In the midst of the Claudeforce announcement and the debate around systems of record, focused AI native startups can win by leaning into their data asset, depth, learning loops, and ability to work across systems and parties

Lio co-founder and CEO Vlad Keil on the KPI for the company's forward-deployed engineers: automate your own job, then move to the next one. "When you look at the org structure of Lio, 85% of the people are engineers." "And even the people who don't have an engineering title mostly have an engineering background. The reason for that is we don't want to be a consulting company." "There is a lot of forward-deployed work to do if you go to enterprises because they have different nuances in their processes. But how we work is... building a product in a way where we're reducing this customization, but also where it's a lot of self-service." "The job of our FDEs is on the one hand making it self-service, but internally it's automating their own job. Their KPI is... literally your job is to automate yourself. And then if you automate yourself, you go to the next task." @askvladi

Seema's point goes one step further. An end-to-end job only counts if someone owns the definition of done: who can stop it, who takes the exception, who signs off. The startup that wins writes down decision rights, not just the workflow. @seema_amble @a16z

when 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!

a16z'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_amble

Really fun to do this podcast with @askvladi at @Lio_Technology on how vertical AI startups have an advantage over incumbents!

Curious to try this - after playing around with a bunch of stuff, I haven’t tried anything that’s actually a good personal stylist in terms of suggesting new items. A few decent options for pulling together your existing wardrobe

If the inventory is unique and/or the price is lowest, @Costco site traffic and conversion will continue to grow with more agent use

great post by @aleximm and @santiago__rdz on the consumer side of going headless. ownership moves to the whoever holds the intent. the agent knows your budget, preferences, and schedule, and it decides where the order goes, and whoever fulfills the order has to re-win the customer relationship every time. it's easier to own the customer if you: 1) have something unique (e.g. Airbnb inventory) 2) are the habit (Amazon's breadth means leverage) 3) have an experience that is so good, people come back directly (this is hardest to believe). essentially if you have things the agent can't route around, that's good! of course, the real question and fight is over the winning agent.

Absolute legend. I remember a founder friend of mine telling me, amazed, how Martin was the only investor he’d come across who is able to operate at every level from the deepest product details to the highest market dynamics, AND somehow be completely accessible to his founders (and be genuinely a good human). 🐐

packed room for an amazing set of guests at @RilletHQ Recon!

Announcing: the @a16z Ops Engineering Fellowship ⚙️ Because: operations (along with everything else??) is becoming something best approached with an engineering mindset. We've been tracking this space closely, and are seeing a new kind of builder emerging inside companies... They connect internal systems, build agents, and redesign workflows across finance, sales, support, and everything between. @levie calls them "Internally deployed FDEs".. others say "AI Operations", "Agent Engineer"... I don't think anyone's agreed on the title yet, but there's something interesting going on - which to me means it's a good time to get the people who are at the frontier .. applying an engineering mindset to re-thinking how companies operate in an AI-native way - together. So: We're launching the a16z Ops Engineering Fellowship: bringing ~50 internally deployed engineers and AI-pilled operators to together over 6 weeks in San Francisco and NYC (+ group chats). There's a killer crew involved already - Claire Vo (@clairevo) is joining as a Founding Fellow, as is Sebastien Goddijn (@sebgoddijn) - who built Glass at Ramp; Owen Williams (@ow) at Stripe; Ann Miura-Ko (@annimaniac) - deep in the trenches of AI-pilling operators via Floodgate; Brandon Gell (@bran_don_gell) - COO at Every; TK Kong (@tkkong) - who helped start Ramp Labs; Matt P (@MattnPortnov) - leading internal AI at Decagon; and several more we can't include in the announcement (but watch this space 👀)... For now: Asking X 1- Who is leading "Ops Engineering" (or whatever you call it) at your company? 2- Who do you respect in this space, who should we know about, absolutely must have join the cohort and/or join us for a dinner ? Tag below, DM me - 3- or apply below ! 👇👇

Fun to see South Indian filter coffee in SF

The founder is the brand at the early stage

lots of "harvey/legora is cooked" comments on the astra announcement yesterday, but the astra product is still research focused vs. end to end workflow (+ the context/memory with that, especially across vaults/matter and firm partners)

All I have to say is @venturetwins you are just at the tip of the iceberg currently with the ethernet and your fiancé

Better models —> tackle more complex workflows —> more opportunities for AI apps

You can tell @Alphaschool has b2b software roots - the webinars, outbound SDR calls, drip email campaigns, etc 😅 (just got a cold call)

We’re talking a lot about long running/horizon agents now but owning the full job and getting these learning loops right is so important to enabling agents to do work over hours or days, through a complex logic chain with many in between decisions and handoffs

“If you are building a venture-backed software company, I think the answer is once again “do it all.” Just build the whole thing – your moat is the totality of what you’ve built and completely owning the buying center.”

Amen