founder driven to start. There are some tells of inexperience, like modeling decreasing CAC with scale, which is almost never true.
Team. Founders building technology companies should be technical. Being technical doesn’t necessarily mean a degree from a university, but in the raw intelligence and grit required to ship something. Communication is an essential component here. You can actually mime this: write and edit down your messaging and then repeat the simplest version until you’re sick of it. Practice on customers, investors, press, your team, and potential hires. Confidence is often mistaken for competence, but clarity of communication from repetition and refinement is competence.
Supporting Startups
Founder asks are often interrupt driven, with investors called in to help. It’s useful to comment on routine investor updates too.
Technical. Founders are much deeper in the weeds of building their products than I can hope to be. But because I see a large portfolio, there are broad engineering and product lessons that could help. I’m also building a portfolio founder community where those that know best can help.
Hiring. This is usually process focused. I built a product in referral recruiting and worked to refine hiring wherever I worked. For example, I sometimes ask “did you tap your network on LinkedIn?” and founders will say “yes” and then I ask “did you export your whole team’s contacts and look at literally everyone for hiring and asking for referrals?” and the answer is never “yes”.
GTM. I’ve worked on multiple growth teams, so I know what excellent structure and performance looks like. The challenge early on is that you don’t have a team of dozens, so the founders need to trade action and attention to get the most impact. Modeling cross challenge cannibalization from a referral program using a multi touch attribution model is hard. Maybe just add a share link and a lifecycle email in an hour. Sometimes you just need to ask what is working and do that a lot more.
Fundraising. Introductions are common in seed rounds where people co-invest, vs later rounds where a lead will dominate the financing. I’m actually building a product to help founders raise called Attention AI. We automatically build your network, have a pre-built comprehensive investor list, and then help operationally get introductions. Communication around fundraising also matters, and I can help founders translate an often analytical and technical focused pitch into something less technical VCs can understand.
If you’re a founder building in AI, ML, or robotics – or if you are an LP who wants to back them, I’d love to hear from you: ivan@tango.vc