Howie Liu: evaluation

Transcript context

…I heard your interview with Brian Chesky and then later you talked about founder mode in that YC retreat, and the points there really, really resonated with me. I feel like maybe less eloquently I deduced some of the same principles just in my own experience, which is I think when you're scaling up, and this relates also to what we talked about before around the early days of building a company, you're in the details, you're finding product market fit, you kind of have to be pretty versatile, right? All these decisions from a technical standpoint to design, to even commercial, and what's the freemium model going to be like? And how are we going to market this product? What does the website look like? They're all very intertwined, right? You can't compartmentalize and then almost factory produce each of these things separately. They're all intertwined and you have a very small tight-knit team that's thinking full stack about all of this combined. Obviously that's the only way, in my opinion, to create that magical product market fit in the first place. Then I think as you scale up, the default guidance that you often get from operational experts and larger scale company investors is like, okay, you got to industrialize the process of all of this stuff, right? It's kind of like going from a bespoke artisanal, one person made an entire item of clothing to we got to factory produce this thing, right? What that means in an organizational context is you then create these different fiefdoms, you hire all these execs and each exec just manages their own swim lane, and there's relatively looser coupling between all of those different groups, right? So then you've got sales executing on its own thing, marketing's executing on its own thing, product's executing on its own thing. Even within product there's different product groups and surface areas that are each executing on their own thing. Using the factory metaphor of there's an argument that that's actually kind of an efficient way to scale up production for each of these different swim lanes, right? Each one can operate in a more autonomous and purely scale up focus, wait, how do we produce more of this thing? If the thing happens to be within one product group improving search, that's our main focus. We're just going to go and ship, ship, ship more stuff to improve search. So it's not completely crazy why people give this advice, but I think what you lose is the magical integrative value of holistic thinking and making the bigger picture bets, right? I think Brian talked a lot about this on his episode with you, which is like, look, in a company that is really serious about product, first of all, I really liked his point about the CEO has to play a CPO role, you have to care about the product. Ultimately the product is the thing and you can't just coast on scaling up go-to-market around the product forever, you got to keep innovating on the product. role, you have to care about the product. Ultimately the product is the thing and you can't just coast on scaling up go-to-market around the product forever, you got to keep innovating on the product. By the way, the best way to innovate on the product is not incrementally split over all these different little surface areas, but actually to have a bigger, more step function vision of how this product needs to make a leap, or what's the next big either act of the product or new capability of the product or reinvention of the product, right? So I think if you really care about doing that from a product execution standpoint and almost refinding new product market fit on a regular basis, I think it necessitates a completely different operating and leadership model throughout the organization. All of the stuff we just talked about in terms of how to operate in the AI native era I think is actually exactly the same as how you need to operate in this constant product market refinding of fit state. So I could not agree more with that concept of you got to think ambitiously and move the organization holistically towards these bigger outcomes, but also ship and learn and experiment a lot more in this era. Then maybe the meta learning I had from all of the above is that the specific advice obviously was like, okay, go scale up in this way or go hire these types of people, experienced operators, et cetera. Now, obviously there's some truth to that, right? The people giving this advice are not incompetent. They had some reason for giving it and in certain contexts that is the right thing to do, but I think my meta learning is it's not enough to just trust the recommendation, like, here's the action you should take from a lot of people, 'cause everybody has different priors and it's almost like we're all our own LLMs, and we all have different training from a different corpus of data informed by our own experiences. Maybe you're trained on the service- ... experiences, and maybe you're trained on like the kind of ServiceNow or the Oracle training corpus, and this person's trained on the Facebook corpus, and I'm trained on the Airtable one. I think what I've tried to do more and more is not to just ignore advice from smart people. Obviously, that's not the right answer, but to kind of take their... It's almost like in an LLM you can now with a reasoning model actually inspect the chain of thought and see how it's thinking. Why did it come up with this answer? To me, that chain of thought like "Why did you recommend this? ", is actually more informative than the actual, "Just do this recommendation." The answer might be like, "Hey, at So-and-So company, this is how we eliminated the PM role entirely." For Brian at Airbnb, it made sense. We're no longer having PMs in their traditional form. Now, we have program managers and product marketers, but more than the actual decision because I don't think it's a one-size-fits-all, everybody should do the same, why did you do that? The why actually was very informative, and then be able to take that and say like, "Okay, how would I apply that?" 't think it's a one-size-fits-all, everybody should do the same, why did you do that? The why actually was very informative, and then be able to take that and say like, "Okay, how would I apply that?" Maybe it yields a different outcome, but the reasoning actually is very informative.…

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