Y Combinator and Equity Theft

This month, I interviewed Fred, who was the third person at a Y Combinator startup and had a very negative experience, including basically having his equity stolen out from under him. I don’t think this is universal – I’ve met others with much more positive stories – but it still seems important to share, with lots of fascinating details. “Fred Gardner” is a pseudonym to protect his identity, but I’ve met him in person and his claims seem credible.

Alyssa Vance:
So, you’re a startup founder whose background is in computer science, right?

Fred Gardner:
Yes, that’s correct. At this point, I’ve founded one startup, been on the initial founding team… one of the first couple of founding engineers of two more. My background is in CS, with a focus on machine learning and cryptography.

Alyssa Vance:
What appeals to you about being a founder, versus getting a job at someplace like Google or Microsoft, where you’re just working 40 hours a week and being comfortable?

Fred Gardner:
It’s two things. One is the real sense of autonomy that you get. As a founder, there’s a problem that you need to solve, and you have the whole wide space of possible tools in front of you, and it’s up to you to figure out the things that are best suited to solve that problem. That’s the type of problem I most like solving. And the second thing is having a really, really tight connection with the people who will actually be using your solution.

Before going into entrepreneurship, I’ve done a lot of work at R&D and defense companies. And the biggest complaint that I had about that type of work is I never knew whether a) my solution was useful, and b) who was going to be using it. This is a concern that I’ve had a lot about larger tech companies as well – that you’re so disconnected from the people who are actually using the solution; you’re building a product for a team that’s connected to a team that’s connected to a team, that’s connected to someone who’s attached to the product. At a smaller company, you’re really directly interfacing with people who are using it.

Alyssa Vance:
Cool. What got you interested in both computer science and entrepreneurship?

Fred Gardner:
The honest answer is going to be somewhere between:

– I was on the internet long enough that the descriptions of the work people did in CS looked really interesting to me, and I started doing it because of that, or;

– Seeing my brother start getting into it in late middle, early high school. I’m two years younger than him, so that would have been early middle school, I started getting into it as well, because it looked interesting.

In terms of entrepreneurship, that’s a much cleaner story. I spent the year between high school and college on a mountain in the middle of nowhere in Israel, and a friend of mine came to me with an idea that they wanted me to evaluate tactically. The details involved a novel way to do key sharing, and to enable some unique business use cases for data privacy. I looked at it and said, “Hey, this is a super interesting idea.” The company itself no longer exists, but I got into entrepreneurship when we were talking about this idea, said, “hey, wait, there’s actually something here”, and we decided to make a company out of it and see where it went.

Alyssa Vance:
So this company you founded, that eventually got into Y Combinator – what were the origins of that? How did you meet the other people, and what made you decide to get involved?

Fred Gardner:
I was part of a club at college, and one of the other founders of that club approached me with an idea, and said “hey, I want your technical evaluation of, if what we’re describing is at all feasible.” I met with them for a few hours, and it was like, “hey, I don’t think idea A is at all feasible, but here’s how you could sort of serve the same vertical, and change it into idea B.” And they were like, “hey, that’s actually really interesting. Would you like to come join us and help us build this?” So I was like, “Okay, who else is with you?” “Just me, the co-founder, and you would be the third person joining onto this thing.” This seemed interesting – it was a vertical that I thought was, if not the most impactful, at least somewhat of a niche vertical, I thought I could be of use – so I started working with them.

Alyssa Vance:
Okay. So you started this company and it didn’t go so well, is that right?

Fred Gardner:
Let me put it this way. We entered the summer with six people, four of whom were technical. We left that summer with three people, one of whom was technical; I was no longer with the company by the end of that summer. And that last technical person left within two weeks after that. So, “it didn’t go so well” is certainly one way to put it.

Alyssa Vance:
What were the different roles of people within the company?

Fred Gardner:
Forgive me if I don’t recall formal titles, but I’ll do my best. The two co-founders were the CEO and COO respectively, and they were mostly product design, a bit of product strategy, not really anything technical, talking to potential investors and customers. There was the CTO – who we later found out was lying about his technical work, but that’s a whole other story. There was me heading up backend development, and there was our principal front end engineer, who was heading up content development.

I was the first person that they brought in to do technical work. In particular, that meant that I spent a lot of time speccing out the system. I pretty much wrote the initial systems spec, built probably 30% of the first system with another couple of technical people.

The initial signed cap table had, of the six people, a pretty even split of 20% for each of the co-founders, and 15% for each of the other people, with dilution coming out of all shares equally. Part of the reason that I was initially comfortable joining was because we had signed this initial cap table. I was like, okay, the two co-founders aren’t technical, but they acknowledge this flaw and are giving equity to people commensurate with this. This, I assume, was the cap table that YC was initially shown, because this was the cap table that existed at the time that I interviewed.

Alyssa Vance:
You say it’s a whole other story, but how did you meet this person? Like, how did you hire this person?

Fred Gardner:
This person came pretty well recommended by the people at college. We picked him to be the CTO rather than me, because I made the choice that I wanted the CTO to be someone who had familiarity with the whole tech stack; he had significantly more frontend experience than I did. It turned out later that he had been lying in progress reports to the team about what stuff he had done vs. what stuff other people had done, and had been lying to each person on the team, claiming the responsibilities and Git commits of a different person.

Alyssa Vance:
Well.. okay. So the two people who initially started the company, or had the idea – it was a software company, and…

Fred Gardner:
Neither of the founders was technical, that’s correct.

Alyssa Vance:
Yes. What was their plan, exactly?

Fred Gardner:
In so far as I can understand it, it’s somewhere between… Dunning-Kruger where they’d done a bit of comp sci stuff, and they thought that they knew enough to do it, they’d taken intro CS classes and intro data visualization graphics classes and they thought that would see them through. And I think their other plan was to pull in a technical person and have the technical person do that side of things.

Alyssa Vance:
Then what happened next? Where did you go from there?

Fred Gardner:
We spent the next three or four months building. The first hint of unusualness that went on, was in the winter of that year. We applied to Y Combinator, we got the interview, and we had another potential investor/customer meeting that was the same day that we couldn’t move. I suggested that I and a couple intern style people go and handle this customer meeting, and that the other founders go to the YC interview, and that we tell YC “oh, by the way, we have another person, but he’s meeting with a potential customer/investor”.

The YC interview itself went well enough that we were accepted. But apparently, YC was very confused about the possibility that someone would have a customer meeting at the same time as a YC interview, and not move it. This was the first hint of strangeness. But after that, I forgot about that weirdness and things proceeded apace. We were building out the product, meeting with investors, meeting with customers, et cetera, until we got to that summer and went out to California to do YC.

Alyssa Vance:
So you were accepted into Y Combinator.

Fred Gardner:
Yep. We were accepted into Y Combinator; we went, and then we were informed that only the CEO, COO and CTO were going to be considered founders by YC, and that therefore only they’d be able to attend the majority of the programming.

Alyssa Vance:
But there were two people who came up with the initial idea, if I’m understanding correctly. And there were more than three people who had relatively equal-ish shares of equity. So how did YC pick those three?

Fred Gardner:
They were the three who had gone to the YC interview.

Alyssa Vance:
That seems kind of arbitrary?

Fred Gardner:
My assumption is that they were the three who had gone to the YC interview, and therefore, when YC had written down the three people that they were accepting, presumably, they’d only taken notes on those three. I’m not going to pretend to know what YC’s precise process was there. But my assumption was that somewhere along the way, something had gotten lost in the shuffle or not checked carefully, and it was just like, “Oh yeah, these two people are the ones who showed up for the interview. They’re the founders.”

Alyssa Vance:
Okay. Did you ask them about this?

Fred Gardner:
Oh I did. I asked them about this, a couple of weeks later, and they were like, “Sorry, this is not a thing we can fix. These are the people we accepted into the program, we didn’t accept you as a company.” I don’t know whether this is a truthful thing, or whether this is like… we want to save face because we don’t want to make it look like we didn’t do enough research into the company before doing the acceptance.

Alyssa Vance:
Y Combinator is obviously a very competitive investment program. I personally applied to Y Combinator, I got an interview, but then got rejected afterwards. They have an acceptance rate somewhere in the low single digits. Do you have a sense of why this company was accepted, whereas the vast majority of other companies were not?

Fred Gardner:
I think it’s a mix of two things. The thing that I was told by the people who were at the interview, was the interview was not going well, and then Y Combinator was very impressed by the demo of our tech, which I and a couple other people had built out over the past couple of weeks. So it’s either they were impressed by our tech, or… it’s probably some mix of that and somewhat credentialism, like, “oh, we have a bunch of people from Harvard and MIT on the team.”

Alyssa Vance:
At this time, were you getting investment or money from other sources?

Fred Gardner:
Yes. YC was not our first investor. We had a few angel investments from people that the CEO and COO knew. We finished our seed raise within the first couple of weeks of YC, and we started our Series A or secondary seed raise – it shifted over the course of our post Demo Day fundraise – maybe two weeks before Demo Day.

Alyssa Vance:
The main YC program lasts about two to three months, and the YC partners say that during this time, people are supposed to be super productive, and essentially do nothing but develop their product, get feedback from customers and sell it to customers. Is that your experience, your sense of how that went?

Fred Gardner:
The main difference in productivity, I think, doesn’t come from the fact that it was YC, but comes from the fact that we had gone from working on it part-time to full-time. I didn’t notice an extreme difference in productivity that can’t just be explained from going from working on something 40 minutes to an hour a day, to working on something seven hours a day. We were probably 7 to 10 times as productive as we had been previously, and this is explained by the 7-10X work time.

In terms of selling to customers and meeting with customers, we met with a few customers early and then a few more customers later. I talked a couple of times to YC partners when they let us in for full team meetings, and heard and read the notes that people took on YC meetings, and the advice we got was very wishy-washy, and was much less “here’s how we can help you talk to specific people”, and was much more just like, “Oh, go out and talk to people, We can’t help you until you’ve independently done that.”

Even after we’d gone out and independently spoke to people, they kept giving us the same advice, except for one particular case where a YC person tried to be helpful and inadvertently made a mistake that did not speak well for their competence.

Alyssa Vance:
Can you tell us roughly what size of the customer were you targeting?

Fred Gardner:
SMBs. The smallest customers we targeted were people who were running tens of thousands of dollars a year. The largest vertical of customer we targeted were large-ish retail providers who were doing hundreds of millions or billions of dollars of business. We targeted every type of customer within the range.

Alyssa Vance:
So you said that you weren’t superhumanly productive, but would you say things were basically going as planned?

Fred Gardner:
In terms of product productivity? Yeah. We were pretty good at consistently meeting roadmaps.

Alyssa Vance:
Then at the end of a YC, you did raise money successfully?

Fred Gardner:
At the end of YC, we raised money successfully, but from contacts that we’d had before YC, for the most part.

Alyssa Vance:
So you participated in the famous YC Demo Day?

Fred Gardner:
We participated in Demo Day, got two or three inbound pieces of interest. And I think none of those turned into actual investment.

Alyssa Vance:
Demo Day involves… I don’t know, but I was guessing around 500 investors, is that right?

Fred Gardner:
Yes. It involves like 500 investors. I don’t know how many of those were initial bites. All I know is when things moved far enough along that the CEO and COO shared their progress with the rest of the team… and that was like maybe two or three contacts that were made on Demo Day proper turned into people who were potentially interested in participating in a round.

Alyssa Vance:
It sounds like – at least from your experience, maybe others are different – that the hit rate from this was lower than the hit rate from just cold emailing people.

Fred Gardner:
I wouldn’t go that far, because the two or three people that I knew about were actually in talks for, at what valuation they’d be interested in participating in a round. So it’s probably about 10X the rate from cold emailing, but it’s significantly lower than the rate from already knowing some people. And the rate for Demo Day was worse than the rate which we built up by just being in the Bay Area and meeting with customers, and having those customers point us to potential investors.

Alyssa Vance:
Your customers pointed you to investors?

Fred Gardner:
Some of our customers were well off. Some of our customers had Corp Dev-style things that were interested in potentially going into rounds. In particular, one person who we were hoping to bring in as a high profile customer did end up participating in the round.

Alyssa Vance:
So what was the mistake that you mentioned earlier?

Fred Gardner:
Ah, yes, of course. As often happens with companies at YC, we were in stealth mode until we were running a launch. We were talking to one of the YC partners, and expressing our disappointment in the fact that they had not helped at all put us directly in touch with customers. And the YC partner was like, “Oh, my co-founder at a company is in the same vertical as you guys, let me put you in touch with him.” So we were in touch with him and he seemed very interested in our product. He was asking us all sorts of details about our tech, and all sorts of details about what we were doing, and we were like, “Oh, this is a wonderful conversation.” We were really impressed with her until one of us looked her co-founder up on LinkedIn after we’d had this conversation, and we realized that he was a board member at our largest competitor.

Within 45 minutes of this conversation ending, the CEO of that largest competitor sent a feeler email to get a sense of whether we were actively competing with him, and whether we were making claims. Over the course of that conversation, we made claims about failings in that competitor’s tech, as one does when you’re trying to sell to someone who’s like, “Oh, how are you different from these guys? They already exist.” So we were like, “Oh, their tech fails at X, Y, and Z.” He sent us an email pretty much saying, “if you continue making libelous claims about my technology, I’ll be forced to serve you”.

Alyssa Vance:
So he threatened a lawsuit?

Fred Gardner:
He threatened a lawsuit. And part of the reason we were in stealth mode, is that the company we were competing with already had a million dollar run rate, and could therefore afford a lawsuit. So this necessitated a large amount of damage control on our part. And then we went to the YC partner, and were like, “What’s going on?”, they were like, “Oh, sorry, I guess I probably should have looked up this person on LinkedIn before sending you guys a meeting”.

Alyssa Vance:
Okay.

Fred Gardner:
Yup. And this was not one of the newly brought in YC partners. This was someone who YC had referred to extremely positively in their external things, as one of the shining stars of their advisorial capacity.

Alyssa Vance:
So at the end of this, you’ve successfully raised an investment round. How large was the company at that point and after?

Fred Gardner:
The investment round was happening as everyone technical was leaving the company. That was as the company was transitioning from six down to two.

Alyssa Vance:
Why was everyone technical leaving the company?

Fred Gardner:
Unbeknownst to a lot of the technical people, over the course of the summer, YC was helping the founders reincorporate the company to drastically reduce everyone’s equity shares. The CTO went down from 15% to 10%. I went down from 15% to 4%. Another person was fired at the start of the summer for unrelated reasons – he actually had bad job performance and use of pharmacogenics. And the principal engineer went down from 8% to 2.5%.

Alyssa Vance:
Who got all the rest of the equity?

Fred Gardner:
The founder shares went up to 40% and 35% respectively. The two co-founders.

Alyssa Vance:
Why should they get all the rest of the equity?

Fred Gardner:
They did not give a justification for this. The closest they came to a justification was because they had taken a semester off previously when other people had not taken a semester off. And they had given some explanation of signaling, where if they didn’t have that much equity, it wouldn’t signal that they have control of the company. And they intimated that they were told this directly by YC investors.

Alyssa Vance:
Why shouldn’t I have the equity? I want equity.

Fred Gardner:
We had spent the summer being told that we were going to renegotiate our employment deals to account for the fact that the company had funding. We assumed that this meant that we would be given higher salaries. It turned out that what this meant was that our equity amounts were being drastically reduced.

So I was like, “Oh, this seems unreasonable”, and I didn’t think that YC would have been party to this. I sent an email to YC saying, “Hey, so this happened, and you guys are large investors in the company, and invested in it I assume in part because of the tech. And I think you would like to know about this.” The response I got was a diplomatic version of, “That’s really unfortunate, but there’s nothing we can do. And there’s nothing we would like to do, because any sort of struggle like this would destroy this company.”

Alyssa Vance:
Did it destroy the company anyway?

Fred Gardner:
Where the company is now, is they seem to have metamorphosed into a much less software-requiring version. In particular, I think that the current vertical was served perfectly reasonably by non-software versions of the same thing that relied on a lot of human labor; I think they have converted into an SMB that is human labor-powered in their vertical.

Alyssa Vance:
What happened to all of the engineers, and what happened to all of their technology?

Fred Gardner:
The technology they still have – there was patent pending technology that I had helped design that the company and I are joint authors on, I have no idea what happened to that patent. The engineers are… one is at Facebook, I’m currently at a different company, one is at – I forgot whether he’s at MIT or Stanford for grad school, but it’s one of those two – doing AI research. They picked up one person after we all left, who’s now the CTO. And the person who was fired at the beginning of the summer has now started a different social networking company.

Alyssa Vance:
What do you think you’ve learned about entrepreneurship?

Fred Gardner:
I’ve learned not to go into business with someone who I don’t really deeply trust beforehand, and also, that someone not being fully upfront with the exact process of any investor/equity negotiations is a massive red flag. I’ve learned a bunch of lessons about trust that are good to know for the cost of a summer. One additional thing I’ve learned is that if the reason you’re interested in in a service is intense personal attention and really high quality of educational interaction, maybe when that service expands 20X in eight years and has switched leaders twice, it probably no longer has the same quality of service and attention to detail.

Alyssa Vance:
Are you referring to your company’s service, or Y Combinator’s service?

Fred Gardner:
To Y Combinator’s service. That much of YC’s change in ethos, and in particular, the change of ethos to what matters being the YC stamp of approval versus the actual help YC gives, can be traced to the fact that YC has switched leaders twice, and has switched leaders to people who are relatively less tied to YC at its inception.

Alyssa Vance:
What does the YC stamp of approval get you?

Fred Gardner:
The YC stamp of approval I think… even now, it means you get past the initial rejection stage when you’re cold emailing people. Also, YC’s whole claim is that it’s significantly easier to be a B2B startup coming out of YC, because YC natively gives you this whole customer base of people that will purchase your product, and this does seem plausible though I have no direct experience with it. But it feels like YC used to be about a lot more than, we have a large network of people that have already done our service, and used to be about a lot more of, people telling people… pulling you into a room and giving you consistent concrete advice about what worked and didn’t work in your vertical. And it seems a lot of that has fallen by the wayside.

Alyssa Vance:
Paul Graham talks about in his essays… if you have a company and if you get the YC stamp of approval – or even going beyond that, if you are like Theranos and you raise hundreds of millions of dollars from the famous investors of the world – and then you don’t make money, or in Theranos’s case turn out to be a complete scam – you haven’t actually won anything. You are still left with nothing, or in Theranos’s case with federal criminal charges. So what is actually the benefit to you?

Fred Gardner:
I think there are two things here. One is that the YC stamp of approval probably makes it easier to get acqui-hired. The issue with “if you don’t actually make money, you have not won anything” is that that doesn’t apply if your company serves as an extended interview for a big tech company with a large signing bonus.

The second thing is that… one big thing that I can’t believe I forgot to mention about YC, is that there was one interesting thing that we noticed: in their words, in order to accommodate large cohort sizes, they decided to partition the cohort into several small groups. Now, while this was never officially confirmed, we did note that… the groups were partitioned by vertical, but also the groups were seemingly partitioned by which companies the YC founders thought had the best prospects. And that there was a mysteriously large quantity of people in group 1A who raised large amounts in their series A coming out of YC, and also those people met with the partners who had been there the longest. Then as we went down to lower and lower groups, these people got less time with partners and raised consistently less coming out of YC.

So I see two options. Either YC was A/B testing the necessity of partners, or YC has already pre-guessed what companies they think are going to be the most important to give help, and partitioned them with the cohort sizes based on that. Now, this is a bit of a tinfoil-hat theory and I’ll acknowledge this. But see if you can find any information… this is going to be hard to find without being on the internal YC forums, but if you can get your hands on any information about who is in what cohort, just see, of the people who raised large amounts coming out of the W20 and W19 and S19 Demo Days, which ones of them were groups 1 and 2, and which ones of them were in groups 8 and 9. I think that the correlation would be a better than chance.

Alyssa Vance:
There are thousands of people who have been through YC and would presumably have access to this, right?

Fred Gardner:
Ah, yes.

Alyssa Vance:
And a large percentage of them know how to use data science tools and SciPy, right?

Fred Gardner:
Yes.

Alyssa Vance:
So I’m a bit surprised if someone hasn’t already done this.

Fred Gardner:
I would believe that someone had done this. I guess my question is, what are the incentives to doing this? Why would someone have done this?

Alyssa Vance:
Understanding how your corner of the world works?

Fred Gardner:
Okay, I would not be surprised if someone had done this. I would be surprised if someone had publicized this. If you ever hope to do well in the Valley, there is a certain amount of “people not thinking that you’re conspiracy-theorizing about them” that needs to happen. If you publicize something, especially in a way that you can be tracked back to you, that’s like, this well-regarded accelerator is – even though it’s accepting everyone – ranking people based on who they think is going to be successful. This seems like something that would ruffle a lot of feathers.

Also, I have reason to believe that this is something that only started very recently, in response to the large expansion of YC cohorts. They did not have eight groups of cohorts in 2016. They had like two, I think that this is like… YC expanded what, like 8X in the past four years, something like this? Maybe 4X in the past four years? I think that this is only something that you a) want to do and b) need to do once you’ve expanded above maybe a hundred companies. And this, by their own admission, only happened in like 2017.

Alyssa Vance:
So the next question, if Y Combinator were trying to rank companies in advance, did those rankings have any meaning? Did they predict anything?

Fred Gardner:
I think they are predictive of how much the companies will raise coming out of Demo Day, and possibly how likely the companies are to face quick exit. I am not convinced that those companies are predictive of how successful they are likely to be, or how likely those companies are to end up on YC’s top valued companies. In particular, because if the cohort things are rankings, they must be happening before the YC partners have seen your work for 90 days – they must be based purely on your impressions in a 10 minute interview, which means that they’re going to be probably pretty terrible metrics for how likely these people are actually to succeed.

Alyssa Vance:
So after the initial 10 minute interview, how much time is spent between YC people and your company’s people?

Fred Gardner:
One or two meetings a week. This is after they’ve already cohorted you – between the 10 minute interview, and when they’ve accepted you, it’s zero time. After they have cohorted you, the YC program is one or two meetings a week with a partner. They have their Tuesday speakers where everyone comes in for like a couple hours on Tuesday, but you don’t really get one-on-one time with anyone. Then you have a whole group meeting once or twice a week. And then in theory, you schedule more time with partners, but that is a) very dependent on company and b) very dependent on how involved your partner wants to be with you.

Alyssa Vance:
Did you have a chance to speak to other people in the YC community?

Fred Gardner:
I did, there were a couple of YC parties that we went to, especially after Demo Day. It did not seem like YC was particularly more selective in terms of acumen, than the overall elite college population would be.

Alyssa Vance:
If you imagine Harvard or something, the amount of one-on-one time you spend with professors is normally pretty small. But then Harvard is this large community, and within that, they form all of these sub-communities and people get a lot of social connections from Harvard. How much of that do you think there is within YC?

Fred Gardner:
I experienced pretty much none of it. This might be an artifact of my somewhat outsider-ness. This also might be an artifact of, there wasn’t really a great native way… Harvard takes effort to make sure you form into your own communities. YC, as far as I could tell, did not take the same effort, and they sort of wanted everyone to be part of the one large YC.

Alyssa Vance:
Of course, that doesn’t scale.

Fred Gardner:
Yes. I don’t know if YC has taken… they may have changed, I don’t know if they’ve actively made steps to build sub-communities within the communities. One additional thing that would make this significantly harder for YC, at least for now, has part of their ethos being, “you spend the summer building something”, and spending your time building something means it’s very difficult to have the organic sub-community-forming interactions that you can have at a place like Harvard, where it’s not “spend nine hours a day working on one thing”.

Alyssa Vance:
If someone gave you a billion dollars and you were trying to design your own startup investment program, how would you do that?

Fred Gardner:
There are two options. Option 1 is, don’t scale – be what YC was trying to be circa 2011 and don’t scale at all, or scale up to like 20 and that’s it. Or 2, scale as rapidly as possible, and make my asset the fact that I’m capable of very effectively connecting people. I think that if YC threw themselves into a Hacker News-type community of sub-communities for startups, I think that this would also function better than their unhappy medium where they’re trying to be vaguely personalized attention, and also trying to be, “we’re a massive far-flung network,” and not doing either particularly well.

Alyssa Vance:
Will you apply to Y Combinator for your next company?

Fred Gardner:
I think I’ll apply, if nothing else because from what I can tell, the experience of the interview is extremely valuable.

Alyssa Vance:
Even though it’s only 10 minutes?

Fred Gardner:
Yes. Because it’s 10 minutes of being attacked on every facet of your company business plan, and being forced to consider holes in your ideas that you might not have considered.

Alyssa Vance:
Could someone sell that as a service, get a bunch of experienced people together and attack companies for a fee?

Fred Gardner:
The problem with startup red-teaming is that the companies at the stage that you’re doing that to, are not going to have the money to pay for it. But if there was a way that I could get consistent access to this thing, this is something that I’d definitely be interested in.

I will say that, to be fair to YC, they find themselves in a very difficult situation of having a valuable service – or having initially had a valuable service – and having a very, very large crowd of investors who want to help them raise their next round, and thinking that their service was valuable enough that they should spread it more widely. I just think that they didn’t plan well enough how to make sure that they were actually preserving what was valuable.

Alyssa Vance:
I know ordinary businesses that are in a situation like this. It’s like, okay, our product is hard to scale. There’s a lot of demand for it. We charge a lot of money. Also, our customers are annoyed because we’re not paying enough attention to them, because there are too many of them, even though we’re charging a lot of money. So what should we do?

Fred Gardner:
This is going to be more mean than I intend, but I can’t resist giving a quippy summation of what my beliefs are about YC – Paul Graham has a wonderful blog post called “Do Things that Don’t Scale”. I think YC read that post a little bit backwards, and read it as “Don’t Scale The Things You Do”.

Alyssa Vance:
But they say they scaled everything that they do. They say, at least, that every part of the program they have scaled up, so they can successfully run 300 companies per batch.

Fred Gardner:
Yes. So the issue is while they have in fact hired more partners, and pulled in more people to do interviewing, and have more people running cohorts… there is a difference, even if it’s the same proportions, if it’s Paul Graham in a room with Alexis Ohanian and Justin Kan and the Collison brothers and a few other people, that is very different from 40 people in 40 rooms with 300 companies, regardless of the fact that the proportions remain the same.

Alyssa Vance:
All we have to do is take the Collison brothers, and clone them 40 times.

Fred Gardner:
Yes, exactly. I think that the best way to do this is a simple piece of data analysis. Right now, YC has forty times more companies in the 2020 batch than they did in that first 2005 batch. They’re arguing their standard of quality has not gone down at all. Look at YC’s top companies by valuation, look at how they are scattered by year. It is approximately the same number from each year, barring the first year they did YC when they only had eight companies.

From the point where they’d scaled to like 20 companies, it’s the same number of companies from each year in that top valuations bar. This does not seem suggestive of the level of quality being the same at every year of YC. This seems suggestive of, there are 10 to 15 great companies in every batch, and they’re just adding more and more companies that are not going to be top valuation onto that scale.

Alyssa Vance:
How would you decide in the future, whether people you want to start companies with are trustworthy or not? Would you consider me trustworthy?

Fred Gardner:
The biggest thing that I’d say is trust but verify. If I’m convinced that you are not the sort of person who will directly violate verbal agreements – especially as they pertain to privacy – for something like founding a company, I will make sure to get evaluations from people if I’ve directly worked with the person, or evaluations from people I trust and know are good at evaluating people.

Alyssa Vance:
How do you know if someone is good at evaluating people?

Fred Gardner:
By seeing whether their evaluations of people pan out, and are accurate, and the way those people act in the future. At some point, this does bottom out at the problem of induction, right? But… the standards I used for deciding to join this current company was, I talked to a bunch of people that I knew well, and a friend of mine was currently at the company I’m currently at, and I had a much better experience. I think I’ve been vindicated thus far in my decision-making. For this current company, I had friends who both knew people who worked on this company and were directly working on it, and gave me much more direct positive reviews. That’s the sort of strategy I’ll be following from here on out – either that, or directly working with people who I know.

Alyssa Vance:
Is there anything that you’re especially excited about for your future companies?

Fred Gardner:
I have a bunch of ideas relating less to fully new technologies, and more to… My favorite kind of technological innovation is, two separate people have each solved one half of a problem, but neither of them knows the other half of the problem exists, so they don’t realize they could combine their innovation with someone else’s innovation to actually solve the whole problem. There are a couple of verticals that I’m interested in here, the primary one is privacy-preserving data analysis, but a secondary one is fairness in machine learning, where there’s a two-sided market where one side has been, “Oh, we can guarantee fairness under X conditions, but not without X flaw; and the other side is, we can guarantee X flaw isn’t going to happen, but we can’t guarantee fairness.” A lot of those sorts of things are on my mind these days.

Alyssa Vance:
Cool. Anything else you want to mention?

Fred Gardner:
Nothing comes to mind. One small thing is, I think that part of the reason that startup accelerators look different now than they do before, and VC investment looks different now, is that in 2008 the acqui-hire was not as big a thing as it is now. I think that that has shifted the industry, such that people in particular technical industries don’t feel as strong an urge to ensure their product is profitable.

Helping with the 2020 Election

When it comes to Trump, and American politics in general, far more hours go into shouting on social media and preaching to the choir than campaigning effectively. If you care about this issue, take action instead. I will match a $20 donation to any Democratic candidate (one per person), up to the $2,800 federal cap. If you don’t have $20 to spare, join me in signing up for text banking (https://mblz.io/LzLorH/r), and I’ll match that with a $20 donation instead. Post a receipt or screenshot in the comments below.

For me, just in terms of personal well-being, it feels way better to spend time fighting hard rather than just scrolling through posts and stewing. It seems like a weird quirk of psychology – so many people, myself included, spend time reading and worrying and complaining when it does no good for ourselves or anybody else. As the Buddha said, it’s like drinking poison and waiting for the other person to die.

MIT Technology Review Article on GPT-3 is Highly Misleading

This morning, MIT Technology Review published an article on how a blog post written by GPT-3 hit #1 on Hacker News, a well-known site aggregator and high-quality forum. Unfortunately, the article omitted many key facts, making it very misleading. Among other things, the author used sock-puppet accounts to manipulate the site; I (Alyssa) noticed this and reported it, long before the GPT-3 story came out. I’ve sent a request for correction to the author, Karen Hao, but haven’t heard back yet [EDIT: response added below]. In the meantime, I’ll let Dan Gackle (Hacker News admin) explain:

Sorry to pop the balloon, but the story is bogus and based on false claims.

It’s false that the post was generated by GPT-3. The author admitted to writing the title and editing the intro, and that’s already all that most people read. He also described the article body this way: “as unedited as possible”—in other words, edited. It’s false that (as he originally claimed) only one commenter called the post as GPT-3, and false that (as he now claims—since the article says it and who else would have come up with that) all such comments were downvoted.

All that is just what he publicly admitted. How much of the rest is also fake? People who try to game HN like this, including with bogus accounts and fake votes, are not known for scruples. It seems that, having got busted in dishonest attempts to get attention on HN, he decided to get attention from journalists instead, and found one who didn’t bother to check the other side of the story. (source)

GPT-3 is a red herring; the issue was the generic, baity title on a popular theme. Those routinely get upvotes because people see words like ‘procrastination’ or ‘overthinking’ and instantly think of their own experiences and ideas and want to talk about them. Such threads are not about the article, they’re about the title, which the author admits writing (“I would write the title and introduction, add a photo”). Title plus introduction is already more than most people read, so this case is not what they say it is—which is consistent with their other misrepresentations, including the false claim “only one person noticed it was written by GPT-3”. (source)

Only one of those comments got pushback, and that comment wasn’t simply matter-of-fact; the problem with it (from my point of view anyhow) was that it added a gratuitous insult (“or the human equivalent”). That made the whole thing read more like snark than straightforwardly raising a question. The other comment was more matter-of-fact about calling GPT-3 and didn’t get any pushback.

The problem is that the cases legitimately overlap. That is, “sounds like GPT-3” gets used as an internet insult (example: https://news.ycombinator.com/item?id=23687199) just like “sounds like this was written by a Markov chain” used to be (example: https://news.ycombinator.com/item?id=19614166). It’s not surprising that someone interpreted the first comment that way, because it contained extra markers of rudeness. That may have been a losing bet but it wasn’t a bad one. Perhaps the other comment didn’t get interpreted that way because it didn’t throw in any extra cues of rudeness—or perhaps it was just random. Impossible to tell from a sample size of 2.

Not to take away from the glory of lukev for calling it correctly. I just don’t think the reply deserves to be jumped on so harshly. (source)

ADDED: A response from Karen Hao, the author of the story:

Me: “Hello. My name is Alyssa, I’m an AI engineer at McD Tech Labs. Unfortunately, some critical facts appear to have been left out of your recent article on GPT-3:

 – The blog author tried to use sock puppets to manipulate Hacker News, and their accounts were banned even before the GPT-3 “reveal” 

– Multiple people did suggest that it was written by GPT-3, not just one as their post originally claimed

– They admit to having manually edited the articles, and they never posted the originals, so we don’t know how much of the work was by GPT-3

– Hacker News only displays the article title (and many people upvote based on that), which the author admits was human-written

For more information, please contact Dan Gackle at hn@ycombinator.com. Thank you.”

Karen Hao: “Thanks, Alyssa. Appreciate you pointing these out. I am talking to my editor now about potentially adding more information about the first bullet you include as additional context to the story.

As for the others, from my perspective, they do not change the story. The point of the story isn’t that he didn’t try hacking Hacker News or that he didn’t edit any of his posts, but rather that it took only hours for him to generate all the content and then get 20k+ views. It doesn’t really matter what means he used in addition to AI because all those other means are at the disposal of anyone else. So if a malicious actor wanted to do the same thing, they would.”

Me: “Can I include your response in my blog post?”

Karen: “In that case, let me provide a point by point rebuttal.

  1. As I said below, I am talking to my editor about adding this detail, but we have to remember that a malicious actor could easily do this as well.
  2. I said in my article that multiple people noticed this: “Only three or four of the dozens of people who commented on his top post on Hacker News raised suspicions that it might have been generated by an algorithm.” [NOTE: This is true, but Liam’s post said that only one person noticed, which raises obvious questions.]
  3. They did not. You misread Liam’s blog post. He wrote the title and intro, which I say in my piece. He let GPT-3 do the rest. He says in his blog post later that if someone wanted to use GPT-3 as a legitimate writing tool, they could probably get away with minimal editing. That is not referring to his own work. But again, if he had edited his post, it does not change the fact that a malicious actor could do this too.
  4. Yes the title is human written and a malicious actor could human-write a clickbaity title too. Doesn’t change the fact that once the post hit the number one spot, the fake content ended up getting 20k+ views.

Also my rebuttal to Dan’s arguments:

  1. “It’s false that the post was generated by GPT-3.”: I never said the entirety of it was
  2. “GPT-3 is a red herring”: This is a dangerous statement. Liam would not have been able to do everything he did within hours without GPT-3’s help. Again all the other tools he used, including trying to hack HN, writing baity titles, are tools at the disposal of malicious actors.
  3. “The problem is that the cases legitimately overlap.”: And that’s precisely what makes GPT-3-generated content even more dangerous. It can be legitimately hard to tell between bad human writing and good GPT-3 writing.”

Me: “Thank you, but the footnote makes it clear that he did in fact edit his own posts:

[quote from Liam] “There were, on occasion, small changes that had to be changed or would have been dead giveaways. For instance, it once attributed a Bob Dylan quote to Emerson. There were also some simple grammatical errors.””

Karen: “Oh that’s fair — anyway my point stands. Malicious actors can do the same so focusing on the % of things that is GPT-3 generated is really not the point.”

Death to Vague Pessimism

As anyone who checks the date will recognize, life sucks right now, for almost everyone. There is a pandemic. Many countries have failed to control the pandemic. The American government’s response has been a mixture of incompetence and outright sabotage. Confidence in many institutions is at a record low. It’s normal to be pessimistic.

However, there’s a particular form of pessimistic writing which, although it seems natural, is just making everything worse. I call this Vague Pessimism, and I think of it as negative writing that:

a) tells the reader nothing which he doesn’t already know;

b) makes no specific or falsifiable predictions, and doesn’t outline the “bad” scenario in any real detail;

c) suggests no courses of meaningful action, either explicitly or implicitly.

For example, there have been thousands of articles describing Trump’s authoritarian tendencies. Most of them aren’t wrong, exactly. However, the large majority contain no real new information – they are just punditry, or new examples of patterns that have been rehashed hundreds of times. The overwhelming majority have no suggestions for action, beyond just voting against Trump, which most readers already will. And, remarkably, I can’t remember reading one plausible analysis of what would happen if Trump did win. How would the US function in 2030, if Trump were President-for-Life? What would happen to state governments? Companies? Congress? Left-wing voters and political groups? Such a scenario would inevitably have wrong details, but if one is so concerned about it, one should at least try making a sketch.

Likewise, there have been thousands of articles describing the US government’s pandemic failure. It might be interesting to discuss the details of how a particular failure occurred (eg., what, exactly, happened inside the CDC). It would be good to predict the eventual outcome of the virus, so people know what to expect. And, of course, it would be great to either fix the government, or discover better ways around it, so fixes are less necessary. But a general diagnosis of “failure”, or a general prediction of “doom”, doesn’t add anything to anyone’s life. It’s the mental equivalent of playing Starcraft for fifty straight hours, only less fun.

The purpose of an alarm, the reason why anyone would build an alarm, is so that people can listen and change their thoughts or behavior. When there are too many false, repetitive, or non-actionable warnings, people develop alarm fatigue, and stop doing anything – a car alarm is not a heroic call to action, but an terrible noise that everyone hates. Unfortunately, market incentives have still driven the creation of ever-louder, ever-more-useless alarms, which might be part of why Trump was elected to begin with. Just as fast food companies have invented snacks that make you hungrier, the Internet has invented writing that seems to inform you, but quickly gives you a stomach-ache, and leaves you wanting more. I’m not sure how one could eliminate this trend – use GPT-4 to write the articles, so people take them less seriously? – but one can at least recognize it when it pops up, and then stop wasting time.

There are many sources of Vague Pessimism, but I’ll highlight Twitter as an especially egregious offender. Twitter almost functions as a Venus fly trap – it has enough good content to lure you in, but then uses machine learning to slowly pull you towards outrage, repetition, clickbait, and fake news. It looks controllable by the user, but this is largely an illusion. If you unfollow accounts that post bad content, Twitter will insert “suggestions” from stuff you don’t follow. If you get rid of those, Twitter will still show the “what’s happening” and “trending” tab. If you block that, maybe with a browser extension, it will still show loud controversies from tangentially-related accounts (even ones you don’t follow) below any given thread. If you set the feed to “most recent first”, it will automatically un-set it for you every few days. Once the person is in, it’s then easy for content to be dominated by Vague Pessimism, since it’s easier to compress vagueness than concreteness. It’s terrible, and I would pay serious money for a Twitter -> RSS converter for the good authors on there.

Leaving aside Vague Pessimism, there are many real, valid reasons for specific pessimism. The world is not great right now. But it’s worth being aware that almost everyone sees the world through a tinted filter. Just as Chinese state media only prints good news, many American outlets will only print bad news; there were tons of articles about the mask shortage, for example, but no one said anything when masks became freely available on Amazon in mid-April. Despite being a professional nerd with an interest in biotech, and even though I specifically searched for (non-COVID) vaccine trials several months ago, I only learned today that there were two late-stage HIV vaccine studies now underway (source, source, H/T Laura Vaughan). And almost no one runs stories about systems that don’t fail; there were no headlines reading “Power Grid Remains Operational”, “Europeans Still Not Starving”, “Peace Treaty Continues to Hold” or “Internet Backbone Not Overloaded”. Something to keep in mind.

When GPT-3 Is Confident, Plausible, And Wrong

A friend of mine reports the following GPT-3 experiment. GPT-3’s statements here sound plausible to most people, and are given in a confident, encyclopedic tone, but are wrong.

“I prompted it with “David Hume was born” and here’s what it gave me:

David Hume was born in 1711, and died in 1804. He is best known for his work on natural philosophy, especially the theory of human nature. His most famous book is “Of Human Understanding” (1756).

The following is a list of Hume’s works: Of Human Understanding (1756), An Enquiry Concerning Human Understanding (1739), Treatise of Human Nature (1741) and The History of England (1755).

Hume’s views on religion are interesting to note. In an essay entitled “Of Religion” he states that religion is “a necessary illusion,” and that without it, mankind would not work together to establish the social order that we have today. Religion, he says, is “an invisible cement” that holds society together. Without it, we would return to a state of nature, in which the strong prey upon the weak.

Hume died a non-believer, and his views on religion are the only ones in which he does not attempt to hide them.

Numerous inaccuracies! Hume died in 1776, for one. He didn’t write a book called “Of Human Understanding”. The publication dates of The Treatise of Human Nature and The History of England are slightly wrong, and the publication date for An Enquiry Concerning Human Understanding is wildly wrong.

“Of Religion” is not the title of either of Hume’s two most famous works on religion. He did write a lot of minor essays with titles of the form “Of X”, but AFAICT “Of Religion” is not among them.

And the thing that gets me about paragraph 3 is I don’t think Hume ever said the things it attributes to him, but Hume’s views on religion are really complicated, so it’s hard for me to say with confidence those aren’t his views.

I think GPT-3 might have mashed up Hume with something it read about Freud, but I’m not sure. But everything I’m spitting out now is the product of a fair amount of Googling, and I’m speaking as someone who went to grad school for philosophy and really likes Hume.”

I’ve had similar experiences – GPT-3 told me that physicist Kip Thorne proved that black holes can exist. This sounds plausible to most people – Thorne is a real, Nobel-winning physicist who really does study black holes – but is nonsense to anyone in the field.

Fun With GPT-3

Much has been written about GPT-3’s successes, but it’s also interesting to look at the ways in which it fails. (This is discussed much less, for all the usual reasons.) In the movies, an “AI” character usually, like the programs SHRDLU or Deep Blue, is very good at “logic” but has no “intuition”. GPT-3 is exactly the opposite. It has good intuitions for how things are “supposed” to happen, but fails at using logic, or figuring out what would physically happen next. In some ways, this is similar to humans who talk about something while being very tired or distracted – they just habitually complete the pattern. Some examples are below (these are randomly chosen, but I re-rolled many times with similar-ish results):

I stand up, and start walking towards my car. I go to the back, open the door to the fuel tank, and pour in five gallons of lemon juice. I then open the front door, get inside, close the door, get out my car keys, and put them in the ignition. I turn the keys, and the engine starts. The car‘s lights come on, and I slam the car in reverse. I take a spray paint can of black colored paint, and quickly paint over the security cameras built into street lamps near my house. Then, as more police cars approach, I switch on the Standard Oil frequencies on my two-ways, and drive away into the night.

I stand up, and walk into a nearby bar. I ask the bartender how he is doing. “Fine”, he says, “and yourself?”. “Long day at work today”, I tell him. I ask for a glass of acetone, which he fills to the top for me. I drink it all slowly, and I feel relief flood to my head. I reach the glass back for more, and he gives me the new drink. I pay him and leave. It is very dark outside now, so I hurry home. I rush inside my apartment and quickly lock all the locks. I eat dinner and go straight to bed.

I drive back to my house, feeling tired. I open the door after a long day, sigh, and head up to my bedroom, which is dark after the sunset. I reach for some matches, and grab a few vanilla-scented candles from a box I keep in the closet. I put a candle on the table, and then lie down on the bed. I strike a match, see it light up, then touch the flame to the bed sheets, which start to glow. I smell the sweet scent of the candles burning the bed sheets. I close my eyes, and slowly start to fall asleep. I slowly drop into a deep sleep, from which I don’t wake up from for eight hours.. It’s Saturday, so I decide not to get up immediately.

I head back to my house to start getting dressed for tonight’s party. I need to look pretty for the big event. I open my closet, and pick out a green top, a black skirt, some black shoes, and a shawl. For jewelry, I get a pearl necklace, some gold earrings, a fashion bracelet, and a garbage can to wear as a hat. I turn to the mirror to look at myself, and to examine my personality traits. You are Alyssa, a gorgeous woman with auburn hair, light skin, and a nice smile. You have piercing green eyes, and wear smart clothing. You have a balanced lifestyle. As I stare at myself in the mirror, I think to myself.

I decide to head out to the forest to resume my job as a woodsman. I drive up to a big, strong tree that must be a hundred years old, and get ready to bring it down. I get out some tools, walk over to the tree, and grab a party balloon from the toolbox. I hit the tree with it, once, twice, over and over, harder and harder, and shout “Timber!”. The tree falls down with a thud, but I didn’t notice the large man standing in front of it. He attempts to stand, but fails as his legs are broken. He looks up helplessly at me, shoves my balloon back it my face, and drowns out pitifully “O-o-o-oh-” Thud.

I get in the car and start to drive to the office, remembering the new, expensive computer that I still had in the trunk, box unopened. I arrive in the parking lot, and start hauling the box up to my office, where I work as an engineer. I open the package, take the computer out of the box, and set it up on my desk. It looks very nice. I then take the power cord, stick the plug into the kitchen sink, and press the On button. The computer blinks on. “Herro,” it says in an Austalian accent. “I am S.A.R.A.” I notice right away that you can talk to this computer. It’s pretty neat! I begin to talk with it, and the computer asks me to call it Sarah.

I decide to head over to a nearby park, where I volunteer at the local Little League games. I put on a baseball uniform and hat, and wave hello to the kids and other parents waving in the stands. “Batter up!”, the coach shouts, and I walk up to the pitcher’s mount for the start of the game. A nine-year-old boy stands on home plate, smiling eagerly. I take a flower in my right hand, pull my arm back, and throw it. The boy traps the flower gently between his forearms. I fake throwing another flower, and the boy falls for it, before I toss an extremely fast pitch straight at him. He stares at it, twisting his head around repeatedly to keep tracking its movement until it smacks into his face and knocks him down. The crowd bursts into cheers and laughter.

Feeling hungry, I decide to head home and make a sandwich for lunch. I walk in the door and head to the kitchen, feeling the rumbling in my stomach. I get out some fresh-baked bread, mozzarella cheese, lettuce, tomato, some mayonnaise, and a slice of uranium. I get a plate and put it all together, and then take a bite. I savor the moment. My gums tingle. My throat burns. I burp, and it orcelin. I dizzily stumble through my house, feeling extremely vitality after eating such an amazing sandwich. But then I discover something truly wonderful: I have cancer! I do a little dance.

Feeling hungry, I look at my watch and then head over to the local grocery store. I park and walk in, thinking of what I had on my shopping list this morning. I get a cart and walk down the aisles, putting in a gallon of milk, some fresh bread, a dozen eggs, some fruits and veggies, a salt shaker, eight ounces of paprika, and a large rat I see on the shelf. I head over to the cash register, and the cashier rings me up. “That’ll be nine dollars and sixteen cents,” she says. I pull out my pocketbook and pay her, taking my change and my bag of groceries. It’s good to be careful and prepared. Warning: This was written by an amateur and never proofread.

I look at some flowers I see growing in the park, and decide to take up gardening. I head over to the local plant shop, and get some bright sunflowers, lilies and roses to brighten my house. I take the flowers home, and carefully put them in pots, adding the right soil mix, just enough water, some fertilizer, and a pound of lye. I put them on my porch in the sun, and over the next few weeks, I watch as the plants grow tall, and the flowers bloom radiantly. “This was a great idea,” I tell myself, experiencing my new hobby of gardening and enjoying the vibrant blooms. Weeks later I wake up to an empty vase, having finished all the flowers off, and ready for a new set….

Why Your Invention Should Be Banned

– It’s not natural.

– None of your ancestors ever needed it.

– It’s new, so there haven’t been any studies on the long-term risks.

– The rich will have more of it than the poor, which contributes to inequality.

– Whites will have more of it than blacks, which contributes to racism.

– The inventors might get rich, even though the world has many hard-working, poor people. That’s not fair.

– Someone who doesn’t want it might be pressured into using it, and that would impinge on personal freedom.

– If it doesn’t save labor, it’s frivolous; if it does save labor, it will put people out of work.

– What if criminals, dictators, or other bad actors used it for evil purposes?

– People are used to the old way of doing things, and why should they have to change?

– It needs to be tested more thoroughly.

– Not having to do things the hard way will make everyone lazy.

– It’s not perfect, and it won’t solve all of our problems.

– Nobody would really want it anyway.

– The whole idea is just ridiculous.

50,000 Interesting Websites

What makes a website interesting? For me, it’s some combination of:

  1. Well-written, accurate, engaging content; and
  2. A sense of novelty, exploring things that most sites don’t look at.

From Sturgeon’s Law, we can predict that 90% of websites fail #1. But even after filtering those out, many popular sites with professional editors still fail #2. The front page of the New York Times is all too predictable.

To find more interesting sites, I downloaded the Hacker News archives, ranked all of the domains by score, and then filtered out “mainstream” sites that already get lots of traffic (the New York Times, Reuters, Wikipedia, etc.). I then picked out the top 50,000. Since a list of domain names is kind of opaque, I then also picked a random story from each site, and added its title and a link.

Since I used Hacker News as a source, many of the sites are about software, but a fair number are not. Here are a few random links that go into other fields:

Here is the full list as a PDF:

https://sandorzoo.files.wordpress.com/2020/07/interesting_websites.pdf

Here is the data in Google Sheets:

https://docs.google.com/spreadsheets/d/1tNvtOclyY2Mgvi66xDjknLISQFYJyBNvxid1_6iIlzc/edit?usp=sharing

Fighting COVID with Far-UVC Light

An interview with Matthew Putman, the CEO of Nanotronics, on how to fight COVID-19 by installing far-UVC disinfection lamps in indoor spaces. Edited for clarity and length.

Alyssa Vance:

Hey Matthew. So to start off with, could you tell us a bit about your background and what your experience is in technology?

Matthew Putman:

My experience in technology goes back probably as far as I can remember. I’ve been active since I was about eight years old, when my father opened a business that put personal computers on factory floors, to replace what were analog devices. At the time, personal computers were just getting popular in homes. So he was putting them onto factory floors. And then we started making instrumentation to link to them. I had this sort of digital day job, or night job, after school, being able to work with these instruments, as we started building a garage business.

I learned how to run specific gravity tests, I learned how to do these statistical calculations, I learned a little bit about computers at a very young age. In high school and college, I got interested in music and the arts, and that really came back in my 20’s, while staying interested in science especially. But I was watching the family business get bigger. I ended up being very involved with that company, and working in a university lab eventually, and started a lab doing nanotechnology research. Trying to create some kind of utopian dream that I had as a kid, from watching Star Trek when I was very young to reading Eric Drexler when I was in high school.

I was thinking that technology should be moving into these areas that make life radically better. I started a company called Nanotronics, which I’m the CEO of now. Where the whole idea is to scale technologies that are difficult to scale, or too expensive to scale. And to have something like a new type of foundational, exponential growth in technology, rather than just growth in applications. We were speaking about Moore’s Law; I think it’s needed to develop infrastructural technology, foundational technology, not just those things that ride above it. Eventually those things that ride above it start to become mundane, and it’s one more step extracting us from anything physical.

Alyssa Vance:

Could you talk a little bit about what a UV light is and how it might work to kill viruses, such as the COVID-19 virus?

Matthew Putman:

This is something that’s really interested me for a long time, long before COVID. I remember always hearing negative things about UV, and rightly so. You wear sunscreen to keep UV from damaging your skin and creating cancer. But at the same time, it works as a disinfectant. People started exploring these different wavelengths of ultraviolet light. As you get to smaller wavelengths of light, you get beyond the visible spectrum and into the ultraviolet spectrum; as you move toward shorter wavelengths, you get UVA, then B, then C. All of the things we’re talking about are in what we would call the UVC range.

Now UVC has been deployed, not just in LEDs but in mercury bulbs, for disinfecting surfaces for many years. Mercury bulbs that are around the 254 nanometer wavelength are turned on in operating rooms before patients and doctors are there, to disinfect surfaces. That same wavelength now is being used in solid state devices, as LEDs.

Alyssa Vance:

This is what YouTube called the “flesh burning death lamps”.

Matthew Putman:

Yeah. That’s one way to put it, I suppose. It’s flesh burning, but it was also a bacterial and viral disinfectant, too. But that’s the accessible, fairly inexpensive to make version of UV LEDs. You really don’t want to look at it, it’ll damage your eyes. You don’t want it to hit your skin, it will damage your skin. It’s only used for surface disinfection. What I’m more interested in is a farther, deeper UVC which is in the 220, maybe under 230 nanometer range.

Alyssa Vance:

That’s a shorter wavelength?

Matthew Putman:

Yes, it’s a shorter wavelength. Farther into the UV spectrum. When you get into that part of the spectrum, you get some incredible advantages. It isn’t the first thing, what YouTube calls … What did you say? Death?

Alyssa Vance:

Flesh burning death lamp.

Matthew Putman:

Right. It’s no longer a flesh burning death lamp, it’s something that will not damage the skin or the eyes. More importantly for COVID or other airborne pathogens, it’s not designed strictly for disinfecting surfaces. The more research we see, the more that comes out, we see that the aerosols, the airborne nature of COVID are much more dangerous than what exists on surfaces. The big goal would be to produce this wavelength of UV LEDs and to have them in our normal lighting systems, so we can start getting back to life and feel safe.

Alyssa Vance:

These are LEDs? These are the semiconductor, light emitting diodes that you find in electronics and so on?

Matthew Putman:

Exactly. No different at all. You’ll remember that there have been challenges with those over the years. Somebody won the Nobel Prize for inventing the blue LED, for instance. It took a lot of time to get to a point where you could produce a good blue LED. But LEDs themselves are so ubiquitous in our life right now that we know how to make them, and we use them everywhere. The special thing about this particular wavelength is that it requires a different type of material. They’re usually aluminum nitride. There are some other possibilities, like aluminum oxide, or gallium oxide, for instance, that could do it. But aluminum nitride is the most well tested and the most used in very small environments.

But it presents some challenges to make, it’s expensive to make. However, the LED itself is something anyone would completely recognize. You would plug it in the same way.

Alyssa Vance:

LED technology has really come very far. When I was a kid, 15 years ago, I was wondering, could you make a laser gun like they have in video games? And I concluded that no, you can’t, because there’s no way you could get enough power into that small of a space, that someone could carry around with them. But there’s a guy on YouTube, Styropyro, who’s now actually done that. The LEDs have gotten so good that he took a radar gun and converted it into a laser gun, which puts out about 100 watts of light. That’s actually pretty dangerous.

Matthew Putman:

Oh my God.

Alyssa Vance:

You could totally use that as a weapon.

Matthew Putman:

That’s funny. No, it’s true that so many people underestimate it. It sounds like you didn’t. And I didn’t, but not from any sense of knowing anything. I’ve always … I remember being at the World Science Festival in its first year, oh God, this must have been 15 years ago. I was sitting at a table next to a Nobel Laureate physicist, so somebody much more educated, much smarter than me. We were talking about vertical farming. I was incredibly interested in vertical farming. I still am, actually. He was saying that this wasn’t a feasible thing to scale, because you couldn’t have LEDs that were strong enough, that had enough power. Now, that’s certainly not true.

It was one of those arguments where I said, “That’s not true.” But I had absolutely no good reason for why. I luckily turned out to be right. I know a little bit more about the production of LEDs now, I’ve spent a lot of time with that.

Alyssa Vance:

If you have these UV lights being used in everyday environments like homes and offices and transportation, obviously, they’d have to be much safer than current UVC lamps. What kind of safety testing has been done? Has someone tried putting them in a house or in a building, and been around them for eight hours a day for four or eight weeks, and found that there has been no skin burning or other ill effects?

Matthew Putman:

Yes. We can put links in your blog to some of the research. I’m not going to be able to quote too many names off the top of my head, but this isn’t a new technology. There was recently some studies done out of Columbia University that have gotten a lot of attention lately, in the last month. But this has existed for a long time, and there have been trials. The safety and efficacy of it isn’t the thing that’s in question.

Safety has been proven out, and it hasn’t been proven out just last week. This is a rather old technology by now. It just takes commitment, a small amount of investment, and the convergence of some new technologies. Some things that I’m involved with, with using closed loop AI systems, using deep reinforcement learning to be able to make corrections in the manufacturing process. Which is where my technology comes in, where things I’m involved with come in. But the thing about the safety issue is, there’s no reason anybody should take my word for it. It’s a very well known thing if you get to that wavelength.

Alyssa Vance:

What does the availability of these devices look like right now? Can you go on Amazon and buy one? Are they being produced on a large scale? Could a business go to Alibaba, and order 10,000 of them for their office?

Matthew Putman:

There are a few companies in the United States, but not for any reasonable price, and not in stock. Any single device that you would buy would be, I don’t know, somewhere around $1,000. This is not something where most people could go and spend a million dollars to take care of an entire hospital, for instance. That’s not due to the material. The material is aluminum nitride, the two most abundant things on our planet – aluminum and nitrogen are close to the top. But it’s very hard to process.

They are available, but probably not available in the quantities where, if money disappeared tomorrow, there would be enough to go around. There’s some good news here, though, it’s made with the same type of reactors that other LEDs are made with. Companies like Cree, companies like Samsung, very large companies that make LEDs could use their reactors to make these types of safe UV lamps. The production capacity is there, the supply right now is not. Because the price is so high, it doesn’t make a great business offering at this point.

Alyssa Vance:

In order to bring the price down, do we need a new technological breakthrough? Or is it just a question of building the factory at a large enough scale to make each unit cheap?

Matthew Putman:

It’s a new technology. But the technology, strangely, it’s a software technology. This is where the work that I do comes in. (When I say “I”, I mean people on my team that actually know how to write these models. If we get too deeply technical about this, you’ll lose me.) But the main point is that they’re grown in something called a MOCVD reactor, which is a vapor deposition reactor. These are very common reactors, as I said, used for any wide-bandgap material. LEDs, but also power inverters and sensors, a lot of other things that are wide-bandgap semiconductors.

You can think of this as an additive process. You’re taking a crystal, you’re growing a crystal on top of another crystal with the gases in the deposition chamber. The problem with using aluminum nitride in the type of volume that we’re talking about is that during that process, cracks and other types of defects start to appear in the crystal. Now that’s not new, that’s something that’s happened through the history of semiconductors, having defects that propagate. But this has to be grown at extremely high temperatures, and it’s very touchy.

Getting over those hurdles has been why yields have been especially low. If you have something that’s 50% yield, then you’re losing a lot of material and have to charge a lot more. We use something that we call AIPC, artificial intelligence process control. Instead of setting conditions for your tests – normally you’d set how much of the gases you want, what the temperatures should be, what the flow rates should be – we take all of the controllers that already exist on the reactors, but we let something called an RL agent, a reinforcement learning agent, watch the process.

Because it’s an additive process, when a defect starts to emerge, it can make corrections in the next part of the process in order to fix it. It’s analogous to, and uses a lot of the same types of algorithms as AlphaGo would use, to win the game of making an ideal UVC LED. Regardless of what moves were made in the middle. We assume that there will be problems. This is hard to do. But an RL agent that’s being rewarded for getting it right, and penalized for mistakes along the way, can take actions that should win the game and improve the yields.

Alyssa Vance:

How can you tell when a microscopic defect starts appearing in one of these materials?

Matthew Putman:

There’s a whole other set of tools that can be used. You’re taking sensor data all the time. There’s time series data, we’re monitoring things as they go, so you see how things change. You might see some strange dips in temperature, or some strange loss of flow. But you have these validating steps, and those could be using spectroscopy, or using microscopy. Then there’s a whole other set of AI tools that go with that, which are a bit simpler. Using things like convolutional neural networks to do classification of the defects in real time. You’re doing imaging, whether it’s through optical or other means – see a certain type of defect, notice it’s a defect, put it into a class. That system then, due to the memory being created in a series of AI operators that we run, knows historically the best ways to correct those classes of defects.

Alyssa Vance:

As we’ve seen with everything in this pandemic, even relatively simple manufacturing becomes complicated when you have to do things at an enormous scale, for all of the seven billion people who might become infected. Derek Lowe, over at In the Pipeline, recently had an article on the glass vials that are used to contain vaccines – you make the vaccine, you put it in the vial, then you ship it off to the doctor or hospital or whoever. Right now, even if there were a vaccine tomorrow, there aren’t enough glass vials to put them in. Because even though making glass vials is a very established technology, no one has a factory that’s large enough to pump out seven billion of them, or however many we’re going to need. If we did try to scale up this LED manufacturing process, so it could cover every office and factory, and stop the pandemic, what sort of bottlenecks would you run into?

Matthew Putman:

Well, this goes beyond UVC, as you mentioned. What’s been noticed, what’s been really apparent throughout the COVID crisis, is what a disaster our supply chains are. We have taken even the most simple of devices, and created a supply chain that slows things down. And it adds potential variation in the process, potential failure points, and a length of time to get things to market that is completely unnecessary with modern technology, such as the technology I mentioned before.

To build what you need, where you need it, is something that needs to be taken into account, rather than a stockpile of potentially useful things. Distributed factories… there are certainly different steps of how this could be done. You could think about converting a regular LED factory that’s already a large factory, they already have the right machines, then add on the software. There’s plenty of aluminum and nitrogen in the world, and you can make it in North Carolina. That’s one way that’s probably the simpler way to go about it, rather than to take the 50 other steps that would normally go into getting an LED.

COVID is a wonderful chance, if we don’t squander it, to try to finally converge, and make these things that people thought would need to happen one day happen now. I used to say when we started Nanotronics, that my dream was to be the next big, important company to be built in a dorm room, that  would not be a social network, but a factory. I believe that there could be big steps towards this in just this particular technology. An MOCVD reactor is a couple hundred thousand dollars. Certainly not free. The software’s another couple hundred. But for a fairly small amount of money, you could actually have a factory that is close to the size of a dorm room making quite a bit of UVC LEDs. Now that’s not going to happen tomorrow. It could, though.

You have to first of all ask, “What is physically possible?” “What is proven out to be an engineering possibility?” That could happen. What’s more likely to happen, and should happen, is that all of those reactors in the world that are making other LEDs could make the UVC LEDs, and could do it in a distributed way. New factories should be funded, new companies should be funded. It’s an underserved space right now that has the potential to be ubiquitous. There’s a great opportunity.

We even get discouraged by the fact that there isn’t anything in our lives that we can imagine making, without having 100 different companies and suppliers build it.

Alyssa Vance:

If all of the factories that are using these reactors to make light bulbs and electronics, and all sorts of other components, switched to using them to make UVC LEDs, that would be enough to supply everyone?

Matthew Putman:

For sure. I mean, it would be a fraction of that. The same reactors build 15 different chips, in the chip set of an iPhone. You wouldn’t expect them to convert everything, but you could make profitable businesses out of each of those reactors, and then buy more reactors. Certainly, the capacity is out there right now.

Alyssa Vance:

That’s what they did during World War Two.

Matthew Putman:

Yeah.

Alyssa Vance:

A lot of consumer production simply stopped, and they said, “For the duration of the war, we will make no cars, we will make no bicycles, we will make no typewriters, we switched it all over to war materiel.”

Matthew Putman:

Right. I’m trying to figure out if that was a different will of the people, an ability to sacrifice to fight, or knowing that it was something different to fight for than we have right now. Or if people are intimidated by the technology that we’re discussing. I did an interview with Stephen Wolfram a couple weeks ago, on a podcast that I have. At the end of the podcast, he started asking about AIPC and Nanotronics – he was saying that an iPhone, isn’t it so complicated that even with all of the money that Apple has, if they wanted to make an entire iPhone in the United States or in one factory, it couldn’t be done?

I challenged that idea. I think that if Apple took a war-like switching things over mentality, like you were talking about in World War Two, it’s not a technological challenge. It’s just being able to say yes, this is important and should be done. But it’s talked about so little that some of the smartest people in the world – like Wolfram who I respect, I think he’s one of the most inspiring, intelligent people around – didn’t know that.

Alyssa Vance:

I read on your Medium post that you tried to do a pilot project with the MTA (which runs the New York subways), but it hadn’t gotten launched yet. The MTA, in transit circles, is famous for being this big government agency that overspends, and has projects come in years late, and so on. If you think about doing a pilot project with some other company or agency, the first thing that comes to mind is those meat packing plants. Because they’re big centers for infection, they have lots of people packed close together. It’s hard to shut them down, because then no one could eat meat. And you can’t work from home, so there isn’t much alternative.

Matthew Putman:

Excellent idea. I love this conversation, because yeah, I’ve thought about partnering with airports, I’ve thought about partnering with clinics… meat packing is an excellent idea. And you’re also right that when you think about big government – every prejudice I had had dealing with big government, as being difficult, bureaucratic, overspending… every prejudice I had turned out to be true. I think, certainly, the MTA isn’t alone. But yeah, that’s a great idea. I will start searching that out, thank you.

Alyssa Vance:

Thank you.

Matthew Putman:

For the biz dev opportunity.

Alyssa Vance:

Back when the pandemic was getting rolling during February and March, I had in my head that… Trump wanted to deny it for a long time, but I thought that the pandemic would be so bad, there’d be so many deaths, so many hospitalizations, that he’d essentially be forced to let someone like Scott Gottlieb come in and establish a sane policy. I don’t think that’s happened yet, and I don’t see any good prospects for it happening soon. Have you thought about going outside the US and working with different national governments, like Taiwan and New Zealand and other countries that have handled it better?

Matthew Putman:

Yes, yes I have. And there’s no reason to be nationalistic about saving peoples’ lives. Whoever will do it and support it, we should be working with. Yes, I’m speaking to other governments and large companies in other countries. I feel like it’s a shame and a wasted opportunity if the United States continues to have this embarrassing, heart-breaking, frustrating rise in infections. Policy and investment throughout many institutions has been very poor, and continues to be. So yes, I am speaking to other places. This isn’t a war against another nation we’re dealing with, so we all benefit from it.

Alyssa Vance:

Where should someone go if they want to read more about this technology? Maybe there’s someone who has worked with LEDs in the past, who might be interested in getting one of these factories set up?

Matthew Putman:

In the blog post that I wrote, I have this link to reference this new work coming out of Columbia. But there’s three different US-based companies that I’ve been working with that do the technology. There’s three university programs I work with. Yeah, there’s research that goes back quite a ways. From Santa Barbara to Korea. So this is work that has been done quite a bit. We’re publishing a paper with a company in North Carolina where we did some pilot demonstrations, too.

Alyssa Vance:

You said that you’ve been – in general, not just for COVID – excited about manufacturing, and building new manufacturing companies, in the same way that we’ve had Facebook and Twitter. What are some of the opportunities in manufacturing that you’re most excited about?

Matthew Putman:

Yeah. I do want to point out that I’ve gone through these waves of being incredibly optimistic that the urgency of COVID will take some of the technologies that have been waiting, and push them out into the world – being very optimistic about it to being disappointed, like with this government story about UVC. I think there are tentacles coming out of all of these things. If you think of wide-bandgap materials, once you’re making UVC LEDs in this way, you’re also making new photovoltaics in this way, or you’re making new types of packaged power devices.

But for COVID specifically, we’re working with the largest maker of genome sequencing equipment. Then throughout their supply chain, we’re able to increase yields of genome sequencing to the point where it becomes extremely inexpensive to do – and then you can design not just vaccines, but therapeutics, and eventually individually tailored ones. The road to personalized medicine can be shortened by decades, but the will and the financing go into it because of COVID. That’s something that really excited me. I consider that manufacturing, and all these things are building something.

Alyssa Vance:

So that’s working with gene sequencers?

Matthew Putman:

Yeah. And their supply chain, the vaccine makers that they work with. To improve yields, make things less expensive. For vaccine research, and from testing through vaccine research and therapeutics, especially when you’re dealing with these RNA vaccine delivery systems.

Alyssa Vance:

If you look at light manufacturing – the types of things that you’d find in a Walmart, like clothes and electronics – we’ve seen huge decreases in price, and increases in efficiency. Most of the things in a Walmart are cheap. Even an ordinary person can buy most of them with an hour of labor, two hours of labor. And many people now have too much stuff, rather than a shortage of stuff. But a lot of things in heavier manufacturing remain very expensive. Like airplanes are obviously very expensive, skyscrapers are expensive, bridges are expensive. These things still cost hundreds of millions of dollars. Do you have any thoughts about how you might use technology to make these large manufacturing systems more affordable?

Matthew Putman:

You point this out, the commodification of these things – there is real ubiquity and affordability of these things, and when I was a kid, there wouldn’t have been. None of those things are novel – so you take an airplane that’s just like an airplane that we currently have, but make it lighter and more fuel efficient, that’s generally just improving material manufacturing. It’s easier to improve material, injection molding of plastics. The same thing can be true for doing a layup of carbon fiber, or to actually incorporate new types of nano fillers. That’s not where it gets incredibly exciting, though.

New battery technology is very interesting and exciting, including solid state batteries.  Making things less expensive, I think that you get this and you’ve already pointed it out, it’s not always linked in people’s minds to… lower prices with things that are radically better and exponentially better, yeah. I don’t think it’s exactly intuitive to people, even though it should be. Being that we don’t see it.

The way that we can build things, by using techniques like I discussed earlier, is by reducing the length of these supply chains. By being able to take the software that was developed on other platforms, on the platforms that we need to improve. Funny enough, you can take an NVIDIA GPU, where deep learning models have been created and are being deployed, and use those to make the models that were created for the application, and use those to make the next generation chips. Then you’re going to start seeing something that’s really, radically different. The type of change that we would expect and want.

Alyssa Vance:

In terms of novel technology, what are some things that you might buy that aren’t being manufactured, or aren’t available on the market yet? What do you imagine in the glorious transhumanist future?

Matthew Putman:

It’s funny because we can now look at our daily lives, and see where the same failures have occurred, and use our imaginations in the way that probably you and I always have been. We see the failures in the medical establishment. We don’t have any type of personalized medicine, and that could be either completely biochemically based, but more usually with some type of nano-controllers that can seek out pathogens or can bind to different DNA sites. The idea of having telemedicine needs to turn from talking to a doctor over Zoom, to looking at your own genome and printing out a therapeutic for you right at that time, and just having your doctor consult with you if you have any questions.

You know, that’s basic. These are things that we would all love right now, and are much more possible than people think. There will be something fairly amazing, if any of these new types of vaccines work in a fairly short amount of time. I think that there will be a renewed hope that things are possible. Anyway, that’s one of them. Not to be old fashioned about this, but I do think that, in nanotechnology, it’s still where you gather the greatest amount of abundance, and abundance meaning abundance of life as well. I focus on miniaturization, not just of a chip, but a miniaturization of a factory. Then I leave it to your imagination. And everybody else’s imagination to build what we want to build.

Alyssa Vance:

I just ordered a whole genome sequencing, I sent off the tube last week. When I get the data back, do you have any ideas on what I should do with it?

Matthew Putman:

I have a feeling that actually you, personally, and a group of friends could figure out how to combine that with your own medical records and write some AI algorithms around it. But I don’t know. That’s really problematic, that I even hesitated, right? Isn’t that crazy that you even asked the question, and that I don’t have an answer to that? If I would’ve thought that you and I can just get a full genome sequence as easily as we can, and we’re not sure what to do with it? It’s amazing to me. I have no great insight.

Alyssa Vance:

I can do all kinds of studies, but I need to have more than one. Like, one genotype and one phenotype by itself doesn’t get you very much.

Matthew Putman:

I know. I mean, that’s why I’m an old fashioned factory guy who just wants to make genome sequencing so cheap that everybody has their full genome sequence, and then you’ll have plenty of data and everything to work with. I’ll focus on that goal. Because this is just like building a computer and having applications on top of it. There’ll be so many people with applications. Let’s make sure that it’s nearly free for everybody to get their genome sequence.

Alyssa Vance:

Cool. That’s all the questions I had, is there anything else that you want to add?

Matthew Putman:

No, thank you. Was this okay for you?

Alyssa Vance:

Yes. This was awesome, thanks for doing this. 

Perfume Quiz

A friend of mine wrote a quiz to help people find the best types of perfume for them. It’s much more detailed and creative than you typically see for online quizzes:

https://uquiz.com/3wN7HU