Tara Seshan

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now if the you know agent is doing things semi-autonomously like you actually really need to make sure that at any given point a user can like predict what is probably going to happen with a feature or a primitive that you're introducing.

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maybe it is appropriate to plan you know many many months out but I think it involves understanding like the pace of the market you're in

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and then at the same time if you build exactly for today you're going to get left behind. So it's like I constantly repeat this but it's like two to three months out.

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learning how to write good evals and writing evals as much as possible is like the most important thing

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the third thing that you just really need in addition to user empathy and systems thinking is like a relentlessness to like try it out and keep iterating

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there there are philosophical things one could have and opine on in this topic but to me there are just so many practical product considerations like the use case matters so much in deciding this.

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probably most importantly in this era is this aiming for 2 to 3 months of like where the models will be rather than being overly anchored in the present or being overly like futuristic and unusable.

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there's a lot of like theorizing that one can do before you ship something but nothing compares to like the actual empirical evidence of seeing users try it, use it, and then iterating from there.

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I think you should use the models as much as possible for writing as reporting and in as much as you think with writing as I really do and I think a lot of people do you should not use it you shouldn't replace your thinking with it

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Um I also think the human brain continues to be valuable in like how we care for each other and relate to one another.

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I think humans will continue to be most valuable as a certainly as a um like an entity of accountability.

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But I think what's been really compelling and interesting about this era and this product experience has been getting the product in the hands of users when you have so much conviction that hey it's transformative like is way better than perfect and that urgency and that uh introduction of that product is is so important.

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You fail if you build for where the models are now. You fail if you build for where you think the models will be in a year. Like both outcomes are equally wrong.

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Um, part of your job now is to elevate everyone's ambitions and say, "Actually, isn't the possibility ceiling meaningfully higher?"

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Your ambitions are no longer limited by like what you're capable of executing yourself, what you're capable of communicating.

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but a lot of it is also just really tactical like data access like cloud infrastructure and reliability pieces that feel um yeah, they feel much more prosaic than some of the the broader intelligence questions, but matter in some ways just as much for end effectiveness.

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Ideally, work feels like a multiplayer game where all of us together are getting stuff done, steering our agents as our agents continue to take care of more and more of those like rowing tactical tasks.

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I think sometimes that we underrate like that power of like intuition or even um sort of like positive like determinism of what we want the future to be like.

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I do think that increasingly the future of work will look more like steering than rowing in the sense that there will be agents that you'll you'll be able to work with that do a lot of the rowing and your role increasingly becomes steering the ship in the right direction and pointing it in the right direction.

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But the core of it has always been about what is like the most essential question you need to ask about your product. Like what is the thing that will determine whether your product works or doesn't work?

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And what that means is the thinking you need to do is being as pointed as possible about what your core hypothesis is. And that hypothesis definition is the most important thing.

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And so actually being prolific and being um more like empirical is way more important than being um like maybe more academic or theoretical.