Coconut Headsets

Today I’m excited to announce that I’ve officially joined PJC as a General Partner. I’ve thought about becoming a VC for a long time. In fact, back in 2011 I was talking to my head of Sales at Backupify and we were discussing “doing what you love.” I talked about how what I really loved was business models. I loved the complex systems aspect of business – how go-to-market and product and customer segment and team all have to line up in the right way, and all the different levers you can pull to influence the business and the market. “That sounds like you want to be a VC” he said. I didn’t realize it at the time, but he was right.

When Backupify got acquired I was 38, and so was a good profile to become a VC. I like to write and network. I had founder/CEO experience, and had a good exit. And I was young enough to do it for a long time. Several firms approached me but it didn’t make sense at the time. So I decided to start Talla and do some angel investing with my friend Todd Earwood, through an entity we formed called Half Court Ventures.

Talla took much longer to get to product/market fit than Backupify, and in the process, I learned that I don’t really like the CEO job. At Backupify, I liked learning the CEO job, I didn’t actually like the things you do in that role. It’s a lot of culture building, a lot of people-stuff, and I’m a mediocre people manager.

On top of that, to be a good CEO, you have to have to like that feeling of walking into the office and being THE guy/gal that everyone wants to talk to. You go to an event and everyone wants your attention. You have a press release and everyone wants to talk to you. While I am not shy, I am a heavy introvert, and I get energized from reading, writing, thinking, etc, so, the social/political burden of being CEO, the people stuff, it weighed on me. I’m happy to be moving to a role where I can play supporting cast instead.

But what I learned making 74 angel investments with Todd over the past 5 years is that I really really loved the investing side. I often have unique insights about markets and as an entrepreneur I could act on one of those insights. As a VC I can act on many of them. And being a natural contrarian, very comfortable going against the crowd, VC is a better fit. As a CEO, trying contrarian tactics is a difficult thing to get a team to swallow. As a VC, betting on a company you believe in that everyone else doesn’t can get you a big win.

This summer, I decided it was the right time to hire a CEO for Talla, and was able to find an excellent candidate (stay tuned for the announcement). I talked to a few firms but, PJC was the right fit for a few reasons. First of all, PJC was a small firm that wants to build. It piqued my entrepreneurial nature to grow something, and meant I could go in as a legitimate partner and do deals, not be a junior partner who had to spend years proving myself.

Secondly, PJC is one of those Boston based firms that does deals everywhere. A lot of firms are focused just on Boston, or just the East Coast. I like the flexibility of finding the best deals anywhere. About half of my angel deals are west coast, so I am excited to maintain an investing presence there.

Third, PJC is a very flexible firm that will do many types of deals. Some VC firms have very narrow ownership targets and deal criteria. While we do have those at PJC, the range is a lot broader than most funds.

VCs get a bad rap, for a lot of reasons. And while I too have had some bad experiences with VCs, I’ve also had some fantastic ones, and I hope I can be one of those helpful VCs, rather than one that is draining to work with.

I’m excited to start this new chapter, and so if you are building early stage AI or B2B companies, please reach out.

I also want to thank all the people that helped me get here. There are way too many to mention but a few who stand out are my partner at Half Court, Todd Earwood, who was always free to discuss and debate business ideas (he is joining PJC too as a operating partner). Jamie Goldstein from Pillar taught me a lot about investing by letting me be a venture partner there for 2 years. David Orfao, Rich Levandov, Brady Bohrmann, and Rudina Seseri were all mentors of sorts who taught me pieces about investing. Dharmesh Shah and Jason Calacanis were instrumental and supportive of my early angel investing and gave me great advice about how to go about it. Jeff Bussgang was one of my biggest advocates years ago when I moved here from KY in 2010, and helped me get plugged in to the Boston ecosystem. Kent Bennett and Eric Paley were helpful as I thought through the transition from Operator to Investor. Phil Beauregard always challenged me, pushed me, debated me, and showed me some good deals along the way as I was angel investing. Vivjan Myrto started investing in AI even earlier than I did so meeting him convinced me I wasn’t crazy to focus on AI back in 2015. And of course I want to thank David Martirano and Matt Hayes for having me join them in their partnership.

I think I’ve also knocked heads with everyone on that list above at some point, and so, have been very lucky to have the kinds of supportive people who tolerate strongly opinionated people around them. So thank you all for your help getting me to this point, despite that fact that I have aggravated the hell out of all of you at times.

I’ll be writing more about the specific theses I have and where I am investing at PJC, in addition to a final advice post for entrepreneurs that I want to write before I am gone too long from that side of the table. Stay tuned.

VCs get a bad rap, but I’ve always enjoyed talking with them. They see a lot of stuff, so, they are great sources of market information. It’s true that they sometimes screw up companies, but I don’t think it is in the way most people assume. It’s rarely that they are greedy, forcing you to take more money than you need or selling a company that shouldn’t be sold. I think the mistakes are of a different category – advice that lacks business context. Founders often take this advice too seriously, and often have their own biases that are stupid, just like the VCs, but let’s look at some examples.

Channel Partnerships

Founders, particularly first time founders, often have this same idea that one massive channel partnership will make their business suddenly jump to hypergrowth. I’ve seen it across my investments too. “Company X wants to sell our product, and even though we are a 5 person company, they think it could $20M in revenue next year for them.” I’m sure somewhere, at some point, this has worked, but usually it’s death for the startup. Why? Because the startup runs out of money before Company X ever makes a sale. A deal like this might take 6-9 months to finalize even if Company X really is into it. Investors see this over and over, and every founder thinks their situation is unique and so, as an investor it gets old explaining why this is a bad idea.

That said, there is a flip side to this. Investors often overly favor the last go to market model of their best portfolio exit, which is stupid because GTM really needs to match your market and buyer and product. But I believe investors overindex on direct inside sales. They say things like “own your own destiny” which is entirely true unless your buyers like to buy through an existing channel. I could write an entire post on what to look for to figure this out but suffice it to say VCs often recommend you sell direct and build a brand even when it’s a bad idea.

Luckily, back in my Backupify days, we had a great Board member who gave us good advice on channel sales. In our first year of rolling out a channel program, we signed up 65 channel partners and they were 3% of our revenue. I told the Board at the end of the year we were shutting down the program. He said “wait, has any partner sold anything?” Three partners had. He said “fire the other 62 and focus on those 3 for a year.”

It seemed stupid to me. If 65 partners can only sell 3% of our deals, how will 3 sell anything? But those 3 sold 9% of our deals the next year. We were too distracted with too many partners, and so we learned, with these 3, how to really do a channel deal. Then we started to expand slowly and when we sold the company, the partner channel was 30% of our revenue.

Buyer Personas

Warren Buffett always says “never mistake precision for accuracy.” I think this happens with personas. Investors always want to hear that “we target B2B companies 100 – 500 employees that use MySQL and sell to the head of I.T.” It’s so crisp and clear. And sometimes, you can do that. But often in new markets, you don’t know where the adoption is going to come from, or sometimes the market will support a very horizontal product, or the buyer profile is psychographic instead of a demographic, but investors like to push you to a clear demographic profile.

At Backupify, we had 9000 customers when we sold, and our largest customer paid us $360K per year, and we had hundreds that bought one seat and paid us $30/year. What mattered wasn’t size or industry, it was whether or not you thought backup was important. We sold to “the broccoli eaters” as we called them. There are people who always wear their seatbelts, floss their teeth, file their taxes in March, and eat their broccoli. Those people also love to backup their data. That was our market. Investors often push entrepreneurs to “focus” when it’s too early to actually do so.

Hire The Stud

This is probably the worst mistake investors make – pushing entrepreneurs to hire some marketing or sales or engineering stud who led XYZ product at ABC big company. It’s almost always a bad idea. That person is almost never the right fit, but every first time entrepreneur feels pressure to do it. It’s compounded by the fact that as a first time CEO, you feel constantly incompetent because of everything you don’t know, and these people seem to have a lot of answers, but, they almost never work out. They can’t deal with your stage and size. Investors though, feel a lot better if they are around.

My point in writing this is to give some examples of specific areas VCs influence founders in bad ways. I also want to show that it isn’t that VCs are morons (although some are), but that they are giving reasonable advice that is just often out of context. It might be great advice at a different stage or size.

One of the favorite parts of modern video games, for many people, is designing their avatars.  I’ve been thinking about this because I’m looking for AI to create an area I call “synthetic social media.”  But so far I haven’t seen any companies in this space.  Since it doesn’t exist, I don’t know exactly what shape it will take but let me try to explain what I mean.

In Japan, there is a synthetic pop star.  In many immersive video games, like Second Life, people use them as a way to escape and be someone else in a virtual world.  I think AI will allow us to combine these two ideas and create “friends” who act the way we want and do the things we wish they would do.  

Imagine a platform that allows anyone to create a virtual pop star.  You design your avatar, give it a name, and then AI can write the music.  You could say you want it to be 40% Rolling Stones, 10% Taylor Swift, 20% Beastie Boys and 30% random other mix, and the AI can write a song that sounds like that blend.  Now the avatar can perform concerts or shows for your friends, singing the songs it wrote.  The most popular songs and concerts (and thus avatars) develop followings.  And it isn’t just in music.  This could work for art or video content – anything digitally created by humans that AI could step in and perform reasonably well at.

Now it gives you the chance to be friends with, or be the manager for, this synthetic AI start on a social media platform that will be mostly synthetic identities.  If you aren’t talented enough to sign and play music, your avatar can.  You can live vicariously through it, and many people will.  Longer term, you could see the idea expanding into sports, and even into the workplace to some extent through synthetic ai workers.  It’s out there but, it feels like a logical extension of where AI is going, and the time is right to set the seeds in motion for phase 1 of such an industry.  

If you are building something along these lines, please reach out as I’d love to invest. 

I was chatting with someone the other day about customer development and I asked a simple question. Now that customer development has become so common among venture backed tech startups, has it moved the needle at all on outcomes? I don’t have any hard data but, it seems to me that just as many startups fail now as did before. And, anecdotally, in my own angel investing (74 companies) it seems that the more obsessed a founder is with customer development, the less likely they are to build a big successful company.

I want to explore a hypothesis today, one that some of you will hate, but will make others of you nod your head and agree. I hypothesize that customer development hasn’t really worked out all that well, and has led to many small outcomes and companies that won’t die, and thus doesn’t fit the venture model. This, in turn, has led to the worship of the visionary entrepreneur and is why we sometimes get bad situations like WeWork was a few weeks ago.

When people say “customer development” they mean a few different things. I mean the more formal version of it as laid out in Steve Blank’s book Four Steps To Epiphany. I think Steve was actually on to something, but like most ideas, it was summarized and bastardized until it became generic and mostly wrong.

The problem is, talking to customers has some value, but it’s not a panacea. Customers often don’t know what they want, or they want a problem solved in a way that is stupid, wrong, not scalable, or not repeatable. Listening too much to customers often gets you stuck in a rut where you do a bunch of services work. Or more often, it gets you stuck in a local minimum with a handful of customers that have your specific problem and desire your specific solution.

Blank’s book actually gets you out of this – he has 4 major steps, and one of them is “customer creation” but, it seems like 95% of what is written about customer development is just about making sure you talk to customers before you build anything.

Here is my hypothesis.

1. Customer development was bastardized from a really nice smart process into “interview customers before you build anything.”
2. As entrepreneurship became “cool” circa 2011 or so, and everyone wanted to start a company, there were too many interviews with people who wanted niche solutions, but these interviews (if a few people agreed) were proof that it should be built.
3. People without vision started using metrics and “data” to iterate their way to success via customer feedback.
4. But it never worked and didn’t move the needle on outcomes overall. All we got was a bunch of walking dead startups that were really lifestyle businesses.
5. Smart investors realized this tired of it and thus and started being drawn to big visions and the entrepreneurs who swung for the fences. (For the record, I personally think it’s better to swing big and miss than to swing small.)

What should have happened, and what I think Blank intended, is that companies that found product-market fit but then realized they:

a) couldn’t reach customers cost effectively

b) were in market with a small TAM

c) couldn’t find a scalable repeatable business model

These companies would re-trench and start with a new product for their market. But that didn’t happen. They adopted the YC “don’t die” philosophy and, well, now you have a bazillion 3 person companies clinging on for life support. And you have a bunch of VCs who grew up in the customer development age and think that a lean company clinging on for life support is what they should be looking for.

All I know is that, in my own experience running companies and investing in them, sometimes they just pop. It’s often hard to explain why. The timing matters, and the it’s a function of all the pieces lining up at the right time. But when they do, you know it. Until they do, you just keep trying stuff. I don’t think it’s a good idea to just stay small because it doesn’t fit the venture model, and if you want to raise venture, you have an obligation to try to go big. (Note, there are many paths other than venture, and those are all good too, but, if you take VC, it’s about going big).

I’d love to see some numbers if they exist, but, my guess is the customer development wave hasn’t improved returns for VCs or success rates of startups. It’s hard, and the factors for success are complex. But feel free to tell me in the comments why I’m wrong.

More and more often, I am hearing startups talk about “synthetic data.”  I’ve seen my existing startup investments start to use it, and I have seen entire companies formed around it.  So, what is it?

Put simply, it is data created by a machine.  Now, why would we do that?  Imagine that you want to train a machine vision model to identify a Tesla.  Now imagine you only have 10 pictures of Tesla’s to train on, so you need a bigger data set to train a better model.  One way to get a bigger data set is to go get thousands of more Tesla pictures.  Or, you could consider doing some simple manipulation to the pictures you have to create new pictures instead.  

For example, maybe you don’t have a picture of a red Tesla.  You could photoshop one of your other pictures to make the Telsa red, and add that red Tesla to your data set so you model performs better at classifying Teslas.  What most people use synthetic data for is to test under different conditions.  They take an image and change the lighting, shadows, etc to simulate different conditions so the machine learning model learns what an object looks like from different angles.

A common use of synthetic data now is to build data sets for autonomous vehicles.  You could create an entire machine generated city, drive around that city obeying traffic laws, and feed that data into the autonmous vehicle model.  This allows you to simulate things that may be harder to capture in real life (e.g. a car running a stop sign).

Now, synthetic data isn’t always good for a model.  In NLP applications, one of the criticisms is that synthetic data sets generated for training are often very simple (because our language generation techniques are still weak compared to other types of AI).  So training a model on all of this language data fails to capture the nuance and vagaries of messy real human language.  But in other situations, like machine vision, synthetic data tends to work really well.

From a business perspective, there are a few use ways to think about synthetic data.  First of all, can you use it to generate new variations of things in ways that are valid for training.  Secondly, can you use it to label data about things that humans no longer need to label?  And finally, should you create the synthetic data yourself, or not?  

My current hypothesis is that synthetic data will mostly be done by a few third party platforms in a market that develops into an oligopoly.  I think the way that software debugging works today:  report a bug -> code a fix -> test on a staging environment -> deploy to production and verify, will be the way a synthetic data workflow evolves.  It would look like this:  report a model failure (e.g., model doesn’t detect things well at night) -> use a synthetic data platform to generate new items for a data set that increase the data for that problem (what things look like at night) -> rebuild model -> test model -> deploy new model to production.  Someday it will be push button easy.

This means if I am right, synthetic data business opportunities will come in 2 flavors.  The first is synthetic data for common objects, where there is lots of data.  These platforms will win by being the easiest to use, connecting to the most workflows, and having the most common options for data generation.  The second is cases where generating the synthetic data is hard because of the nature of the problem space and the lack of existing data sets to start with.  This will lead to specialized providers who can master specific domains.

If you are working on AI, soon you will need a synthetic data strategy.  And if you are company in the space, please reach out if you are looking for investment.

A few months ago I spoke on a panel, and I was asked a question that I am always asked about AI and automation – will it kill jobs? And if so, what do we do? The answer I gave surprised many people who said they had never heard an answer like this before so I thought I would write about it here.

Here’s a heuristic that will guide the way – things we no longer need don’t always go away. Sometimes they die briefly, but then are resurrected as status symbols.

Bookstores

When I was working on my MBA in 1998, my business school professors were freaking out because they had no idea what business was going to look like when the Internet really took hold. I had a professor tell us that in 5 years all brick and mortar retail would be dead, and that we would only shop online. Obviously they were very wrong. If you take bookstores as an example, you might be surprised that while Amazon initially put a big dent in the bookstore industry, there are now more bookstores in the U.S. than there were before Amazon. The algorithms of Amazon are super efficient but, they don’t provide the serendipity, the community, or the curation we sometimes desire.

Watches

I remember playing golf with a friend in 2007 who said “wow, you still wear a watch?” He told me he had no need for one because he had a cellphone and it always had the time. The cellphone was, for a while, definitely killing the wristwatch, but much like independent bookstores, it appears that watches are starting to come back as more of a status symbol than ever before.

Horses

Two hundred fifty years ago, everyone owned a horse. That’s how you got somewhere. When cars came around, it didn’t kill off the horse industry altogether. Instead, horses became expensive status symbols. To own a racehorse, or to participate in equestrian events, it it’s own culture and community filled with mostly wealthy people.

So what does all of this tell us about AI, automation, and jobs?

I believe that as jobs get automated away, employing people to do a thing, instead of robots, will become a status symbol. Humans are always competing with each other for status, and that won’t stop. Bookstores, watches, and horses didn’t go away, although they did change in different ways.

But, some things do go away forever. For example, no one washes clothes by hand now that washing machines were invented. How do you explain that?

My theory is that there are two kinds of jobs. There are jobs machines are better at, and will always be better at. And then there are jobs that really can’t be done much better, but could be done faster or more efficiently. by a machine. The latter category jobs will emerge as a status symbol if you have a human still do it. Those jobs will pay well because they now become “luxury services.”

If I think forward and see some new jobs emerging as a result of AI automation (data curator, data annotator, model trainer, AI designer, etc), and see some jobs becoming status symbols as mentioned above, and also factor in that hybrid human-AI partnerships will upskill some low skilled workers and enable them to keep working, I’m actually pretty bullish that at least for the next few decades, we don’t have a problem with automating jobs away. There will be economic upheaval, and some industries will be like the clothes washing industry and suffer pretty greatly, but, at the macro level I believe the story will actually be all right.