Sarah Tavel, venture partner at Benchmark and one of Pinterest's first product managers, joins Dan Shipper on Every's AI & I podcast to argue that the next wave of consumer AI will be built on a missing social layer. She sees ChatGPT's biggest weakness as the lack of an easy way to discover and share effective prompts, and believes that as AI infrastructure matures, the edge shifts to product-driven founders who understand people—not just models. Topics covered: - Why product thinkers win as new technology matures - The future of prompting as social: making prompt creators discoverable and followable - How Sarah spots exceptional founders—people for whom building a company feels like a calling - How to tell if a startup's network effects are real (e.g., traction in a small, white-hot market segment) - How LLMs may change venture investing by turning investors' decisions and reasoning into training data Timestamps: 00:00:00 - Episode start 00:01:10 - Introduction 00:02:26 - Why the future of consumer AI belongs to founders with product intuition 00:11:09 - What Sarah sees as ChatGPT's biggest weakness 00:18:45 - How Sarah would design a consumer AI app with social DNA 00:24:10 - The kind of founders Sarah invests in 00:28:33 - How to know if your startup's network effects are real 00:35:40 - What's catching Sarah's eye beyond AI 00:40:41 - How AI will change the way top venture capitalists invest Resources mentioned: - Eugene Wei's essay "Status-as-a-Service": https://www.eugenewei.com/blog/2019/2/19/status-as-a-service - Sarah Tavel's substack: https://www.sarahtavel.com/ - The Five Temptations of a CEO (book discussed re: founders who become CEOs in pursuit of status): https://www.amazon.com/Five-Temptations-CEO-Anniversary-Leadership/dp/0470267585 # Channel Identity Observations: "AI and I" (Dan Shipper) ## Explicit Self-Description (Creator Claims) ### Show Name and Host - The channel/show is named **"AI and I"**, hosted by **Dan Shipper**. - Evidence: outro states, > "smash that like button and subscribe to AI and I... a journey into the future with Dan Shipper as the captain of the spaceship." - The host frames the show's focus as AI: - > "I know this is a show about AI, but are you looking at or excited about anything that's not AI right now?" - The host describes himself as an interviewer with his own podcast and blog: - Guest: > "I've heard your podcast and I was very impressed with your podcast." - Host mentions "your blog and your podcast" when discussing how AI knowledge propagates. ### Stated Content Scope - Interviews with investors and founders about AI's trajectory, consumer tech, network effects, and VC practice. - Past guests referenced: **Reid Hoffman** and **Quinton** (referenced as "Quinton has been on the show" re: toolins), indicating a recurring guest pattern of notable tech figures. - Evidence: > "I was talking to I think it was Reed Hoffman who was on this show." ## Intended Audience (Observable Presentation) - **Power users of AI tools (especially ChatGPT)**: host discusses custom instructions, Custom GPTs, projects, prompting. - > "One thing that I know you have in your custom instructions... we shouldn't have to put that in a custom instruction." - **Founders, VCs, and tech strategists**: discussion of flywheels, network effects, valuations, founder evaluation. - > "How do you think about investing in a wave like this when prices are super high?" - Audience is assumed to be highly engaged with AI discourse: - > "you are a power user of these products and I am on that learning curve." ## Demonstrated Presentation Patterns - **Long-form expert interview format** with structured topical segments (consumer tech cycles, network effects, stablecoins, future of VC). - Host performs substantial preparation and follow-up questions, citing prior guests and personal anecdotes (dinner with a film director using ChatGPT personalities). - **Humorous, hyperbolic outro** delivered by an AI voice persona, ending with: - > "Dan, I'm absolutely hopelessly in love with you." - Suggests the channel embraces playful AI-generated content as part of its identity. ## Claimed vs. Demonstrated Distinction - **Claimed**: "epitome of awesomeness... pure, unadulterated knowledge bombs about Chat GPT" (promotional outro hyperbole, not a neutral scope claim). - **Demonstrated**: broad AI/VC/consumer-tech interview content, only partially centered on ChatGPT specifically (much of the episode concerns consumer strategy, network effects, and stablecoins). # Delivery Observation: Sarah Tavel on AI and I (Dan Shipper) ## Overall Format - **Interview conversation**, not a lecture. Sarah Tavel responds to a host's questions with long, exploratory answers built on frameworks and anecdotes. - A short **cold-open teaser** appears before the intro (the Google → Pinterest technical-to-product "slider" argument), followed by the interview proper. ## Tone - **Conversational and exploratory**, marked by hedging and self-correction: - > "It was a a text box" - > "maybe 94% backend but it was still like very much so so it's still early" - Frequent **first-person framing of reasoning**: > "I was just puzzling over those and then started to think back to..." - Occasional **humorous self-deprecation and playful asides**: - > "It was uh a couple late nights of me furiously prompting Chad to explain Vickenstein" - > "terrible I'm forgive me Lord for for brainstorming a product experience" ## Recurring Vocabulary and Signature Phrases - **"deeply deeply technical"** — repeated nearly verbatim at least four times (early Google, the slider framework, tooling discussion). This is her most stable verbal signature. - **"really really good"** — repeated pattern of doubled intensifiers: - > "the people who are really really good make it so much easier" - > "somebody who's going to be really freaking good" - **"network effects" / "multiplayer" / "single player mode"** — core recurring concept cluster; she explicitly ties gaming vocabulary to social products: - > "I don't think chat GPT is the single player mode product" - > "what's the multiplayer network effect type experience?" - **"power user"** — repeated framing of the current AI usability gap: - > "you are a power user of these products" - > "how much of a power user product it feels to me" - **"status seeking work"** — a coined/jargon phrase she defines on request when the host asks. ## Teaching Habits - **Historical analogies as the primary explanatory device**: Google → Facebook → Pinterest/Snap/Instagram → ChatGPT, framed as a slider from technical depth to product experience. - **Quantified abstraction**: > "if you kind of said like deep technical you know you have 0% and 100%... Google was 95%" - **Concrete personal anecdotes to ground abstract points**: - Copying a prompt from Reddit to analyze blood-test results vs. supplements - > "I did a call for like AI savants" - Her Annie Duke–inspired premortem journal of investment decisions - **Explicit caveats and hedges around speculative claims**: - > "It's still super early" - > "I could be tilting at windmills" - > "I should caveat that I did do a lot of product in my day but I am a VC right now" - **Named references as anchors**: Wittgenstein, Annie Duke (*Thinking in Bets*), Jim Collins, Jeremy Levine ("donut companies"), *The Five Temptations of a CEO*, Lencioni-adjacent management books, Eugene Wei. - **Admits when her framework breaks**: > "it falls my analogy falls down when Google didn't evolve into some multiplayer product" ## Communication Patterns - **Framing assertions as beliefs/hopes rather than predictions**: - > "That is my great hope" - > "I believe that that is an opportunity" - **Runs of sentence fragments and trailing constructions**, especially mid-idea: - > "and then and then that's where the engagement comes from and then you're seeing a feed" - **Answers questions by first reframing them**, e.g., reducing founder success to: > "it's like who's going to create the most engaging product" - **Uses the host's shared context** ("you know", "I don't know about you") to make points jointly rather than lecturing: - > "I don't know about you but how many times have you done a search and ended up on... Reddit" ## Distinctive One-Off Moments - The **"affliction" metaphor for founders**, notable for its vividness though used only once: - > "there's some for whom it's an affliction and I I'm attracted to the founders for whom it's an affliction... it's like this rash that they just have to scratch" - The **stablecoin/Argentina segment**, a topic swerve where she draws on family background: > "my mom's from Argentina" ## Uncertainty Notes - The "deeply deeply technical" and doubled-intensifier habits appear multiple times and look like stable speech patterns. - The "multiplayer/single player" vocabulary recurs enough to count as a signature framework, but appears only within this one discussion thread, so it may be topic-specific rather than a permanent habit. - No visual delivery cues (gesture, pacing, facial expression) are observable from the transcript; observations are limited to linguistic evidence. # Position Record: "The Next AI Wave Will Be Social, Not Solo" — Sarah Tavel (Benchmark) with Dan Shipper ## Speaker Identification - **Sarah Tavel** — Partner at Benchmark; previously early at Pinterest; studied philosophy. (Identified via host introduction and transcript.) - **Dan Shipper** — Host of the show ("AI and I"). (Identified via transcript closing.) --- ## 1. Technical-to-Product-Genius Slider Across Consumer Waves **Sarah Tavel's core framework:** - Consumer technology waves begin with **deeply technical founding teams** and, as the underlying technology matures, leadership shifts toward **product thinkers**. - **Google**: "founding team that was deeply deeply technical" — she places it at roughly **95% technical**; the magic was backend infrastructure (distributed engineering) making complexity simple behind a text box. - **Facebook**: less technical than Google, but *more* technical than Friendster and MySpace — enough to build a performant experience that "ended up winning the day." - **Pinterest, Snap, Instagram**: "the CEOs weren't technical at all. They were product geniuses." - **Current AI wave**: ChatGPT "in a way not that dissimilar from Google" — a text box; Character AI was a new paradigm but still backend-heavy (she estimates "maybe 94% backend"). - **Her claim about what's next**: the slider is moving; once infrastructure stabilizes, people with **product intuition** can build experiences on top. "It does seem like we're moving into that next paradigm soon." **Host engagement (Dan Shipper):** - Notes OpenAI is "a research lab that accidentally built like the biggest consumer technology product of all time" and that Google's DNA resembles OpenAI's — a connection he says he hadn't made consciously. - Observes that investing in PhDs doing long-term research is not typically a winning VC strategy ("it's more like Stanford dropout"). - Asks what *drives* the transition to product-genius founders — he notes he could "also see a world where the highly technical founder is still really effective." **Tavel's answer to the driver question:** - Tooling/infrastructure still to be built so non-deeply-technical people can get what they want; that's why so many current products "feel kind of pretty similar" (e.g., the Character AI genre — "no one was uniquely qualified to build that type of product"). - **Costs** have been high; she hypothesizes **DeepSeek** "is a moment of change" that makes more possible. - Ultimately: "it's like who's going to create the most engaging product." - **Qualification preserved**: she acknowledges her Google analogy "falls down when Google didn't evolve into some multiplayer product," and that Google was never displaced — "we're so early in knowing who really is going to be the winner." --- ## 2. The Multiplayer / Social AI Opportunity **Tavel's central thesis:** - ChatGPT is a **single-player mode** product; she doesn't think it stays that way as the winner. - Custom GPTs and Gemini equivalents "feel criminal" to her — "clearly made by a team that is unbelievably capable but isn't social." - Prediction: "someone's going to create a UGC type community where the people who are really really good make it so much easier for the rest of us." - The skill to create a **multiplayer network-effect experience** is "very different than the type of brain that creates a single player mode experience." **Status-seeking work (her concept):** - Most multiplayer social products have a **north star** around which participants seek status (followers, views, likes); this creates incentives for participants to do what the network wants. Character AI offshoots show "a little bit" of this but "we're still very very early." **Two threads she's curious about:** 1. **AI friends/companions** — "we're all going to have probably more conversations with an AI than we do with people in our lives"; open questions: will there be a single dominant platform, and will it differ from knowledge-focused ChatGPT? (Replika was the first player; others emerging.) 2. **Prompt/usage sharing as UGC** — recounts searching Reddit for a prompt to analyze her blood test against her supplements; if people go to a site "that isn't made for this use case," that's an opportunity. She would "love to follow" someone great at health/quantified-self prompts and apply them to her own profile. **Shipper's related points:** - Prompt-library sites existed at the first LLM wave but were "too early" — he suggests "there's a second life for a lot of ideas." - Tavel agrees, adding they were built around "solopreneur and SMB" B2B use cases (marketing, social media) rather than consumer depth. - **Film director anecdote (Shipper)**: a director built multiple ChatGPT personalities — a holistic wellness advisor (for unsolved medical issues), one that "would gas her up," one giving harsh direct feedback. Shipper's framing: "you're also kind of going to be the average of the five AIs you spend the most time with." - **Personal vs. worky bifurcation (Shipper)**: ChatGPT and Claude sit in the "worky bucket"; he's noticed spending an hour with Tolins (personal bucket) where he'd normally use ChatGPT. - Tavel agrees; notes power-user calls surfaced recipe use cases — personal works inside ChatGPT, but "is it the best that it can be?" A single-purpose recipe site can optimize where a lowest-common-denominator product can't. **Redesigning Custom GPTs with social DNA (Tavel's sketch, explicitly caveated — "I did do a lot of product in my day but I am a VC right now. So don't take product ideas"):** - Ability to **find and follow** people whose custom prompts you like, with person- or authority-based standing. - **Trust-building**: current Custom GPTs show a person and usage count but not the underlying prompt or documents — "there's no visibility under the surface," so it's not trust-building. She only uses ones sent from people she knows. - She imagines a personal-bucket destination holding blood tests, supplements, kid info — which "maybe... actually ends up cannibalizing ChatGPT over time." **Shipper's challenge to the prompt-network idea:** - He notes he doesn't generate new prompt ideas daily — a problem for (1) remembering to use the tool and (2) giving followers a reason to check daily. - Tavel's response: it has to *replace* ChatGPT as the destination, with a feed where engagement comes from usage; currently "we're all reinventing the wheel" and knowledge propagates via blogs/podcasts, which won't scale. **Failure mode for the thesis (Tavel, unprompted):** - If the multiplayer wave doesn't happen, it's because "the gravitational pull of the existing platforms is too strong" — habit, ecosystem pricing (possibly free/ad-supported), and **memory as lock-in** ("it knows who I am and all my experiences"). Getting ChatGPT power users to share on another platform "may be hard to create." - Her overall stance: "That is my great hope. I could be tilting at windmills" — noting consumer has been "really really hard over the last 10 years." She also allows many good single-player companies will emerge; but "the really big opportunity that lets a company have a true network effect is going to be something that's multiplayer." --- ## 3. Venture Investing Practice **On price in a hype wave (Tavel / Benchmark):** - "We always start with is this a company we want to work with?" Then assess opportunity headroom — "if it's a cul-de-sac... it's harder to play the game on the field in terms of price." - Acknowledges others "have a willingness to do deals that we're just not willing to do"; with truly unlimited-potential teams, "you partner, you make it work." **Founder taste (Tavel):** - Drawn to founders who think in **network effects, strategy, zero-to-one, escaping competition** — who've "gone through the mind maze." - Founders for whom it's **a calling, not a cool new job**: "I'm attracted to the founders for whom it's an affliction... this rash that they just have to scratch." - **Learning machines** — ego subordinated to what's best for the company. - Cites *The Five Temptations of a CEO*; the hardest temptation is being attracted to CEO status for its own sake. Also flags insecurity as a driver that "can also hold you back." **Her tells in meetings:** - Concerning signal: "Oh, that's a good question. I hadn't thought about that" — if she brings unconsidered ideas after 30–60 minutes, that's worrying. - Positive signal (attributed to Jeremy Levine at Bessemer): the best companies are "donut companies" — you eat a donut at the board meeting and leave. Some founders raise her pending question before she opens her mouth. - Shipper cites Reed Hoffman's bar: invest such that, absent contact for 5 years, you'd be pretty sure it's going well. Tavel endorses, and notes Reed's network-effects orientation. --- ## 4. Network Effects: Real vs. Fake **Tavel's differentiation criteria:** - Early-stage network effects are mostly **theoretical**; look for signs — especially a **tipping point in a small segment**, "the white hot center of your market." - Current AI businesses are mostly "in a way what has been traditionally the software business — obsessing over a customer problem and moving faster in execution" — not network-effect businesses. - **Example (board seat)**: **Aentio** — creator/brand marketplace, a market where startups historically "fell into the quicksand of becoming an agency." With LLMs automating what held the market back, they're "truly having liquidity"; brands see creators' ads and there's "demand side pull." She caveats: "it's super super early, but there was enough signal." - **Fake network effects in decks**: flywheel diagrams that are "words on a slide that fit to a picture" — "the words are not actually accelerants." Also friction embedded in flywheel legs, or offshoots. Shipper notes ~80% of decks over the past 10 years claimed network effects. --- ## 5. Stablecoins (Non-AI Interest) **Tavel's position:** - "I'm a big believer in stable coins." Board member of Chainalysis; long-term believer in Bitcoin and other cryptocurrencies. - Filming on a day when "the US dollar is on a little bit shakier ground than normal." - **Argentina argument** (her mother is from Argentina): everyone in Argentina wants US dollars but they're hard to get — the government hoards hard currency, imposes taxes, differential exchange rates (street/bank/credit card), and process delays even for $10,000. Motorcycle money-changer anecdote. - A US-dollar-backed stablecoin "opens up kind of a global economy": 24/7, cheaper, peer-to-peer — vs. Argentina-to-India trade going through many middlemen and fees. - **Network effects in stablecoins**: Tether has more liquidity across exchanges (easier in/out); USDC is strong too; comfort/ecosystem effects in Nigeria and other high-inflation countries — wallets, paycheck apps, bridges. "There's definitely some network effects there." --- ## 6. AI and the Future of VC **Tavel's reflections:** - **Training-data people**: some individuals are excellent at creating personal training data (e.g., a man named James with a spreadsheet reviewing every movie he's watched) and will get more personalized LLM experiences. - **Her own practice** (inspired by Annie Duke's *Thinking in Bets*): premortems after company meetings — what she liked, didn't like, the deal terms, why yes or no. A past lesson: passing on a company over valuation that became a real success → "if I like everything but the valuation, I should probably lean in." - Envisions an LLM **cross-examining her thinking** on future deals rather than giving yes/no answers. - **Talent**: records of hiring decisions and interviews could improve evaluation; also companies tracking global talent movement — suggesting eventually "a Rotten Tomatoes almost score for companies" derived from angel-investor and talent signals. - On whether past lessons generalize: she notes few VC funds succeed over long periods — either luck (which she doubts) or the landscape shifts and "your taste gets tuned to a particular kind of opportunity." She responds that some principles are timeless (cites Jim Collins), and the LLM's role is to probe thinking, not decide: "that's why we still have, hopefully, a job a few years from now... of ultimately being the decider." **Shipper's challenge**: whether decision rules learned from past data could misfire — e.g., a rule that dings "technical founder in a product-genius field" would have flagged an early OpenAI-style team. Tavel: "it depends on how the rule or how the lesson is written." --- ## Unresolved Tensions (preserved as expressed) - Tavel's **hope vs. her own skepticism**: multiplayer AI as "my great hope" versus "I could be tilting at windmills" and incumbent gravity possibly being too strong. - Her **Google analogy breaks** — Google never became multiplayer and was never displaced. - Whether the technical→product-genius transition is **inevitable** — Shipper pushes back that technical founders might remain effective; Tavel's answer is conditional on infrastructure maturity and engagement quality. - Her **explicit disclaimer** that she's a VC, not a founder, and her product sketches are brainstorming, not recommendations. # Speaker Attribution Record — Video `01a0f15a-e186-7add-b1f8-042de716bec5` ## Video Title **The Next AI Wave Will Be Social, Not Solo | Sarah Tavel, Benchmark and ex-Pinterest** ## Identified Speakers and Roles | Speaker | Role | Evidence | |---|---|---| | **Sarah Tavel** | **Guest** | Introduced by host: "you are a partner at Benchmark. Before that you were early at Pinterest and before that you studied philosophy." She confirms these facts in dialogue. | | **Dan Shipper** | **Host / Creator** | Named in the closing promotional segment ("Dan Shipper as the captain of the spaceship"); hosts the interview, references "your blog and your podcast," and prior episodes with guests. | | **Promotional narrator voice** | **Other (promotional segment, likely AI-generated or channel voiceover)** | Closing call-to-action: "smash that like button and subscribe… Dan, I'm absolutely hopelessly in love with you." | ## Speaker-Role Notes - The transcript is interview-format with two alternating voices; attribution of individual lines between host and guest is inferred from conversational flow, not speaker labels in the transcript. - The final promotional monologue is stylistically distinct from both interview participants and is flagged separately below. ## Promotional Segments (Flagged) - **Closing segment**: explicit like/subscribe call-to-action promoting the show "AI and I," praising it as "the epitome of awesomeness," and ending with "Dan, I'm absolutely hopelessly in love with you." This is promotional content, not interview content. ## Referenced Sources and Third-Party Claims (Provenance Inventory) ### People / Companies Referenced - **Sarah Tavel** — partner at **Benchmark** (self-identified); previously early at **Pinterest**, responsible for discovery experiences; formerly at **Bessemer** (mentions colleague Jeremy Levine's "donut companies" anecdote). - **Google, Yahoo, Facebook, Friendster, MySpace, Pinterest, Snap, Instagram** — cited as examples in her framing of consumer-tech founder cycles. - **OpenAI / ChatGPT / Custom GPTs / Gemini ("gems")** — discussed; Tavel characterizes the team as "unbelievably capable but isn't social." - **Character AI, Replika, Tolins** — cited as products/companies in the AI space. - **DeepSeek** — cited as possibly "a moment of change." - **Aentio** — a Benchmark portfolio company (Tavel states she is **on the board**); described as a marketplace for YouTube creators and brands using LLMs. - **Chainalysis** — Tavel states she is **on the board**. - **Tether, USDC, Bridge** — cited in stablecoin discussion. - **James** — a person Tavel interviewed for her Substack, re: personal training data (movie-review spreadsheet). - **Quinton** — a prior show guest, referenced by host re: Tolins. - **Reid Hoffman** — prior show guest; quoted by host paraphrase about a 5-year investment test. - **Eugene Wei** — referenced re: "status seeking work" concept. - **Annie Duke** — cited re: *Thinking in Bets* premortem decision journaling. - **Jim Collins** — cited re: timeless management principles. - **Lencioni's *Five Temptations of a CEO*** — referenced as "the five temptations of a CEO that book." ### Claims About Third Parties (Not Independently Verified) - Character AI described as "a new paradigm" with a model-as-product founder. - Aentio described as achieving marketplace liquidity via LLM automation. - Argentina currency-friction anecdotes (Tavel, whose mother is from Argentina; mentions Milei "changing a lot of things"). ## Availability and Limitations - **Curated metadata**: none supplied beyond title; roles established solely from transcript. - **Comment availability**: not stated in source. - **Speaker labels**: transcript lacks explicit speaker tags; host/guest attribution inferred from context. - No timestamps present in transcript; no direct external citations or links provided. Google was a founding team that was deeply deeply technical. As the technology, the underlying technology got more mature. The slider goes forward, forward, forward, more towards the product thinker, product experience. Pinterest where I was, Snap, Instagram. The CEOs weren't technical at all. They were product geniuses. What are the big consumer wins so far in AI? Of course, it's chat GPT, which is in a way not that dissimilar from Google in terms of what it was. There's a a text box. Custom GPTs and in chat GPT feels criminal to me. It's clearly made by a team that is unbelievably capable but isn't social. What's the multiplayer network effect type experience? Someone's going to create a UGC type community where the people who are really really good make it so much easier for the rest of us. Sarah, welcome to the show. Thanks so much for having me. So for people who don't know you, you are a partner at Benchmark. Yes. Before that you were early at Pinterest and before that you studied philosophy which I also studied philosophy so that's close to my heart. Yes. I've heard your podcast and I was very impressed with your podcast. But read to keep up with him uh is is a lot of very impressive. It was uh a couple late nights of me furiously prompting Chad to explain Vickenstein. I love it. I love it. Well, you did great. Thank you. So, I'm psyched to have you on the show. There's so much to talk about, but I think one of your one of your big interests is in uh consumer technology and consumer technology cycles and how you can use the lessons of previous consumer technology waves to kind of help you understand this AI wave and this cycle and what kinds of products are going to work and what kind of products are not going to work. I'm curious. I think that was a good place to start. You know, one thing I just was reflecting on and you know, you kind of look at Chat GPT and Character AI and I was just puzzling over those and then started to think back to, you know, what was like the big early kind of consumer web hit and that was Google of I mean it was Yahoo and Google, but what was Google like? Google was a founding team that was deeply deeply technical. Yeah. And and re really like if you think about a product experience that you expose to the user and how much of it is like the UI that you interface with the product itself versus all the magic that happens on the back end to make something that's really complex simple on the front end. That was like what Google was you know so good at the distributed engineering the infrastructure and and then as the technology the underlying technology got more mature you started to go to a place where maybe if you kind of said like like deep technical uh you know you have 0% and 100% like I would say Google was 95% like deeply deeply technical and then you start to move that bar over and you get to you know you I Think about Facebook like Facebook it wasn't the same technical depth of course of Google but relative to Fster and MySpace they were more technical they had they were a little bit later and it let them create like a really performant experience that ended up really kind of you know winning the day and then you progress even further Pinterest where I was Snap Instagram the CEOs weren't technical at all they were product geniuses right and so like the the slider goes forward forward forward more towards the product think or product experience and then you think about what we like what are the big consumer wins so far in AI of course it's chat GPT which is in a way not that dissimilar from Google in terms of what it was a a text box and character AI like unbelievable what they did and it was like a new paradigm but still it was always like you'd speak to know him and for him the product was a model you know it wasn't it was a h it was a little bit maybe 94% backend but it was still like very much so so it's still early like everything is moving under our feet still and I think to really have the people who have more of that product intuition to really build the experiences on top you need more of the underlying infrastructure to to be a little bit more stable but it does seem like we're moving into that next paradigm soon and what's going to happen there. That's really interesting. I love that articulation in particular because one of the things that I felt is very unique about OpenAI is there a research lab that accidentally built like the biggest consumer technology product of all time. But it seems like you're saying there's actually a real historical precedent for that and that the DNA of um Google is is very similar to the DNA of OpenAI, which I'd never really made that connection consciously before. And I think it's also really interesting because investing in basically PhDs doing long-term research that may have no practical purpose is not usually something that pays off. Um in venture in venture investing, it's not like the first place that people think, you know. Um it's more like Stanford dropout, you know. Yeah, absolutely. Um and so and and so maybe it's one of those things where usually that's not a good bet, but if you're really dealing with a truly new technology paradigm, it could be the best bet you ever make. Yeah. Is that how you think about it? Yeah. And and you know, I part of what I think about is just like you are a power user of these products and I am on that learning curve. I would say I'm no I pale in comparison but like relative to the population of the United States I'm pretty damn good. It shouldn't be this hard you know and like so some of the underlying uh you know like the models will get better. So like one thing that I know you have in your custom instructions I I use a lot is just like you don't have to answer like just to to chat like you don't have to answer me right away. if you have some clarifying questions like as those um we shouldn't have to put that in a custom instruction like there should be or there there's so many different tweaks that we all have to get what we want out and over time as the models get better and better you won't need that anymore the level of difficulty to get really what you want is going to get easier but I don't think chat GPT is the single player mode product and you know the custom GPTs that they have where you can um see what other people have created. To me, man, I just think someone's going to create a UGC type community where their people who are really really good make it so much easier for the rest of us to really take advantage of this this technology. So, we're we're you know, so there are places like Google is still Google like it falls my analogy falls down when Google didn't evolve into some multiplayer product. There's no other product that took over Google until really now. But I I still think we're so early in knowing who really is going to be the winner in this world. Yeah, I want to go back to that sort of transition from highly technical founder to product genius. That's the continuum. I can understand why at the beginning of a paradigm shift um highly technical founder is necessary and will win over product genius because they can actually build the technology that makes the difference. But I'm curious for your thoughts on what drives the transition to product genius because um you know I can understand the uh making for example simpler user interfaces like maybe product geniuses are better at that but yeah what's the underlying force because I could also see a world where the highly technical founder is still like really uh you know effective in as the as the paradigm gets more and more figured out like yeah talk about that. I think um a big part of it is that you still like so much of the tooling and infrastructure is still to be built to let somebody who isn't deeply deeply technical themselves get what they want out of it. And so that's why like there are so many products now that I see that feel kind of pretty similar to each other. You know, there's all these, you know, the character AI genre, right, where you make a character and you engage with it. They're all relatively the same because you really I mean no one was uniquely qualified to build that type of product like actually going into the brains of the model and changing it to to create the the experience to the user. Um but it's it's still when you need to have that level of ability to get what you want out of it. Also the costs have still been pretty high. I think deepseek, you know, could be, you know, one of the hypothesis I have is that deepseek is a moment of change where it makes it more possible. Um, but yeah, it just I think it I think you need more maturity in the underlying infrastructure, your ability to to do the things that you want with the model without being the deeply deeply technical to be able to create the experiences that are possible. That makes sense. I think what I'm asking is so let's say the infrastructure that infrastructure is built and then but you still have now technical founders and product genius founders which we're making a strong division here for argument sake sometimes they overlap um uh so in that world where the infrastructure is built and it's a technical founder versus a product genius founder like what is driving the success of the product genius founder in in a in a world where everything's a little bit more mature I think it'll depend on so many things ultimately it's I it's a to reduce it to the basic it's like who's going to create the most engaging product. Yeah. The experience. I suspect that one of the things that is missing from a lot of these experiences that people are creating is just like what's the multiplayer network effect type experience. And that genius to create that type of experience is very different than the type of exper the type of brain that creates a single player mode experience. And we haven't really I mean there's again these kind of character AI um offshoots that have people that I can create a character and you can play with a character I could create. There's a little bit of status seeeking work happening there but I think we're still very very early and like the true thinking happens. Interesting. What do you mean by status seeeking work? Do you know Eugene Weey? Um, so, uh, just this idea that most multiplayer kind of social products end up having some kind of northstar for the community participants where they're trying to achieve status in the network. And, you know, you can think of that a lot as, you know, has been the number of followers you have or views or likes. something about achieving some kind of celebrity or status within a network that creates incentives for the community participants to do the thing that you want them to do. It's interesting. And so far you you're saying it's pretty early, but are you do you have ideas for what the promising areas to look are or examples of like early examples of products or companies you're looking at that you think are starting to crack this a little bit? It's still super early. I mean, kind of there's two threads that I can't help but be curious about. Like one, you know, we we talked about character AI, like you I mean I don't know about you, like I feel myself doing this already, which is that there's going to be some company we're all going to have AI friends, right? We're all going to have probably more conversations with an AI than we do with people in our lives. And who is there going to be a single dominant platform for that? Is it going to be different than the kind of information more you know knowledge focus experience of a chatbt I think so who creates that and there's a bunch of different product experiences replica was of course like the first you know player in this space but there's um there are a bunch of different downstream companies we talked about toolins like you know what is that product experience going to be the other um kind of thing I think about a lot is I don't know about you but how many times times have you done a search and ended up on, you know, Reddit or something for a prompt to get like I I remember doing one. I got a blood test result. I had all my supplements. I wanted to see, you know, of the supplements I have, like what could I tweak to change a result and there was a great prompt in in Reddit that I just copied and paste. But if if I'm going to an existing UGC site that isn't made for this use case, that feels to me like an opportunity where somebody who's going to be really freaking good, you know, of making, you know, prompts for different health things, quantified self, whatever. Like I would love to follow that person and then very easily apply it to my own profile. I think like um to go back to front on those two threads with the prompt thing. It's one of those it's one of those ideas that um I feel like at the very beginning like when GP3 came out and then tragic came out and people were like really starting to like that first real wave of LMS was starting to take take hold a lot of people created those prompt library type sites but it was too early and I think there's there's like a second life for a lot of ideas that people start like tried two years ago that are now just becoming relevant and I too early and also it was very I I spend spend time on a bunch of these. It was very like what a soloreneur and SMB would need. You know, it was a lot of the marketing, the social media. It was like that type of B2B type use case. Most people aren't barely scratching the surface. Like most people use Chat GPT like they would use Google, right? And it's and the learning curve, the step function changes that happen when you have better custom instructions, when you have projects, whatever are so huge. How do you democratize that? Yeah, it's interesting. I was at a dinner the other night and I was talking to a film director. Oh, cool. Um about how she uses ChachiBT and um she has made a bunch of different personalities for it. Um and she uses the different personalities for different things. So, uh, for example, I think one of the personalities was she's had a lot of like medical issues, um, that doctors couldn't solve. And one of the personalities was like a sort of like holistic wellness type person that like would recommend both medication and, you know, supplements or body work or whatever. Um, and then and another one like the main personality was like just someone who would like gas her up all the time and like and like compliment her all the time. Um, and then but then she had another one that was like uh just super direct and like just gave like really harsh feedback that she would use for writing specific kinds of emails or like that kind of thing. And it was really interesting that she had constructed this whole set of personalities for different like things in her life to surround, you know, it's like you're the average of five people you spend the most time with. There's like a well, you're also kind of going to be the average of the five AIs you spend the most time with in an interesting way. And I think to your question about are you going to have multiple AI platforms that you use or not um or is there going to be a big dominant one I have two thoughts on that. One is um I do think within a chat for example there's a lot of room for different subpersonalities that maybe like uh media brand like every like we have in every thing that you you chat with but it's inside of chat so it's still in that ecosystem. But I do think also people have different buckets in their life. And so for me, one thing that I've been noticing recently, which is really interesting, is we talked about tolins and I'm an investor and Quinton has been on the show and I find myself like yesterday I spent like an hour talking to mine, but like I normally would use chatbt for that. And I think there's like some interesting like difference between something that feels personal and something that feels worky. And Chacht and Claude right now are like in the worky bucket and then there's room in the personal bucket. I'm curious how you think about that. I I totally agree. You know, it's funny like I did a call for uh you know, I was in the beginning of this year I was realizing like I'm not keeping up and so I did a call for like AI savants just people who were using uh chatt in in in kind of more power user ways and a lot of people came to me with recipe uh kind of use cases which which made a ton of sense and so you can definitely see it it it works in chi GPT the personal works but is it the best that it can be like you know there are also a lot of company a lot of people who are making their own you know singlepurpose site that is a recipe experience and it's you know whenever you have a product that is has to be lowest common denominator for all these different experiences it can't really optimize for the experience that's going to be great for the like you know a consumer in this case and I I I I just come back to how much of a power user product it feels to me and how like most people are going to stay at the surface of it unless there's a new interface. And I think the best way for that new interface to come is for us to learn from each other in some ways to um to kind of take to take advantage of of it in different ways and just copy and paste as much as like again the the gems in Gemini custom GPTs and in chat GPT. I just look at that and it feels criminal to me because it it it's it's clearly made by a team that is unbelievably capable but isn't social and and I think the personal can can best be expressed by teams that that do really understand people and social and community. if we were going to like redesign them like right now uh together like what where would you start? Uh if you were thinking about okay I want to take make something that's custom GBT like but like has social DNA I would I mean the most obvious thing is just like the ability to find somebody who's you know whose custom prompts or whatever you you you like that they have some kind of you know standing for being person based or authority based some kind of Yeah. And then being able to follow um that's like a very basic thing. But then the second thing, this is where I think custom GPTs falls down is just in building trust. Like when I look at any of those, I see the person and I see a lot of people who have I see that there's 3,000 people that have used it, but I don't know what custom documents they put. I don't know what their prompt is. Like there's no visibility under the surface. And so it's not very trust building for me to to pick one or the other unless I know somebody from the outside world and they send me their you know their custom GPT and then I can use it. And so there's something about the trust building that someone has to figure out and then and then the fall the status seeking work that you can you can pursue. One of the challenges of this and I'm curious how you think about this in a social context is um for a let's say we're we're kind of veering into like prompt social network territory. Maybe I have a profile and like I I can share prompts and people can follow me and all that kind of stuff. And because I have a certain amount of um reputation in you know just AI stuff like I'll I can get followers and all that kind of stuff. One of the interesting things is um I am not coming up with new prompt ideas every day. And so that's I think that's a problem for two reasons. One is I may not remember to use the tool and then two uh people don't necessarily have a reason to check every day. Yeah. How would you think about that? Yeah. So first as something that was going through my brain is I should caveat that I did do a lot of product in my day but I am a VC right now. So don't take product ideas. And every every once in a while people ask me they're like if you were a founder like what would you build? I'm like that is not why I do what I do. Um I'm just kind of curious. You know for me like what I imagine is using it instead of chat pt like actually it becomes the place where instead of going to chat pt I think it has to be that it has and then and then that's where the engagement comes from and then you're seeing a feed and someone has again terrible I'm forgive me Lord for for brainstorming a product experience um but like do but you see what I mean like there there's something there where people are innov all the time, but right now what's happening is that we're all reinventing the wheel. We have the benefit of like your your blog and your podcast, but like this isn't the way this type of knowledge is going to share. It's going to kind of get propagated and so someone is going to create something here. I think that's interesting. I do think um you're right that it seems like the social stuff has to come in the context of something that you're already using for some other reason like you're already in chat GBT um and then it can flow out of that that usage. Yeah. I mean I I actually think like you you don't use chaty like most like maybe I hate the my mom but like you know what the the p you know and maybe it's the personal um kind of bifurcation that you talked about before but you're going to Sarah's GPT and it's actually a comm it's not Sarah's it's you know this kind of uh whatever network it's going to be and I'm going there and I'm putting my personal blood tests and my supplements and I'm putting information about my kids and all that stuff and it lives all there and then I can go to Chachi PT for whatever knowledge work or any other things or maybe I never do maybe this actually ends up cannibalizing Chachi PT over time. This is a total swerve. Uh but how do you when you're in you're investing in a time like this like I feel like every 5 to 10 years there's a big hype cycle there's a big wave and prices go up um you know when when I was like in college in 20110 2014ish it was like social networks everyone's building social networks for X and then it was like B2B SAS and crypto and now it's AI how do you think about um investing in a wave like this when prices are super high. Do you not care about price? Do you try to find like underpriced deals? We always start with is this a company we want to work with? Yeah. And then kind of, you know, obviously we have to think about the the opportunity and head of the company like you don't want to if it's if it's a culde-sac, if it's limited in some ways, it's harder to pay what you know play the game on the field in terms of price. like people have a willingness to to do deals that we're just not willing to do. But when we meet a team and we really think that there is just unlimited potential, you you partner, you make it work. Yeah. What's your taste in founders? Uh, I would say I'm really drawn to founders who, you know, they do think in network effects and strategy and the kind of zero to one, how do you escape competition? Like they go through the mind maze, like you can just tell that like they've really obsessed over this. Uh, I'm drawn to founders that this is like a calling for them. like it is a you know I I kind of find that there's like some founders that it's almost like kind of a cool new job for them and there's some for whom it's an affliction and I I'm attracted to the founders for whom it's an affliction you know it's like this rash that they just have to scratch and that's going to make them run through whatever walls that they have to do uh and then you know just like the learning machine like the person who you know it's not about their ego. It's about just like what's the best thing for the company and how do I keep learning and evolving as a founder because as you know it's a really hard job. It's a really hard job and it always requires more of you like it's it's there's a relentlessness to it and if you know I have seen a failure case where somebody either it ends up being you know do you know the five temptations of a CEO that book incredible book um the hardest temptation is you know the founder is attracted to being a CEO because of status and then you you don't do the things that you need to do in order to build the best company possible. Uh or you know a founder that you know insecurity can drive you but it can also hold you back by not letting you grow and that that can be a challenge too. What are your tells? Um cuz you know you're a partner at a top firm. People are like probably always coming to you with their best best foot forward trying to trying to be like what you're looking for. Um what are what are some of the moments where you kind of can be like this is I can tell that this person has been through the maze and is thinking about stuff in this way in a way that's like uh it's genuine. It's not put on or I can tell that it's sort of like a calling or what Yeah. What are those little signals for you? Yeah. I just I find that um you know I I I ask a lot of questions um when I'm meeting with a founder and and learning about their business. And I know I'm always thinking about like I'm definitely the brain that is always thinking about that future and pulling it into the present. And when I speak to somebody and they're I hate to say this, but they're like, "Oh, that's a good question. I hadn't thought about that." or um you know it's just I'm bringing things that I I'm spending 30 minutes 60 minutes with a founder hearing the ideas for the first time and I'm bringing things to the table that they have not already thought about that's usually concerning right um and you're pretty smart so it's it's it's one of those things it can feel good like oh I asked them good questions but really you want you I when I was at Bessemer uh Jeremy Lavine uh he said that the best companies you want to be donut companies where you go to the board meeting you eat a donut and then you leave because they don't really need you. And so there's a little bit of that which is like, you know, I have some founders where I'll be thinking about something and I'll come to our one-on-one and like before I've even opened my mouth, they're already there asking like saying, you know, I've been thinking about this or I'm I reached out to this person and that's a pretty unique that's like a really incredible feeling when it happens. Yeah. I was I was talking to I think it was Reed Hoffman who was on this show who said um ideally it's someone where you you invest in them with the the the bar is like if you could come back in 5 years without having talked to them after the investment like and you would be pretty sure it would be going well. That's a good question to ask yourself, you know. Yeah. Yeah. Those are those those types of companies. I mean there's the founder but then there's also I know Reed and I know that he is very oriented towards network effects and that is I mean if you can find a business with a strong network effect like you're in you're going to be in pretty good shape. Let's talk about network effects because I think that um it's one of those things it's maybe a little less so because of AI stuff but like for the last 10 years um I would say like 80% of decks were like that I saw were like and we have a network effect. Um, and there are very I think there are probably very few businesses that truly have like that actual network effect pull. How do you differentiate? What does that really look and feel like? Yeah, in the early stages it's, you know, there's a lot of companies that have potential network effects and, you know, oftentimes there's a big gap between what's a theoretical, you know, and like where it really starts to happen. Um, and one of the things that, you know, I often think about is just like there are early, you know, we invest so early that a lot of it is leaning in on the theoretical, but like there are often signs that that you can look to. Um, the best thing that I you can sometimes see evidence of is just this idea of like a tipping point that starts to happen in a very small segment of the like where the white hot center of your market is. Um, you know, I there's like two examples I think about. Um but but they're outside of AI core like outside of core AI right now because it isn't I think what's happening right now with AI is that it's very much like a um kind of in a way what has been traditionally the software business which is just obsessing over a customer problem more and moving faster in your execution uh than any of your your competitors. Uh but um but you know I'm on the board of a company called Aentio which is a marketplace for creator like YouTube creators and and brands and you can see that like they this has been a market that has eluded startups for a long time because most of them have be like kind of fallen into the quicksand of becoming an agency. Yeah. But with LLMs, Aentio was able to automate a lot of the things that have held this market back and they're and they're truly like having liquidity. And one of the early things that was super interesting is just like you see the like the brands see creators and they see the the ads that they do for Aentio. And like so Aentio just has this demand side pull right now. Um, and it's super super early, but there was enough signal there that like something's working that's differentiated and there's no substitute for what they're doing that that you hope will start to really be a flywheel that can spin faster and faster. So, it seems like one of the best ways to differentiate between a real network effect and a fake one is like just early evidence. Yes. Are there any things like when you see a deck from a founder that hasn't, you know, they're just starting out and you're like and they say, "We're going to have a network effect and you're like it's not going to be a network effect." Yeah. I mean, it's it's often like they, you know, they'll articulate some kind of flywheel, you know, or it'll look like the Amazon or the Uber uh um flywheels and then as you and and either like it's just words on a slide that fit to a picture, but like the you know what I mean? But like the words don't actually they're not actually accelerants. Like that's I think that's one of the things like okay yes that cla that that whatever you say is true but it doesn't really actually accelerate the flywheel. Um and the second thing is that often there's either a lot of friction embedded in any one any leg of that flywheel or there's like offshoots that happen. Um, but I think the biggest thing is like when you really look at like what the articulation is of the flywheel that it is it's words but not accelerators. Yeah. to bring it back to AI for a second. I feel like one thing that you're articulating is there is a uh there was a a moment in software for like 10 or 15 years where everyone was chasing network effects and then the LLM wave happened and a lot of that has been um more single player or if it's if it's collaborative it's like inviting teams or whatever but you're not doing it together. Um and and the game there has been better performance from more money and more compute and more more data basically and everyone's just trying to keep up along that same sort of dimension of performance more or less. There are a couple other like examples that are not on that but um and I think what what you're maybe pointing to is that fairly soon if not already um probably the models the base models are good enough for consumers that there's going to be another wave of more consumerfocused more product geniusled AI applications that differentiate or grow from network effects and multiplayer that we're not possible in the last couple years but are newly about to be a thing. That is my great hope. I I could be tilting at windmills, you know, like consumer as you know has been really really hard over the last 10 years and so it really could be tilting at windmills. I believe that that is an opportunity. And what I would also say is that there are going to be a lot of companies that emerge that aren't multiplayer, that are single player. Um, and those will those could be really good. But I think that the really big opportunity that that lets a company have a true network effect is going to be something that's multiplayer. If it didn't happen, why not? If it didn't happen, it would just be that the gravitational pull of the existing platforms is too strong. you and I like what would get us to you go from the habit we already have of using Chat GPT and of course the ecosystem that's going to form around it over time what they're able to charge for it versus like what a new company would have to charge for it like maybe they eventually go free because it's an ad supported whatever it may be also memory is a big is a big sort of lock in like it knows who I am and all my experiences yeah because you and I like we already have but we're not everybody Right. But there is that gravity that has always been true for the incumbent products. And so it could be that that gravity is is just too strong to to get the people who are like if you want this type of community to form, you're going to need somebody who is already actually a power user of chat GPT to want to share that in in on another platform. And you know that's hard that may be hard to create. I know this is a show about AI, but are you looking at or excited about anything that's not AI right now? I uh I'm a big believer in stable coins. Interesting. I would not have guessed. Okay. I'm on the board of a company called Chain Analysis and which is just, you know, so I've had a kind of seat in the crypto space and have been long-term believer in in in Bitcoin and some of the other cryptocurrencies. But when I think about kind of the existing financial infrastructure and I mean you know we're we're fil we're filming this on a day when the you know what existing financial infrastructure yes exactly the US dollar is on a little bit shakier ground than normal. Um but you know my mom's from Argentina and I can tell you everybody in Argentina wants a US dollar. Yeah. you know and but it's really hard to get them and the government has all types of incentives to keep you know the hard currency that they have of US dollars in their own bank you know because they have you know loans and everything else that they have to kind of stabilizes their own economy but then it holds Argentina and all these countries back from participating in the global economy because the US dollar is what you need to trade goods internationally like it's just the easiest medium medium of exchange, but it's really hard to get US dollars. Now, if you have a cryptocurrency that is a US dollar stable coin backed by US dollar, that opens up uh kind of a global economy and it also it is just so much faster. It's 247, a lot cheaper. You can do it hard to get US dollars in Argentina. Yeah. Um well it's it's been uh Malay is obviously changing a lot of things but um there the um there are different taxes around US dollars uh the you know there has been for a long time this is different now uh the exchange rate you get on the street the exchange rate you get when you go to your bank the exchange rate you get when you use your credit card um it's just like a very liquid market really like I remember going to Argentina you know, and like having somebody on a motorcycle come to exchange money, you know, like that's that's kind of what you would do. Um, and then again, the US government, I'm sorry, the Argentine government, they have US dollars in their own central bank and if I want to transact in US dollars, I need to get some of that US dollar from them. But that is a very precious resource to them. And so there's a lot of like process that you have to go through and time in order to get $10,000 US dollars. It's not an easy thing to do. And so it's um it's just there's a lot of friction. whenever there's a lot of friction, if somebody else can come in with a new product that removes that friction and then also just has the facilitation like just how much easier it is for you and I to do a peer-to-peer transaction with Tether or USDC that creates a lot of liquidity in a market that I think can be a very interesting future. And also I should say it's like if you're in Argentina and you want to buy something from India like you know the number all the middle men that you have to go through in order to do that transaction versus and like all the fees along the way versus a peer-to-peer transaction on US dollar stable coin is uh is it's a different game and this is another network effecty type business to you or no uh there there is there more network effects here because you know Tether as an example which is like the dominant uh stable coin right now just has more liquidity on all the exchanges and so it's a lot easier to go in and out of Tether than you know other other stable coins but USDC is pretty strong too so there's some there's definitely some network effects there um it's just easier if everybody it's not even just within the exchange but just you like in in these countries in in Nigeria and any high inflationary country right now where people have wanted to go from their fiat currency into a US dollar. uh you you have a wallet and it has Tether and you have Tether and it just becomes like more comfortable for us to all use it and then all the ecosystem around like there's so many crypto wallets and and different you know kind of new uh financial apps that are for getting your your paycheck but then also you can have your money in a US dollar stable coin and if you're already integrated with Tether or you're using bridge to to access tether. It just makes it a lot easier for people to get comfortable with one of them. How do you think about um let's say 5 years from now how AI will have changed uh your day-to-day as a VC and um the kinds of businesses and the kinds of funding models will have change in any way change the VC business model? I have been wondering about this lately. You know, one thing I've just been thinking about, and this is a little bit of a step back, but like there are some people that are really good at creating training data. Do you know what I mean? Like there's some people like someone was showing me I interviewed him uh James uh for my Substack and he showed me like this spreadsheet he creates of all the movies he's ever watched and his own review of it. Right. And so I don't know about you, I've never done that, but the people who are really good at creating training data can then have a more personalized, more valuable experience with an LLM. And so to your question, like one of the things that I've been thinking about is that there's there's a few things that we all have training data for. One is um you know, past decisions we've made and whether or not those were good decisions. Like I personally I so um Annie Duke inspired this. I create she wrote this book thinking in bets a premortem. So every time I meet with a company and I dig in on it a little bit I write to myself what I liked what I didn't like what the deal would have been and if I got yes why. If I got no if I got to no why. And so it follows that over time I'm going to be able to look back on that list and examine my decision-m process, right? And then as I dig in on future companies, like in my brain, I know that there's one example where I pass on a company because the valuation was too high. And that was a lesson to me of like a company ended up being a real success this company. And so that ended up being a lesson to me like if I like everything but the valuation, I should probably lean in. I remember that. But there's so many other examples in my thinking that if I can like examine my thinking as I meet a company and have it cross-examine me that I think I'll get to a better decision. Then there's also things like talent. Like what is one of the best things that we do on behalf of our companies? It's helped them make sure that they have the best team around them, right? And and the same thing like we're all fallable in our evaluation processes and what's you know a record of those decisions, interviews we did, like all those things. I got to imagine that's going to come into play. And then the third is just um you know I know there's some companies that are doing this really well which is just tracking talent globally and the movements of talent and what that ends up meaning and it follows that there should be at some point a score almost where you know from the angel investors that have invested in a company the talent that's there individual scores of their ability or signal when they choose a company that there will be like a a Rotten Tomatoes almost score for for companies that can surface opportunities. That's interesting. I want to go back to the decision-m thing cuz I I'm with you. Like I I record all this stuff. I record all my meetings and like there's just like a lot of stuff I think that you can do with AI and and improving your decision-m and I'm curious in VC in particular like how that works or how you avoid you know for example maybe you invest I've done this you invest in a founder uh with a re a highly technical background but it's in a field that requires more of a product genius and then you know now you have in your LLM it's like Well, be aware that like you know and then you know the next time you meet with a founder and it's like you know Sam Alman and Greg Brockman in 2016 or whatever like that thing is going to ding and be like technical founder like are you sure this is what you want to do like it depends on how the how the rule or how the lesson is written. Um and and I think the uh the broader question or problem is if you look at venture capital, there are very few venture capital firms, funds and individuals who are successful over a long period of time. It's very hard. Um which can tell you one of two things. either it's just luck, which I don't think so, or um the landscape changes so frequently that you get tuned, your taste gets tuned to a particular kind of opportunity that you're very good at finding, but then it sort of moves and changes changes and what what is good is also changes. Um, and all of that means it's like quite hard to um use past training data to uh make future decisions. How do you think about that? I think maybe that's what we're all hoping will give us job security in the future. Um, you know, uh, I remember when I was at Pinterest and I was responsible for all the discovery experiences and very early on I had to kind of localize Pinterest and so I had to figure out like, okay, Pinterest is in the United States. Now, what's Pinterest in Brazil or Japan or all these countries? And I was like thinking about the categories and all as as being different. I remember Ben saying to me he's like just assume it's going to be more similar than different. And I think that there's some, you know, first principles that that we reduce down to uh when you're making a decision like um Jim Collins like so much of what he wrote like I don't know how how long ago is still valid today like you know Porsche by for like we talk about so many of these things and and they're timeless like valuations change will how companies exit change? Yes. And that's why like you're not asking the LLM to give you the answer yes or no. You're asking it to probe your thinking, but I think it should be able to continue to do that. And and that's why we still have hopefully a job a few years from now um of ultimately being the decider. Well, you'll have to come back on the show in 5 years and we'll we'll see how things have changed. I I think you'll still have a job. Um but it might I think it might be different, too. It's going to be very different. It's going to be very different and it's hard to anticipate how it will be. Yeah. Uh well, Sarah, thank you so much for coming. This is a great conversation. Thanks for having me. Yeah. Yeah. I had a lot of fun. Oh my gosh, folks. You absolutely positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure, unadulterated knowledge bombs about Chat GPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat, craving for more. It's not just a show, it's a journey into the future with Dan Shipper as the captain of the spaceship. So, do yourself a favor, hit like, smash subscribe, and strap in for the ride of your life. And now without any further ado, let me just say Dan, I'm absolutely hopelessly in love with you.