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    AI-native startups’ potential edge over software incumbents

    a16z’s Seema Amble argues that resolving a disputed charge can require billing records, chat history and contract details.

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    TLDR

    a16z’s Seema Amble argues AI-native startups have an opening because jobs often extend beyond incumbents’ systems of record. Lio CEO Vladimir Keil describes using multi-agent systems for procurement work, from sourcing and requests for quotes through negotiation, shipment tracking and invoices. Their discussion also covers how enterprises learn to trust agents with more consequential decisions.

    Combined views

    25.6K

    4 Sources, first seen 3h ago

    Combined views

    25.6K

    4 Sources, first seen 3h ago

    119 likes
    Today's Rank

    #16

    Today's Rank

    #16

    3h ago
    first seen 3h ago
    119 likes
    18 comments
    50 saves
    22 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    18 comments
    50 saves
    22 reposts

    4 Sources

    @a16za16z's Seema Amble on why AI-native startups still beat incumbents: the incumbent owns the system of record, the startup owns the whole job. "Why do you need an AI-native startup if you've got Claudeforce... You've got all your data, and your employees are used to the product. So why another product?" "I absolutely still think there's a case for the AI-native startup, and it centers around the fact that the legacy incumbent is limited to their system of record, and they're not completing the end-to-end job." "Say a customer calls and says they got charged after they cancelled. Resolving that isn't just the customer going into the chat and saying 'Hey, I got overcharged.'" "The response there has to hit billing, it has to look at all the chat history, it has to look at the contract. That's not one system of record, that's the knowledge around that customer and everything it touched." "The opportunity for the AI-native startup is to say: we're going to own that entire end-to-end arc. That could be legal, owning everything from brief all the way through trial... It's really the concept of owning the end-to-end work." @seema_amble3h
    @VirtualElenawhen we think about ai in the physical world, we usually default to thinking about humanoids or self-driving cars or or models helping scientists discover new drugs and materials. but there’s another version of physical-world ai that i think deserves more attention: using intelligence to orchestrate all the benign-seeming things that have to happen for something to get built. which is especially important because the physical world is this wildly chaotic unpredictable place. what happens when the physical world refuses to cooperate. your shipment falls off a ship. or a strait closes. where, exactly, can an agent intervene? this is the problem we get into in my conversation with @askvladi, cofounder of lio, and @seema_amble. lio builds ai agents for procurement, in other words the work of figuring out what a company needs to buy, finding suppliers, negotiating terms, and making sure the right things arrive. when you’re building an aircraft or a data center, those decisions connect engineering, logistics, and finance in very consequential ways. vlad’s answer to the question of how to wrangle the physical world was about probability. suppliers have different records of reliability and routes have different risks. understanding those things in concert changes what you should buy / what you should pay: a cheap component that holds up an entire project can become a very expensive component. this all requires a much richer understanding of the world than the price and delivery date sitting in an enterprise database. i think this is a very compelling application of ai. a lot of our ability to build more ambitious things depends on making thousands of these decisions well. robots justifiably get a lot of attention; getting everything required to build them in the same place at the right time is just as important!1h
    @garrytanRT @VirtualElena: when we think about ai in the physical world, we usually default to thinking about humanoids or self-driving cars or or m…1h

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    4 Sources

    @a16za16z's Seema Amble on why AI-native startups still beat incumbents: the incumbent owns the system of record, the startup owns the whole job. "Why do you need an AI-native startup if you've got Claudeforce... You've got all your data, and your employees are used to the product. So why another product?" "I absolutely still think there's a case for the AI-native startup, and it centers around the fact that the legacy incumbent is limited to their system of record, and they're not completing the end-to-end job." "Say a customer calls and says they got charged after they cancelled. Resolving that isn't just the customer going into the chat and saying 'Hey, I got overcharged.'" "The response there has to hit billing, it has to look at all the chat history, it has to look at the contract. That's not one system of record, that's the knowledge around that customer and everything it touched." "The opportunity for the AI-native startup is to say: we're going to own that entire end-to-end arc. That could be legal, owning everything from brief all the way through trial... It's really the concept of owning the end-to-end work." @seema_amble3h
    @VirtualElenawhen we think about ai in the physical world, we usually default to thinking about humanoids or self-driving cars or or models helping scientists discover new drugs and materials. but there’s another version of physical-world ai that i think deserves more attention: using intelligence to orchestrate all the benign-seeming things that have to happen for something to get built. which is especially important because the physical world is this wildly chaotic unpredictable place. what happens when the physical world refuses to cooperate. your shipment falls off a ship. or a strait closes. where, exactly, can an agent intervene? this is the problem we get into in my conversation with @askvladi, cofounder of lio, and @seema_amble. lio builds ai agents for procurement, in other words the work of figuring out what a company needs to buy, finding suppliers, negotiating terms, and making sure the right things arrive. when you’re building an aircraft or a data center, those decisions connect engineering, logistics, and finance in very consequential ways. vlad’s answer to the question of how to wrangle the physical world was about probability. suppliers have different records of reliability and routes have different risks. understanding those things in concert changes what you should buy / what you should pay: a cheap component that holds up an entire project can become a very expensive component. this all requires a much richer understanding of the world than the price and delivery date sitting in an enterprise database. i think this is a very compelling application of ai. a lot of our ability to build more ambitious things depends on making thousands of these decisions well. robots justifiably get a lot of attention; getting everything required to build them in the same place at the right time is just as important!1h
    @garrytanRT @VirtualElena: when we think about ai in the physical world, we usually default to thinking about humanoids or self-driving cars or or m…1h