Year: 2024

2000+ angel investors and growing!

I’m super psyched! Our global angel club called Angel Squad, led by Brian Nichols, has reached 2000+ members this past summer across 40 countries! To me, this isn’t just a vanity metric. Angel investors play a crucial role in nurturing startup ecosystems — much more than VCs, so growing and nurturing angel investor communities worldwide is really important to me.

Silicon Valley’s Secret Sauce

Silicon Valley’s long-standing success as a startup hub is often attributed to its weather, schools, and legacy of tech companies. However, I disagree. There are plenty of places in the world with permutations similar to this that don’t have anywhere near the startup density that the San Francisco Bay Area has.

I think, a less obvious, yet critical factor for Silicon Valley’s success is its vibrant Angel investor community. Unlike the common perception that Silicon Valley’s Angels are wealthy individuals writing $25,000 checks at a time, many people invest much smaller amounts here.

For instance, early Uber investors included people who invested as little as $5,000, which became worth $25 million by the time of the IPO! What this illustrates is that angel investors don’t have to invest a lot of money in one go, and finding winners can be life-changing for small angel investors.

This culture of numerous small-scale investments enables a large pool of resources and support for many startups in the Bay Area. Early-stage companies benefit not only from financial backing but also from the introductions and advice that these investors also provide. Such a supportive environment allows startups to thrive and grow.

The Ripple Effect of Angel Investments

A robust Angel investor community can significantly impact a startup’s trajectory. With small checks, startups can secure essential early funding that institutional investors often hesitate to provide. This early support is crucial for the initial phases of a startup, where risk is high, and traditional funding is scarce. In addition, small check investors can often lead to larger checks later by opening doors. One of our portfolio founders at Hustle Fund named Steven Fitzsimmons (Fitz) broke down the anatomy of his seed round a few years ago. His smallest investor (who invested $5k) was the most helpful of all. Small checks lead to both introductions and more checks.

In contrast, many other cities outside of Silicon Valley, despite having either good tech ecosystems or wealthy individuals, lack such a vibrant Angel network. Wealthy individuals in these areas often do not reinvest their money and time back into their local startup ecosystems, which stunts the growth of potential startups in the area. Places like Boston, for example, despite its tech prowess, academic strength, and successful individuals, has lacked for decades a strong Angel community of hundreds of active individuals until more recently with the emergence of active angels from newer successful companies like HubSpot and more. (And I’m sure many of my Boston friends will disagree and say they’ve been actively investing for a long time now, but they are the exception not the rule to the geography :))

Growing Angel Communities Globally

The rise of Angel investor communities in cities like New York and Boulder also illustrate the transformative power of these networks. By fostering a culture where successful individuals reinvest in new startups, these cities have developed robust startup ecosystems. Neither of these cities were previously known for being tech hubs. This model shows that there is no special formula exclusive to Silicon Valley; any city can replicate this success by building a strong, active Angel community.

Angel Squad’s Vision

Hustle Fund’s Angel Squad aims to replicate and expand this model globally. With 2,000 members already on board, the goal is to grow to 10,000 and eventually 100,000 Angel investors. We want to empower entrepreneurs everywhere — not just in the US.

Let’s go!

The journey of Angel Squad is just beginning, but I’m so proud of Brian and team for the progress they’ve made. If we can continue to help great startups globally get access to more capital — to truly have free markets — that would be the dream. Let’s go!

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Data on how we invest at Hustle Fund

One thing no one talks about are the differences in how investors within the same VC firm make investment decisions. Firms are supposed to be united in their decisions. But, the untold secret is that often there’s a lot of disagreement within a partnership over what deals to do. Sometimes these conversations can become heated. But ultimately, if a firm decides to do a deal or pass, the entire firm is united on that front – even if individuals within the firm feel differently. I’ve talked about the differences between the consensus model and the champion model on this blog before.

This is no different at Hustle Fund. There are often deals that I do that the rest of the investment team thinks are awful. And vice versa. Part of why we have a champion model at Hustle Fund — where any investment professional can do any deal he/she likes — is that outliers tend to be the most contentious.

One of the reasons for differences in opinion about a company lies in what individuals think are the most important aspects of an early stage company. For example, my business partner Eric Bahn really values great products and fast shipping of product. Of course, I think this is important as well, but in my list of things that I care most about, product isn’t always top of the list. And of course, it also depends on what the specific industry is. If you’re selling Salesforce to salespeople, then product is less important than if you’re selling a Canva subscription to designers. Product matters more to certain customers than others. But there are many other nuances about how my decision making is different from my colleagues’ that I hadn’t been able to quite articulate before…until now.

First, some context about our decision-making framework at Hustle Fund. When we evaluate startups, we use a 4 point system. 4 is excellent. 1 is terrible. By having a range from 1-4, it forces the decision maker to pick a number that is either on the weaker or stronger side. No one can pick 2.5 — you have to take a stance on whether you believe a given company is strong or weak in a certain area. And then using this point system, we grade a company across a variety of axes. But ultimately, the scores are meant to help our investors guide thinking; there’s no minimum overall score that a company needs to achieve in order to receive an investment offer. Moreover, if a company scored all 4s, it’s also possible for that company to not receive investment. E.g. it might be a pre-IPO company that has clearly proven out an amazing team, an amazing product, amazing traction etc…but then it’s no longer a pre-seed company.

So with our scoring system, the vast majority of companies we meet do not score highly, including those we end up investing in. The companies are all early, and we do not have grade inflation. But the scoring does show patterns in what each of the investors on our team care about. And having amassed a large data set of how our investment team thinks, I’m excited to share with you our results on how each investor on our team differs in thought process.

Average Scores Across Our Criteria:

We used AI to help us analyze our investment patterns. For the companies who received funding from Hustle Fund (our portfolio companies), these were the average scores we gave our companies when we decided to invest.

  • Team: 2.98
  • Product: 2.32
  • Market: 2.79
  • Execution: 2.76
  • Fundraisability: 2.48

This is pretty interesting, because you can see that our investment team cares about “team” most importantly. As a whole, when we meet with a founding team, we are making our decisions to invest in large part because we are impressed with the team. In contrast, even if we don’t believe the current product is great or we don’t believe the team can fundraise, we’re often still willing to make the bet anyway. As a generalization, the categories of product and fundraisibility matters a lot less relative to other criteria.

Scores Per Investor (with commentary from AI):

  • Elizabeth Yin: tends to score lower on average, especially in Product (2.01 average) and Fundraisability (2.19 average)
  • Eric Bahn: gives higher scores across the board, particularly in Fundraisability (2.65 average)
  • Haley Bryant: has the highest scores in Execution (3.06) and relatively high in Team (3.5)
  • Shiyan Koh: has high scores in Market (3.05) and Fundraisability (2.80)

This is particularly interesting and can be interpreted in a few ways.

Since these are scores for companies that get investment, my scores could be interpreted in a couple of ways. You could say I see the weakest dealflow across my team (!) or you could also interpret this to say I’m the hardest grader of everyone, including the companies we invest in. There’s probably some truth to both in that I care less about how developed your product is and care less about a founder’s fundraisibility than my peers. In fact, across the industry, many VCs care a lot about whether a startup will get follow-on funding, but I very much prefer the founder who has less glitz and glamour and just gets to work. It also means that a startup who receives funding from me may end up being largely bootstrapped for longer and may have fewer downstream investors chasing them until they achieve some serious results. That’s a bet that I’m willing to make that few VCs will make.

You can see that Haley cares most about execution and team. Shiyan most about market. This isn’t to say they both don’t care about other criterion, but you can see what we all think a lot about. (Eric gets along with everyone and hands out A+ marks to everyone :).)

Variations in Scoring (with commentary from AI):

  • The variations in scoring are relatively moderate across all investment team members and criteria, with Fundraisability showing the highest variation for Eric Bahn (1.03) and the lowest for Haley Bryant (0.51). This means that Eric will back a bunch of teams that he thinks can’t raise more money as well as a bunch of teams he thinks will raise money very easily.
  • Elizabeth Yin and Eric Bahn show more significant variations in Product and Fundraisability scores. This means that Eric and I will back some teams that have great products and high fundraisibility as well as those that don’t. We do this because many ideas don’t require that much money or a rocket science product, but other businesses do. It’s case by case.
  • Haley Bryant and Shiyan Koh have lower variations in Market and Execution scores, indicating all of their teams need to have strong market arguments and strong executing teams.

Common Themes and Observations:

According to our AI that did this analysis, “Each investor has a distinct scoring pattern, reflecting their unique perspectives or priorities in evaluating startups. This diversity in viewpoints enriches the investment decision-making process but also highlights the importance of consensus-building or weighting different criteria according to strategic priorities.”

Basically, we look at companies in different ways but individually, have distinct things we look for. If I were pitching Hustle Fund with a new company, I would be looking to pitch the person on our team who was best suited for my company. E.g. I would pitch Shiyan if I had a huge fascinating market, but if I were not great at fundraising, I would pitch myself with a more bootstrapped approach.

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Do you set up your exit before you start your company?

This week, I had a really fascinating conversation with a portfolio founder named Joshua Lee, who’s the CEO and co-founder of a company called Ardius. Ardius helps startups claim R&D tax credits. So it’s a no-brainer why founders sign up for Ardius, because they can get free money, and Ardius takes a cut of what they are able to help founders get. If they can’t help you get any credits, they don’t get paid.

But what was fascinating was that when I was talking with Joshua, he said that one of his learnings in trying so many startups over the years, was that founders don’t think enough about their exit path before starting a company. Ardius, in fact, was not his first company, and so after many companies that didn’t work out, he decided to work backwards to figure out what to build.

I asked him, well, what do you mean?

He said that when he started Ardius, he not only talked with potential customers, but also talked with potential competitors – large companies who could potentially be building a competing product on their own. He was trying to see if he could manufacture his own exit before starting Ardius. He wanted to know what the M&A appetite would be if he were successful. He wanted to know how big of an opportunity large companies saw in what he was building.

And I asked “wasn’t that kind of dangerous to talk to companies that would potentially be building the same thing?” And he said, when he talked with a lot of the major players in the HR benefits space, in fact, many of them were building a competitor to Ardius. And some of them even told him they would squash his company.

While that was frightening to him, his thesis was that if you’re good, startups are actually way scrappier, faster, and more specialized and can run circles around most large companies. And as it would turn out, he was able to plant seeds in their heads that in case they were not happy with their own progress, they should stay in touch.

Ardius ended up doing quite well as an independent entity. And that caught the eye of all of these would-be competitors. Fast forward, Ardius was acquired by Gusto in 2021 after discussing with all the major players in the HR benefits space. These were relationships Joshua had already been building for years, which made the acquisition process quite smooth.

Now this whole story is a pretty controversial path. Many venture capitalists wouldn’t like this path, because VCs would prefer companies to keep raising money if it makes sense to continue to swing for a larger exit. After all, VCs need their winners to be large enough to above and beyond overcome the losses of portfolio companies who fail.

But, Joshua’s view is that VCs should think about the faster liquidity they could get with a manufactured exit. Instead of waiting 15 years to get to 100x (or more), would you rather wait 5 years and take a 10x? He thinks the idea that you have to wait a long time for liquidity in venture is outdated. From an IRR perspective, his model is also way better. In fact, in this particular example, the IRR of the longer time period is 36% vs 58% for the shorter time period.

Not to mention that we didn’t talk about how with his model, the team isn’t grinding too long to lead to burn out. Nor does the team become too big and chaotic, as you often see at large fast-growth late stage startups.

His argument certainly made sense to me. Certainly, if you were running your own angel investments, his proposed model is intriguing. You get your money back sooner, and you can redeploy sooner into other investments.

But, it made me think why this doesn’t work in the traditional VC model. VCs are judged on multiples returned as liquid cash over the term of the fund. Typically funds have a 10 year lifespan. So in this model, the winners aren’t around long enough to become huge winners. And yet, if you’re getting money back in year 5, you don’t have enough time either to deploy the returned capital back into new startups. So, the cash is just sorta stuck — either doing follow on checks into later stage companies, which tend to have lower multiples than early stage startups OR it just gets returned as cash and isn’t enough cash to make up for many of the portfolio losses. In other words, this model — assuming it works — works really well in an evergreen fund but not in a fund with a set term limit of 10 years.

All of that said, his model of building relationships with potential partners / acquirers / competitors from day 1 is smart — even if you aren’t looking to get acquired right away. One of my other portfolio founders did something similar, not for the purpose of M&A, but for the purpose of building relationships and ended up getting acquired by essentially a would-be competitor, because she had built those relationships early.

It may be scary talking with potential competitors – especially when you have nothing – but if you truly believe that your startup is great, you can outcompete your competition. As we’ve seen time and again, more funding does not equate to more success.

One thing that I am skeptical of in this model is the notion that you can actually predict what will be acquired. And therefore, your loss ratio is lower. Afterall, one of the hardest parts about building a startup is finding a repeatable sales process and enough customers who want to pay for your product. M&A demand is predicated on the assumption that your product will find very strong product-market fit. If you are able to find customers but only slowly, your would-be acquirers might decide that there isn’t enough demand for your idea. And, you might not be able to manufacture the M&A deal you thought existed when you set out to begin your company. In other words, I’m skeptical that you can come up with a higher batting average of ideas that are successful that companies will want to buy than the traditional VC model.

I always learn a lot from my portfolio founders, and this model of building-for-exits is certainly food for thought.

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Introducing Raise Millions

I’m biased here, but our team at Hustle Fund has published the most comprehensive guide on startup fundraising to help first-time software founders (IMO).

And now you can get it here for free.

Why we wrote the book

Fundraising used to be reserved for an “elite” group of people. If you weren’t born into a family of venture-backed entrepreneurs or grew up in Silicon Valley, you would have no idea how to raise money. 

This meant the “elite” group of people were the only ones with the opportunity to fundraise and build billion-dollar companies.

Over the years, I’ve been writing tactical tips and tricks for fundraising on this blog, on Twitter / X, and on our Hustle Fund blog. I’ve created small janky guides – like this one Questions that VCs may ask you. But there hasn’t been a comprehensive, one-stop place, that has all of this information.

Until now.

We believe great founders can come from anywhere and look like anyone. So Tam Pham, Kera DeMars, and I created Raise Millions to bring transparency to the world of fundraising. Whether you’re a solo founder from halfway around the world or a fresh college graduate in the US, you’ll learn how to raise for your tech startup.

What’s inside the book

  • Chapter 1: how the fundraising process actually works
  • Chapter 2: the essential ingredients of a killer pitch deck
  • Chapter 3: building relationships with investors
  • Chapter 4: how to pitch to investors
  • Chapter 5: steps to take once an investor verbally commits
  • Bonus resources: email scripts, templates, and a cheat sheet of our entire book

This book is educational and actionable. You’ll learn everything you need to know to fundraise from the pre-seed stage all the way to your Series A. 

Seriously, this guide will cover everything you need. 

And it’s free… 

Part of why I write is to hopefully help other founders avoid a lot of the mistakes that I made as a founder. (I made SO MANY MISTAKES.)

So, anyone can download Raise Millions: The ultimate guide to fundraising for first-time founders here for free. And let me know what you think.

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I’m becoming a cartoonist…with AI

Another year!

Last year was an incredible year for artificial intelligence (AI). If the rise of the internet was an inflection point, the rise of artificial intelligence (in my opinion) is an even more massive one.

I think what I’m most excited about is that sometime in the near future, everyone will have the ability and the access to build anything digital. It could be websites, movies/films, songs, mobile apps, comic books. You just need a device (phone / laptop / tablet), internet, and creativity.

In the 90s, with the rise of the internet, this started to become possible, but you still had to run to Barnes and Noble to buy books on how to code. The most motivated quick learners reaped the benefits of the internet in the first inning. You also often needed servers that filled closets and needed to handle the maintenance of all this yourself. It was not a super accessible period of time — not anyone could do this — but it laid the foundation for where we are today.

Fast forward to today, you don’t need to know how to code to build digital things. There are so many no code tools available to take the heavy lifting off of building, and you can learn about all of this with simple video tutorials on YouTube. There is so much that has already been made accessible to the billions of people on this planet. I learn a ton from watching YouTube videos — there is no better educator than YouTube (in my opinion).

Yet, despite these advances, there are some things that historically have still been hard in the last decade or so. For those who have no talent — like me — design, for example, is still hard. I can read and watch tutorials all day, and I still will not be able to draw well to save my life. If I practice for years, I could probably get better, but I won’t be able to come up with cool images or videos in the next couple of months.

Until last year.

Enter Midjourney and so many others. Last year, I thought I could finally fulfill my childhood dream of becoming a cartoonist to draw comics about my favorite stuffed animals I grew up with — all with zero talent.

But, is it hype or is it real?

Meet Warren Hippo that I created with Midjourney

My childhood stuffed animal Warren Hippo should be at least 30+ years old by now, but he is somehow forever 5 years old and will always be 5 years old.

Midjourney was able to make him look pretty real. And more importantly, pretty close to the original Warren Hippo too:

Meet the original Warren Hippo

Pretty close-ish right? I thought I was on my way to becoming a professional artist.

Unfortunately, image consistency quickly became a huge problem, and it’s one of the top issues that Midjourney users want solved (according to a poll in their Discord community).

Midjourney renderings of Warren in various poses. He seems to have…evolved into a different hippo.

Modifying Warren into different poses was an entirely different exercise. Pretty challenging, and he turned into a different hippo. While it’s still really impressive that you can generate these new images of different poses in about 30 seconds, the character consistency will have to get better in order for the technology to truly enable the next generation of comic book artists, brand marketers, and designers, etc.

And this issue isn’t just limited to Midjourney. The generation AI tools I’ve played with are all really good at generating single-use images, but it’s much much harder to create themes, characters, and brands that you can use over and over again. This is getting better, as you can now seed designs with other images and you can preserve past images as well. But, as of writing this blog post, this is still a major blocking point.

In addition, these tools are all great with common images — like dogs. If you want to create a cartoon dog, it’s easy.

But what about something that doesn’t really exist? Something that doesn’t really exist even in people’s imaginations? Like a hippocorn?

I wanted to create a cartoon of our stuffed hippocorn at Hustle Fund. The original stuffed animal looks like this:

Dunky has wings and a rainbow horn and two large teeth

Unfortunately, because the internet doesn’t really know what a hippocorn is supposed to look like, it’s hard to describe it using generation AI tools. You get weird stuff like this:

Early version of a generated hippocorn image

In fact, we did many iterations on our prompts to try to create a good hippocorn design for our announcement of our plushie. Alas, we were unsuccessful.

Generative AI tools are, by definition, horrible at creating images that don’t really exist, because it has no data to work with.

Over the holidays, I was determined to create a great hippocorn cartoon using Midjourney. So I spent a couple of days working on this. (I know, this is a ridiculous hobby.) I realized that if I were going to be successful, I needed to feed it the data to train on. I ended up seeding Midjourney with a ton of photos of our stuffed hippocorn, and the results turned out a lot better.

After a few days of work, this looks a lot closer to our plushie hippocorn Dunky

But even in seeding it, Midjourney struggled to identify its wings (this is why you see a white shawl around the hippocorn). It took a long time to interpret the two white squares as teeth as well.

In addition, it struggled to render different poses of Dunky as well. You can see the teeth were lost.

Renderings of Dunky the hippocorn after having a tooth extraction at the dentist

But, maybe it’s good enough for some use cases.

Ultimately, we were only able to create hippocorn cartoons because we already had designed a hippocorn. In other words, we needed a designer to create our stuffed hippocorn in order to feed the AI models with our design. It would have been impossible to create a hippocorn from scratch with no designer.

This is where AI still falls short. If you are creating something completely new, you will still need a designer to design what you are developing. That being said, AI can probably speed up some bits that are too tedious to do manually, saving your designer some time.

Why bother with your stupid hippocorns?

As we assess products in AI, which is an incredibly competitive space, these nuances matter…A LOT! I think it’s easy as a VC to watch a demo of a product and say “Wow, that image generator can do ABC things.” But really testing the limits and edge cases is important to understand the state of AI and stay on top of who has the lead.

It’s also important not to write off any of these companies because of these limits. I’ve seen so much change in all of these generative AI design products in just the past few months. They are getting better so quickly, and I suspect in a year from now, some of these problems that I’m writing about here will be solved.

What the future looks like?

Eventually, we’ll look back and say, “Wow, the 2020s was an amazing era for technology.” You’ll be able to build movie studios from your computer and distribute your films on the internet, disrupting the traditional movie industry. You’ll be able to write or draw books, including graphic novels, on the internet and distributed on the internet, disrupting the publishing industry. You’ll be able to create websites and mobile apps more easily — as just one person.

So, if I were going to start a new company today, I would probably not build an AI company — there are so many of them. I would probably build a company that thrives with the assumption there will be a lot of AI companies to help us design and build faster and so much more. For example, at Hustle Fund, we hire no-code developers, because you no longer need to code everything from scratch to get things built. That’s an occupation that didn’t exist even five years ago. So, what are the new roles that will emerge in the next five years? I’m sure there will be prompt engineers in the coming few years. Or maybe QA testers for AI. Maybe you’ll need tooling or legal for the creations you develop through all of these AI tools. Think about what that world looks like — I would build a company for that world, because it is arriving so quickly.

Momentum is moving creativity forward quickly. We’ll see new brands arising from individuals and influencers. The next few years will be remarkable.

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