LiveJournal for Bryan.

Whether you are a bull or a bear, it is hard not to admit that there are a lot of very strange things going on right now. I have had many conversations with friends over the past 6-9 months that devolved into some form of the question “Is this how the market is going to be forever, or is this just a point in time thing?”. I honestly have no idea what the answer to that question is, but it did get me thinking a bit about some of the things that have changed over my 8 years in VC, what the underlying cause might have been, and what advice I would give to folks operating within it. While existentially uncomfortable to admit that I am now someone who has “seen cycles in the past”, perhaps I can do some good with it.

It is, of course, impossible to point to a thing or set of things and say “that is what has caused all of this”. I am generally skeptical of certainty in the world (and you should be too), and capital markets are just far too complex to be boiled down into simple cause and effect. But in thinking through the current state of things, as well as my own experience, one thing that struck me is how much the persona of the average VC and what “ideal” looks like has changed over the last decade, especially junior ones. At its core is a general degradation of financial and business model literacy in exchange for connectivity. Whether it’s willful ignorance or just ignorance is anyone’s guess, but I think it is still interesting to discuss nonetheless.

To be specific, the big change is in the backgrounds that are prioritized in hiring. Historically the average VC background was someone who came from banking, PE, or Corp Dev. There are many notable exceptions, especially at the partner level, but this was the average and where firms generally hunted for junior talent. Over the past few years there has been a quite dramatic shift, with a lot more emphasis on hiring folks that have more technically facing operating experience, are highly networked, or ideally both. Recently, the latter has eclipsed even the former. The logic here is that as VC has gotten competitive, the biggest competitive differentiators in early stage investing are access and nuance of knowledge/expertise.

I do think that this is broadly the correct orientation, but it is important to acknowledge that it requires explicitly devaluing an understanding of finance, business models, and capital markets. The assumption is that this can be taught (especially now) much more easily than network and technical expertise, which is true. It also ties into the single piece of advice I have given every non-finance junior investor that asks me for it over the past 5ish years: spend as much time as you can studying public markets.

The theory makes sense, but in practice, I feel like very few people do this work or heed this advice. They are just not really incentivized to do so. On a “why aren’t the youths listening to me” level, that’s fine. I don’t consider myself to be a bastion of wisdom nor do I think what has helped me is the capital R right way to do things. There’s a reason they make chocolate and vanilla too. But on a collective level I do think it is a bit of a problem.

Before you read this as an ex-finance person wagging their finger at investors that come from a non-financial background, it’s worth considering the source. I myself benefited tremendously from the trend of hiring folks with more technical and operating experience. When I first got into VC after working as an Engineer at Palantir and MemSQL I knew (and I am not exaggerating), quite literally nothing about investing. Like had never opened a financial statement level nothing. I was clearly an experiment for the firm, as at that time I was the only person with a software engineering background there, and was a bit of a fish out of water.

To make things more stark, I started at Index right around the same time as Chris Ahn and Mark Fiorentino, who both became my good friends. Chris and Mark both had operating experience at Github and Stripe respectively, but had also done stints in a combination of PE and Banking. Talking to them made the gap in my skillset abundantly obvious very quickly, and I set out (with a lot of help from them that I am still grateful for) to try and learn as much as I could about public markets and business structures. I wish I could tell you that this was driven out of some foresight around how useful it would be, but the reality is it was largely driven by not wanting to look like a complete moron. The bloodbath in the wake of ZIRP was a nice kick in the pants as well -- you just have to have at least some basic understanding of what is going on in macro. I am still likely average at best at this, probably below, but I at least try.

This effort, which at times feels like a waste of time given how far it is from Seed investing, pays off handsomely. Private market investors may be what helps a company cross the river Styx, but it is ultimately the public markets that lay down the final judgement. What do they care about? For similar companies in a given sector, how do they trade? Not just multiples, but what is their actual capital structure? What do people seem to pay attention to? What are other businesses outside of my sector of expertise, and how do those trade? Business models are not 1:1, but they do often rhyme, and it is helpful to have a comp set through which to ask yourself “What is this business really?”. No great company can be private forever, though many seem to be trying!

I think the lack of this kind of thinking in the private markets over the past 3 years has been incredibly detrimental. It seems like everything, and I mean everything is being evaluated at best via the same frameworks VCs have been using over the past 10 years to evaluate SaaS. Using ARR for things that are neither annual nor recurring. “Fastest to $100m ARR” graphs that completely ignore margin profile (which, of course, is very meaningful for the cash flow and future earnings of the business. My favorite is when companies use SaaS/software infra quotas to comp people selling tokens). And it’s not just the bulls, the bears do it too. Objections like “The margin is too low, it will never work”, “this is a commodity, commodities don’t trade well” are equally guilty of superimposing SaaS accepted practice onto things that are not, in fact, SaaS. At least not as it was traditionally defined. Increasingly even this could be viewed as somewhat generous. A lot of things are not even being evaluated via this flawed methodology. They are being evaluated purely on how “legible” they are, during a time when the gap between “legible to VCs” and “legible to customers” has never been wider.

The onus of this is ultimately not on junior investors. They are just doing what they are incentivized to do, and it’s the industry as a whole’s responsibility to create the right incentives. If a junior person gets promoted on markups, one could reasonably argue that is not really in their economic or professional best interest to spend time they could be sourcing on financial models, customer value, or public market end states. Problematically, doing the same is not totally irrational at a fund level either, at least in the short term. But it is a local maximum, and I think the industry could afford to be a bit more future thinking. Bills ultimately come due.

Time will tell how things shake out, with everything going on in the market right now, it does feel like something has to give. Perhaps it will not matter and I will look like an idiot, but just as many systems are eventually consistent, markets are eventually rational. You get to decide how you want to deal with that risk.

In recent days, one cannot open their computer without immediately being hit in the face with yet another take on AI safety. We need to slow down, we need to speed up, we need all the data centers, we need to stop data centers, we are all going to die, we are all going to live forever, we are creating digital God, we are creating the antichrist. All sufficiently hyperbolic takes to fit right into the engagement bait economy that we find ourselves living in.

But hiding beneath the headlines and hot takes lies the real challenge that faces us as a society: the enormous gap between the public and Silicon Valley’s perceptions of AI. This is the real alignment problem of our time.

On a surface level, there are many obvious reasons why this is the case. We live in a culture of enormous distrust in institutions, so the public is naturally inclined to dislike large tech companies (and billionaires more broadly). The use cases for AI (beyond information retrieval on steroids) have largely been things like coding and design, which disproportionately benefit Silicon Valley types over the average American. The financial outcomes are extraordinarily skewed to a small few. And, of course, the truly head scratching PR and comms efforts of the labs themselves, which certainly have not made things any better.

But, under the surface, I think there is something deeper, more subtle, and more problematic. I suspect the disconnect between the industry and the public is less about the safety of the technology (though that is an important element), and more about its shape. It has to do with the word “intelligence” and how one defines it.

In Silicon Valley, the perception of intelligence is highly correlated with math and technical ability. It is viewed as the end all be all, intelligence in its purest form. You see manifestations of this belief everywhere, from compensation bands, to influence, to the (misguided) indignation with which engineers often view the sales team. In the minds of many, to possess this type of brain is to be intellectually superior.

In some ways, this is pretty unsurprising: it is just capitalism working. We as a society do need this type of thinking to march progress forward, and (at least in its applied form) it generally tends to be highly correlated with economic value. As a result we have, quite “rationally”, disproportionately rewarded this type of analytical, systematic thinking. The educational system has adjusted accordingly, putting an increased emphasis on STEM education, slashing art programs, and decreasing the perceived relevance of the humanities. In a way, one cannot really blame people possessing this type of capability for arriving at the conclusion that this is the highest form of intellect. We created an entire system that reinforces the belief that there is one capital I intelligence and the rest is just navel-gazing.

Talking about this is not particularly original, as it is far from new. But what is new is that it has never had a mechanism through which to go quite so mainstream. Today, we have such a device, the large language model, and this view of intelligence is clearly reflected in them: what they are good at, what they are bad at, and how they operate all show this quite clearly. Good at math and the concrete, bad at writing and the abstract, and able to rationalize reward hacking with no regard to its impact. In my view, this is where the real danger lies. Not in killing us all literally, but in eating at us spiritually by flattening all of the complexity of the human mind into a singular box. The model solves a (albeit very hard) math problem and we throw around the term AGI, as though this is the only form of intelligence that matters. It’s clear that they are intelligent, but no one who has used them would claim that intelligence is general. They are high IQ and low wisdom.

In a technical vacuum, this would not be a problem. One could (and people do) chalk it up to these being verifiable domains that are inherently easier to train the models to do well. While this is true, one cannot take this outside of the aforementioned sociological context in which they were built. I believe it is pivotal to understanding the direction we are going.

For example, consider some of the more prevailing socio-political frameworks of many of the technology’s creators. Here you see this exact ideology expanding from a run of the mill “I am smarter than you” social pecking order kind of thing into something with a deeper, more moralistic element. This is considerably more nefarious and sinister. The Rationalist Movement and Effective Altruism are literally predicated on the belief that analytical ability is the key to the maximization of moral goodness. Those who effectively crunch the numbers make their way into the company of the righteous. Those who cannot are not only dumb, but immoral. In both cases, they need saving. This does not even get into the valid question of whether or not EA is a way of nerd sniping people into being able to morally justify their own greed. History would tell you that it is easy to justify all kinds of behavior when you believe you are fighting a holy war.

This is not to say that we should not try to quantify the impact of philanthropy, far from it. It is to say that we need to be self-aware of intellectual hubris, and acknowledge the gaps and in some ways fruitlessness of trying to distill something as complex as the human experience down to numbers.

At the end of the day, human intelligence is not myopic. It is the mosaic of minds, not the singularity of them, that makes a society special: the way great writers are able to weave narratives and create complex, relatable characters, a therapist with a keen insight into the complexity of human emotions, a school teacher who knows how to communicate with children, a painter who can see the ways color can evoke emotion. These are all equally important forms of intelligence, and opportunities for human genius. These people contribute an enormous amount of value to our society, and whether they are good at math or not is irrelevant. A society that values only technical ability risks becoming cold and dispassionate. In a word, it lacks beauty.

I think people just know deep down in their gut that something feels wrong, and this is driving a lot of the anti-AI narrative. It goes beyond the surface level cynicism around personal interests, financial upside, and regulatory capture. One could argue this feeling is “irrational” because it fails to see the way AI will make our lives better, but it does not matter. You can tell people “trust us, we are smart, we ran the numbers!”, but it just won’t work. The numbers fall short. They are an attempt to explain something unexplainable, and humans know this on a deep intuitional level. After all, even science itself is a field that is literally predicated on proving itself wrong.

It’s clear that the current path will provide us with endless bounty. It will advance mathematics, invent new medicines, and likely improve the world in ways that are difficult for us to even imagine. But society needs more. It needs AI that engages with the mess, not the hyperbolic mess, but the real mess. In a graduation speech to Kenyon students, David Foster Wallace described the importance of caring for people in “myriad, petty little unsexy ways, every day”. As the models themselves graduate from the lab and move into the real world, perhaps this is the message they need as well. These things are uncomfortably unverifiable, abstract, and messy, but in that lies their power. A model could not write Strange Fruit (incidentally, written by a school teacher), nor could it deliver it with the pain and impact of Billie Holiday. These things are inherently out of distribution. AI must acknowledge our humanity, in all of its depth, complexity, and darkness.

The doomer narrative misses the mark just as much as the utopian one. Reality is never quite so black and white. The world we live in is a messy, confusing place filled with mystery. Science attempts to explain that mess. Art attempts to reveal and acknowledge it. We desperately need both.