Why OpenAI Will Become an Infrastructure Play

Harry, no. It is so great to be here. Thanks for having me on. You know, the whole world only makes sense going backwards because, like, who would have guessed from that meeting, you’d become one of my largest investors, like, five years later. Just incredible what you’ve done.

Oh, man. So I loved building things since I was little, and I was completely obsessed with Legos. And so my dream was honestly, literally, to be a Lego master builder until my mom did some research, and she found out that actually, like, it’s not that great of a career. They’re paid something like minimum wage, and that was in middle school. And so I forget if it was that Christmas or the next year, but she gave me a programming book for Christmas, and I was and that was the end of the story.

I was like, okay, computers are next.

Yeah. The biggest lesson I learned was empathy, honestly. So when you’re in consumer software, you can’t possibly begin to understand how many different people, personas, use cases, mindsets, the human experience all comes to bear on your product. And what I saw was actually a trap. So the product managers who were super data driven started designing and building features for the average user, because that’s what the data told them. And they actually believed that there was something such as an average Twitter user. It’s such a huge mistake.

Right? Like, you’re conflating all of these different populations. You have sports fans who want a live, like, chronological timeline during the game. You have celebrities who want to maximize their reach. You have Japanese users who, by and large, want to remain anonymous. None of them is average. And so what I really learned is you have to deeply understand each population and design and build a feature for them. Don’t

build it for yourself. Like you deeply understand the problem, that gives you superpowers in terms of solving it the right way. You definitely need to understand, are you repeated by many other people around the world? Do they share your problem? Do they share how you think it should be solved? But I think it’s so much easier to build something that you personally feel than have to sort of try to interpret other people’s thoughts and beliefs about it. So no, I would not be worried about getting too emotionally involved.

I think that’s a superpower.

it is too easy for companies to fall into like, oh, we don’t know. We need more data. We need to run a survey for that. Particularly at Twitter, we need to get stat sig data, which always took two to three weeks. And then you would have to analyze that, and then see what happened. It’s honestly a disaster. And so agree or disagree with some of Elon’s decisions, but he is moving quickly in a direction that is way better than sort of standing still.

but honestly pure execution. People know the vast majority of managers are terrible managers. Right? The vast majority of founders are simply bad at running companies. And and I’m sorry, but it’s true. And so what I mean by that is like most founders aren’t intentional about how they go through and operate the business. Intentional with their time, with their decisions, with who they hire, with what they say no to. And so if you don’t have conviction, you’re really gonna struggle as a founder because you need this like deep seated obsession of what’s right and what’s wrong and what you believe in, and how that informs every decision you make.

And your decisions may still be right or wrong, they’re not gonna be perfect. But if you were intentional about them, at least you can trace that back and learn from it, versus I see too many founders just sort of going on a random walk, and then when it turns out they were wrong, what do they have to learn?

into management because they were good at a former job. Their passion, right, their experience isn’t managing. The the feedback cycle is slow. They’re the boss, so they don’t get the raw feedback. I think there’s a ton of challenges, and it’s even worse for CEOs. Because who in your company is gonna give you really crisp, blunt feedback on what you did right and wrong? And your investors aren’t involved enough in the day to day to really know. So they can give strategy advice, but they don’t know how you’re behaving in meetings.

a great question. So actually, every time I’ve promoted an IC to a manager in a startup, this was not a Twitter, we’ve done it as sort of a trial period. And so it’s like, hey, so and so, we have this opening for this new role, the team’s growing, we need some structure. We are going to have so and so take on the role as a trial over the next two months, and let’s see how they do. And honestly, there’s been both outcomes. In some circumstances, it’s been great, and everyone’s rallied around them, and it’s like, great.

Okay. Now they’re the manager for that team. And I’ve had circumstances where they haven’t, and it was sort of widely recognized that they weren’t excelling in that role, and we decided to move them back to an IC. And that was okay as well. I think there’s a a really bad perception that being a manager is better than being an IC. I think it is different. And you can be an exceptional IC, and you should be comped appropriately for that. Or if your career passion is to mentor and guide folks, then you move into management.

I have felt strongly in for a long, long time. Google sort of pioneered this well in the early days and created this whole track for engineers to sort of keep climbing in in comp and title and recognition and so on without taking on management roles. And I’ve tried to mimic that at all my companies.

Yeah. It’s certainly not easy, because you can constantly fall into a trap of thinking you’re getting feedback and you’re not. It’s really how you set the culture of the company. So one of the things we do at Digits is we run the entire company on a weekly sprint. As part of that, every Friday, we end every week with a full team retro. And we call it anchors and breezes. Anchors are what slowed you down, what didn’t go well, what you needed help with, like feedback on the week.

And then breezes are what went well, shout outs to people who helped you, things you learned, etcetera, etcetera. And you create this culture of just constant iterative improvement, which then allows sort of feedback conversations and one on ones and so on to be widely recognized by the company. It’s like, that’s what we want. The whole mindset is just how do we get 1% better each week. I love the idea, but when you get to a 100 people, does that still work? Yeah. It fractalizes. So what happens is each team will run their retro on Friday, and then surface sort of the highlights, like the biggest anchors or breezes to the full company wide retro.

Everyone has sort of two opportunities. You do a team wide thing, and then you do your own small team,

also it’s important to do it correctly. So I agree with your mentality. Crashlytics, I never thought was, like, successful in any one moment. Not when we were acquired, not when we hit a billion MAU, etcetera etcetera. Like, there’s always the bigger goal. But if you have that mentality with the team, it’s very demotivating. What are we trying to go to? Like when are we gonna get somewhere? And so it’s really important to celebrate small wins. And so we use this Friday show and tell we call it basically to show off what we did each week, and champion who did what, and celebrate all the small wins of the week, so people feel really connected to the company and what’s happening.

really came out of the Crashlytics journey. And so, you know, we got very lucky with market timing. We scaled from zero to 300,000,000 phones in twelve months, got acquired by Twitter. Today, Crashlytics is on five or 6,000,000,000 MAU, roughly every active smartphone on earth. It’s incredible. Through that journey, I was struck by this dichotomy. On the product side, you have real time analytics, performance monitoring, live dashboards. Right? Like, knew exactly what was going on with the product and who was using it. And then on the finance side, I literally had a black and white PDF of my p and l and balance sheet once a month, two to three weeks late, that I didn’t understand because I didn’t have a background in finance.

I was an engineer. And so I was like, what is happening? And so literally that is why I started Digits. The simple premise, can we make accounting real time and intuitive for startup founders? And so what’s crazy is it took us five years, but we finally just launched it. Like, it’s actually here five years later.

quite a journey. Obviously, trying to make accounting real time is a lot easier said than done. When we started the company, we went heads down on r and d and really struggled with data quality for, three years. And that was because in 2018 when we started, the tech to really automate bookkeeping didn’t fully exist. I think I was a little optimistic on how it could work. And so we have dozens of patents on it now, but it was like a brick wall. So in 2021, we made the decision to pivot from like the pure bookkeeping automation to collaboration tools.

So better financial reporting, better client portals, better transaction review process. And that worked. We got a thousand accounting firms on the product, 5,000 downstream businesses, like that was sort of off and running. But what bugged me is that wasn’t really why we started the company. We had bigger ambitions. And so then last year, literally all of a sudden, GPT three comes out, ChatGPT comes out, GPT four, and we started experimenting and we’re like, woah, hold on, we’re back. Like, we can actually do what we set out to do.

Literally overnight, just like, we’re back focused on this and spent this whole year building it.

million dollar question. I’d say it’s more of an art than a science. Like, you need to have a feel and an instinct as the founder of like, do you see a path to success? If sort of the window’s closing on your path to success with your current business, like, that’s to me when you have to go pivot. And a lot of super successful companies were hard pivots. Right? Like, Twitter was a podcasting startup. Slack was a game. YouTube was a dating website. It’s totally crazy. To me, it’s like impossible to say like, oh no, Stop.

Like, that pivot’s too far afield. You have to, like, have this sort of founder instinct, and that’s how great companies come to be. The one thing I’d modulate that with is like, you need at least a year of cash left, because if you don’t have a year of cash, you’re not gonna have time to see this pivot through. And so it’s really like founder grit and enough cash. If those aren’t there, return your capital. If those are there, I would go for it. That’s how like huge opportunities come about.

It is not easy, but I think it’s rare that you would have more than twelve months of cash. Because like usually you raise to have eighteen months, and so by the time you figure out it’s not going well and you need to pivot, it’s twelve. The big thing I would change from what you said, is it’s not an experiment. At no point were we like, we’re gonna run two or three experiments. It is pivoting on a dime. You are all in on the new direction, and that is the only thing that

team on board. If your team loses trust in you, you literally have no one to pivot, so it doesn’t matter. They can really sense the uncertainty. My other advice, as I’ve said, is like be very intentional and very decisive. And so both times we’ve pivoted Digits. I gathered our core leadership team, laid out like what are the challenges, what am I seeing, what are the options. We knew within twenty four hours what the new path was, and what the priorities were. We were all in on that new direction.

It comes back to conviction. It is still a bet, but it’s like, here is the information we have on the field. We need to make a decision right now, because what kills companies is uncertainty. And if you sort of muddle your priorities and have one team try this, and another team try this, no one’s heart is in it, and both are gonna be mediocre. I would rather see founders take like one to the moon bet on one new direction, and it either works or doesn’t. Do you agree

you. I think this is the hallmark of great founders, is you need to be able to convince your team and have the trust of your team to go all in on a new direction. Disagreeing and committing in that scenario is like, okay, you might as well step back and like, let’s just have a smaller team and really focus on this, because it’s not gonna be productive.

this experiment thing, honestly. A bunch of angel investments I’ve made have tried to pivot, but I don’t think they went all in on it. I think they saw it as a flyer that they’d try for a few months, and they didn’t see it as life or death. And by the time they realized it wasn’t really working, it was life or death because they didn’t have much cash left. And so you really need the conviction, like, day of to just sprint towards the new direction.

A 100%. And so, yeah, next to your team is obviously keep the investors informed and up to date, particularly your board. We’re lucky to work with Peter Fenton at Benchmark. His clairvoyance on like where the large opportunity is, and just relentlessly pushing us to find that and adjust like on a dime, is super impressive, and I think hard to do. So definitely like keep your board tightly in the loop on this.

The power of really deep intuition and conviction, looking at a market from a very theoretical level. So one of the most interesting aspects when he originally agreed to do our a round, I sort of asked him why afterwards, why he committed so quickly. And he said, well, it it had flashbacks to Uber, because when Uber was going against the taxi industry, the NPS scores on taxis was so bad. Even if Uber was mediocre, it would still be way better. And he said accounting gave him the exact same vibes.

The status quo is just so bad that if you can make accounting like somewhat enjoyable, it doesn’t even need to be delightful, you’ve already won. And so his ability to distill these markets into these like very high level crisp understandable talking points is super impressive.

Yeah. This is such a special moment, and it I’d say it vividly reminds me of the rise of mobile, which we wrote in Crashlytics from 2010 to 2013, the web two o transition in 2005 to 2007. It’s like these platform shifts are so rare, and I love them because that’s where all the massive opportunities arise. And so the key though, as a startup founder, is to be in a position to rapidly adapt to that new reality, and you need to be faster than your competition. So like, for five years now, I’ve run Digits on a weekly sprint.

Every week we get to decide what our direction is that week. And I have to say that’s been critical this year, keeping up with all of the changes and evolution of the tech. No. I’m I’m not particularly afraid of it. I love the energy of the tech world moving at a huge pace. And so the key thing though is this is technology. Like, we are viewing it as a tool. It’s not life or death for the company. It’s like databases and so on. It’s just evolving faster.

Let’s see how we use it, and it gives us more capability, but you ultimately need to stay focused on your customer and what problem you’re

I certainly think we will. And this may not be a popular position. Obviously, OpenAI is charging ahead, of leading the way right now. I think the market forces at work mean there’s just immense energy to have an open source equivalent. Meta appears to be highly motivated to open source its work. Many folks want to run these themselves and tune them themselves and so on. That is hard and expensive today, but I can’t think of another thing in time in history where something hard and expensive in tech has lasted all that long.

It’s going to be commoditized.

if it’s different. The so the closed may win in terms of having the most advanced model, but I think the Apple versus Android comparison is exactly accurate. So you’re gonna have something proprietary that might be best because it can be fully vertically integrated, they control all the different variables. But I think there’s gonna be an open source equivalent or more open equivalent that’s a very close second, And for many people and for many use cases, it’s just as good. So what I’m really excited about is I can’t wait to see who becomes the android to OpenAI.

Like, who is clearly the, like, solidifies the second tier.

good question. So we built our ML team three years ago. We’re training our own in house models. You’re right, like you can’t hallucinate in finance. We’ve done a ton of work to make sure our math is always accurate. It’ll be really interesting to see. Every model has different strengths. Like if you already look at BART and versus GPT and so on and so on, my sense is there’ll be a very common popular open source sort of base LLM, and then tools to allow folks to fine tune it easily.

What we’ve discovered is while it create it takes so much time, data, energy, money to train an LLM, fine tuning it can actually be done relatively straightforwardly with a surprisingly small amount of data as long as your data is very high quality and focused. And so imagine you basically have your sort of default Linux operating system. Right? And then every everyone customizes their flavor of it for their product market use case, whatever it might be.

LLM layer, the data size so far has been very correlated with performance. And so, right, the bigger the models, the more data, the more parameters, etcetera, the better they do. Now there’s starting to be this counter push of like, okay, can we compress them? Can we pull that back? Like, how do we maintain the performance improvements without the size? So I think that’s a super interesting part of r and d. What I’m talking about is sort of the next tier of how do you fine tune the models, and that’s where actually I think the quality of data is most important.

Right? They’ve done an amazing job. Like, yes, they’re fast, but instead of pushing the bounds on pure compute, they’re pushing the bounds on energy efficiency, which for Apple’s use case is critical. And so I think there will be a lot of really interesting r and d on how do you make these models maybe smaller and perform it in certain use cases.

Yeah. This is the real problem you’re hitting on. So startups are gonna get killed because they’re very thin wrappers, and I agree with you. Most of what I’m seeing on the angel side right now are these very thin wrappers on top of OpenAI. If your primary product value is is scripting GPT, that’s a thin wrapper. If you’ve built it in two to four weeks, that’s a thin wrapper. The key thing to me is like we are in a hype cycle around AI, much like potentially the hype cycle with crypto and other tech before it.

All of these sort of fake use cases are gonna get quickly washed out and replaced and commoditized. It’s really important to me that people view this as a technology. Like your MySQL database. MySQL was very popular twenty years ago. Right? Like, was super cool what it could do. It’s still cool, but if you try to go raise money and you’ve built a form on top of MySQL, you’re not gonna be successful. In five years, that’s what a lot of these are gonna look like. You need to really focus on the market and, like, solving a core problem.

more startups. The the one thing I would say is I view OpenAI probably evolving more into an infrastructure company like AWS. They will host these models, they’ll allow you to fine tune them, they’ll allow you they’ll give you all these base capabilities like you get with EC two and S three and so on and so on. The big companies, the big incumbents that will be able to leverage that tech, I would doubt if OpenAI goes and builds like a Notion competitor or an HR or Salesforce competitor or so on.

They probably wanna stay at the more generic level. From the startup side, a lot of startups are getting killed. It’s funny. I I use the term Sherlocked. I’m an old school Mac programmer. Back in 2002, Apple killed Watson with its Sherlock tool, and so the name sort of stuck. There’s a lot of companies getting Sherlocked because they’re pretty incremental, and they’re filling gaps in OpenAI’s current product without realizing that like, yes, they’re just on the road map, they haven’t gotten there yet.

And so if you’re working on a use case that’s pretty horizontal, that like OpenAI is going to need to solve within five years in order to scale, that’s not a great investment, and that’s not a good use of your time as a founder.

of course, is super focused on privacy. They don’t want your data to leave the device. The only way to do that with AI is if you can fit a machine learning model on the device and keep all the data there. I bet Apple can and will. And so if you project forward five years, if they get to the point where they can run a sufficiently large LLM on your iPhone, then OpenAI is out of the picture. You don’t you don’t even need to hit their servers.

It’s just on your phone. I hadn’t

This is a really good question. So they had the exact same reaction to cloud. If you go back ten years, they were like, I would never put stuff on AWS or Google Cloud or Azure or whatever. That’s ridiculous. Why would I share my data with those companies? Right? Now it’s just not only do they all do it, but it’s actually better because those companies’ core competency is running data centers. Most enterprises have no idea how to operate a data center. I think this will go in the same direction.

You’ll have very clear guidelines around how the companies use the data for model training, and it’s off limits, and so on, and sort of that trust will be overcome. The other angle though is there’s also the danger of every enterprise jumping on AI because it’s hot and cool. They all jumped on blockchain for zero reason even though it did nothing. And like IBM’s at fault. IBM was consulting, charging for services to consult on how to adopt blockchain into your enterprise. That’s ridiculous. Again, me, focus on your customer, your market, your product need, and view this as a tool, not a panacea.

And adopt it strategically on like what makes sense and where it’s gonna push the product forward.

right in that it’s a large market. To me, it’s not a very interesting market. Like, yes, there’ll be a lot of consulting to help enterprises adopt AI, and the products won’t be that great, and they won’t really make that much of a difference. What I see when you come to platform shifts is, again, this opportunity for sort of a net new approach to really take over fast. And what gets me excited is like, let’s build real workflow automation for real people in massive industries that are outdated.

Like, for example, accounting. Like, are you gonna trust Intuit to adopt this and totally disrupt their own product lines? Or do you think a new upstart’s gonna come in and do it better? And that’s where I’m excited to see and, like, pour energy into.

faster for a couple reasons. So if you look back at mobile, so the iPhone came out in 2007, they opened up the App Store in 2009. By 2011 to 2012, a lot of people were using and building apps. And then enterprise adoption even lagged from there. So call it five to seven years. With AI, there’s no new hardware to buy. So you don’t need this huge purchase price, right, locking out enterprises and people all around the world. You can just instantly benefit from it online, and there’s no new UX pattern to get familiar with.

Right? You don’t need to be used to carrying something around in your pocket, looking at a little screen, squinting at reading things. There are chatbots. It’s a very fluid interface. It does things for you. That makes sense. And so I actually think the adoption curve here will be radically faster, and industries will be disrupted probably way quicker than prior tech waves, just because of the barriers are so low.

do

They need to go all in on it. I don’t think they have a choice. I I agree with you. I think it’s existential for them. Because if AI replaces search, their golden goose has been killed. It is way more effective to kill your own golden goose than let than watch someone else do it. And again, I mean, going back to Apple, it reminds me of the iPod Nano. Apple killed their most popular product, actively killed it, because they knew there was better tech coming. And I think Google needs to get bold and do the same.

What has been interesting to see is it’s very clear the top models are memory bound as well as CPU bound. So you can’t just make the CPUs, the GPUs faster. You need to increase memory bandwidth on par with that. And OpenAI actually shared a tech talk a few weeks ago that talks about how they were tuning their data centers like this. So it’s gonna be new challenges for NVIDIA, for ARM, for these chip companies to really unpack, I would bet on the pace of technology. It will get a lot faster and a lot cheaper very, very fast.

I very much see AI as a tool, not a product. And so it’s a it’s a technology. It’s like your database. It’s like memcache back in the day. And so because of that, I don’t think it’ll change how people price in specific industries. Like, if your market does Percy pricing, that’ll probably stay. If your business and product does consumption pricing, that’ll probably stay. And you’ll have to work that into how you use the AI. I think it’ll be commoditized and seen

It is extremely challenging, and what’s interesting is the counter reaction, because you’re seeing Reddit, Twitter, etcetera shut off APIs, put in more strict rate limits, etcetera etcetera. And so the whole world is starting to lock down data, which was counter to the trends over the past twenty years when everything was being pushed more and more open and API accessible and so on. And so I think there’s been a clear realization that the data is valuable, and that’s one of the things, like, we’ve been really focused on at Digits is we have a proprietary dataset of a 100,000,000 financial transactions.

And that’s what we can train on and make sure our finance and bookkeeping AIs know what they’re doing. So I do think the data is really, really important.

do. And this

usually sort of broadly captured in the terms of service, having access to your data to improve the product. And so we do not share any of that data externally. We train our own models internally on the data, but that does allow us to improve the product for you and make your accounting better.

for a moment because it’s not entirely a joke. So Google Photos, potentially one of the most strategic products ever launched, because it allowed Google to collect the world’s largest library of photos ever assembled. And that’s how they were able to train their early vision

on.

interesting. I would be very nervous about the OpenAI Azure partnership. And of course, Meta just made a big deal about partnering with Microsoft as well. So I think you’re right. I would look to aggressively move into the space and acquire something to bolster AWS.

think Google’s by far the most vulnerable. Because, again, their business model is pretty binary. Right? Search is all their revenue, and so if that gets damaged, they’re in a huge problem. And they’ve been slow to react. They combined two different MLAI teams. They’ve just punted Gemini into q one, which tells me it’s not doing very well. So I would be nervous.

They really are. And one of the funnier ones to me is Dropbox. Dropbox has a built in AI, and I don’t know. I mean, I I’m sorry, Drew, but I I just want Dropbox to store the files. So I I think there is a bit of a sort of bandwagon ride the hype wave aspect. Dropbox Dash. What is fun though is obviously it’s a lot of experimentation. So all these companies are trying things out and seeing what works and what sticks. And so the industry is gonna learn a ton over the next twelve to eighteen months on, like, where it’s appropriate to use AI and where it’s sort of just a useless add on.

that always the case, though? Has there been a period where

have some data for you. I’m more than happy to share this publicly. So since 2014, I have angel invested in 97 startups, and I I just did the count. So far, 30 have failed outright, about a third. Another 19 are still at one x, so basically haven’t gone anywhere. And if you look at the overall portfolio, really it’s like 10 of them matter. But what’s crazy to me is, again, twenty fourteen, nine years of data, still the vast majority of the gains are on paper, and it depends how these tend to on how successful the portfolio is.

No. Very few have returned. A handful gave cash back, but or are you talking about on paper? No. I’m saying my actual cash back, like DPI. Oh, very, very few. Yeah. The the time range on seed investing on the angel side is just is like a decade plus. Okay. So we have that. In terms of the top 10, do you trust the book values? I do not. No. And I think a lot of them are basically holding at their 2021 valuation whenever the last round they raised was, and it’s nowhere close to the reality.

I do get data from the secondary markets, and so I can see with some of them, like, they’re trading versus where their last preferred round was, it’s a bloodbath. It’s, like, down 80% for many of them. So I think it’ll be really interesting.

founder who’s sitting on a a price that’s just untenable? That is a really tough position to be in. And I think you you don’t wanna get there. Like, my advice has always been be really disciplined about each round, and what you give away, and terms, and so on. If you’re in one of these boats, I would look to like recap if you can, really focus on growth, trim expenses, length and runway. It’s gonna be big shoes to build into.

actually. So I’ve done secondaries over the time, and that’s actually been my most successful outcome so far was via secondary. It’s like waiting for them to sell. I’ve unfortunately had the opposite happen where a couple of my early investments went on to IPO, and so were extremely successful. But then during the six month lockup window, went almost to zero, and I couldn’t exit almost anything. I’ve actually had more success selling on the secondary market pre IPO than I have had actually waiting the whole time.

it is crazy. I I mean, I understand why, but I don’t understand why small time angel investors should be locked

Yeah. Big lesson learned is no matter how great you think the company is, how great you think the market is, how great you think the founder is, it is still damn hard. The odds of success are very low, and you can’t get too cocky. So on two of them, actually two of my biggest failures, I was so convinced. I was like, this is a no brainer, this is gonna be a huge home run. I’m gonna put in way more than I usually do. Usually, try to stay pretty disciplined on check size.

Both of them went to zero. What can you Okay. Do about So what gave you the confidence in those companies? It was seemed to be a very impressive founder. To me, a very obvious market. I won’t name their names, but I was like, hey, just like solid execution here should be a clear good outcome. And there’s just too many variables at play over the course of the startup journey. And you don’t know everything, like as an angel you don’t do that much due diligence, so it’s hard to fully understand.

And so my advice would be just like be very disciplined. It’s like do a bunch of deals because you need a portfolio, and put the same amount in every time and average it out.

advice. Yes. Totally agree. Yeah. On the consumer side, I’d say real traction on enterprise software, okay. Potentially more sustainable.

Oh, man. So okay. So this is all true. This was ten years ago. A college friend of mine was prototyping what was gonna be a new teenage social network, and I invested 10 k. I was like, the guy’s smart. Let’s see what happens. He eventually pivoted that into down to lunch, which did well for a bit and then failed unfortunately. Then he pivoted that into crypto, but not the coins. He started and launched Alchemy, which was like an Ethereum dev platform dev tool thing. And at the height of the Bitcoin boom, I exited on the secondary market for 200 x.

Holy shit. Lesson learned there, nothing. It’s all about a founder’s grit and mentality, and you can’t back the early idea and think that that’s gonna be it. Did

Yes. So that is why on paper, and like and and in terms of cash returned, I’m actually in pretty good shape. But, yeah, it’s remarkable how how the outliers outclass the rest.

Yes. And getting in very early at a very low valuation. That’s another great lesson learned for folks actually. So I had the opportunity to take some money off the table in a deal. So I got a three x return. Right? Returned cash in the bank, three x return. On paper, it’s gone on to do another 10 to 15 x, but I’m worried that valuation’s fake. I don’t think it’ll ever return the remainder. So I only sold some of my shares, but that might be the only cash I get out of the deal.

Great question. I so I am biased obviously by the founder’s side, but I think it’s very scenario dependent. So if the founder is still like excited, the team is there, they have grit, like, I don’t think you can ask for cash back. I think, like, you wanna bet on the founder? Like, let’s see where it goes. If the founder’s clearly wavering, if there’s performance concerns, if there’s been a pattern of bad decisions, then maybe there’s a a clear scenario and case for it. But in general, I would bias towards supporting the founder.

No. I I think a lot of valuations will still crash from here dramatically. And there’s probably gonna be huge layoffs going into q one, q two as well. What’ll be really interesting is the number of companies that raised and have sort of struggled through 2023, but now are down to six months or so of cash. I think next year could be very tough because they’re not all gonna be able to raise follow on rounds.

Yeah. This is a great question. Because a lot of folks, you’re right, are trapped in companies with high valuations where their options are likely underwater. Yeah. And But then it is safer than just starting something new? Right. And so it it probably is safer than starting something new, but it’s not safer than switching to a earlier stage sort of growing company where you’re gonna have real options and a real impact. I actually love sort of down markets, because I think in the very hot markets, it’s too easy to raise capital.

And you peel off all the sort of like fantastic second engineers, or product managers, or designers, or whoever they might be, and they go get funding and start something, and that actually blurs, it stretches the talent density across the entire ecosystem. Versus in down markets, can build higher talent density because it’s harder to

the problem. You’re right. And that’s probably the counterforce is all of these big companies who raised huge rounds in 2021 have high salaries. If you switch, you’ll get more equity but way less cash. And so that’s probably keeping folks where they are.

man. No. I don’t have a good of an insight as you. I’d say looking at my portfolio, it’s not even necessarily the most techy founders that are successful, but the most sort of customer obsessed and just like really deeply personally understand the market and really wanna solve it. And usually have some pattern in their background like their parents were in a sort of adjacent space or something where they’ve just felt this personal identity come to bear on what they’re doing. And I think that gives you an extra push, an extra drive, an extra grit to make it successful.

change is less than 10 out. What do you mean runaway climate change? Like, just a self building cycle of higher temperatures and catastrophic storms and droughts and everything. And so we just accept this new reality? No. I think we’re too late. And like, I funded climate change films fifteen years ago. It’s still shocking to me that this is not like the singular top priority in any political campaign in the entire world. But

Yeah. I don’t I I don’t think you can’t accept it. It’s going to happen. And so the real question is what are we doing about it, and which countries is it gonna completely impact way more than others? So it’ll be really challenging.

Yes. The impact is unfairly distributed and the cause is unfairly distributed. That’s why it’s basically a tragedy of the commons. And so economically, it’s like, how do you motivate that on a global scale?

And they haven’t had the time to catch up and drive efficiency and technology and so on. The whole thing is unfair. Yes. I’m asking the tough questions. It’s printing another quick fire round.

I would go be CEO of OpenAI just so I can see the road map, and then I’ll I’ll know what to do from there. Next one. Most controversial view that you have today. Ah, so I would say actually AI won’t replace many jobs, think, actually. It’ll drive productivity, not replacement, and it’ll be like other technologies that have come out. Like phones didn’t replace people, now you just use them and you can do business from wherever. I think AI will let you get a lot more done in a lot less time.

Why do you think the popular narrative is that it will? Is it because we like to be scared? Yeah. We like to be scared. We like to be terrified. So there’s this concept in economics called the lump of labor fallacy. It was literally from the late eighteen hundreds. And you can look back, there’s newspaper articles in the New York Times, from the thirties being like, we are gonna be replaced by robots within the next decade. And it’s like, nope, that didn’t happen. And it just marches forward every decade.

There’s a fear of the new technology coming in. People hated personal computers in the seventies because they thought it was gonna take over and replace them. And so it’s just it’s this fallacy. Can you imagine being a math teacher back in the day with the advent of calculators? You would think your entire job is useless. But what’s the right way to view competition, Jeff? Ignore them completely. Focus on the customer.

be aware of the other boat. I don’t think It’s like, be really aware of the customer pulling you, and like, make sure you’re going in their direction as fast as possible. At as a startup, maybe if you’re a giant Goliath, you should start paying attention to some competitors. But as a startup, the market is way larger than you could possibly capture this year. So like focus on your customers. I think a lot of product managers get too distracted watching the competition, and they don’t have a vision for their own product.

What’s the best piece of advice you’ve been given? Oh, man. Twenty four hour hypothesis. When your team comes to you with a question, you need a decisive answer within twenty four hours. If you’re slower than that, you’re not moving fast enough.

Yeah.

I go back to one of Bezos’ letters, type one versus type two decisions. The vast majority are type two decisions, they’re reversible. You can undo them. What I would say it is on net, it is way better for a startup to be moving in a direction than the absolutely perfect direction. Because you can keep refining that going forward, but if you pause on a decision and you’re like, give me a week, what do you what do you want the team to do? And this was one of the biggest challenges Twitter faced internally back in the day, was just complete indecision from leadership.

climate change. I’m gonna be off growing obscure wine grape varietals in a cold climate. Where

Digits will be the new de facto accounting platform. We’re really, really excited. We’ve built basically a completely object oriented, real time AI driven approach to finance to make it really intuitive for startup founders to understand their finances as the business happens, not three weeks late on a black and white PDF.