Dan Shipper explores the paradox that AI, despite making skilled expertise cheap and automating knowledge work, has created more human work rather than less. Drawing on how Every uses AI agents (Codex, Claude Code) across coding, writing, design, customer support, consulting, and operations, he outlines the two shapes of agent work — delegating tasks to agents and collaborating with them inside tools — and argues that cheap competence produces more attempts, more generic output ("slop"), and higher demand for experts who can frame, review, and improve that work. Rather than replacing humans, agents move human work up a level in expertise. Based on Shipper's essay "After Automation" and his earlier argument for the "allocation economy." Chapters cover how Every uses agents, why every agent needs a human, why automation creates more work, what "slop" really means, what experts do next, and what to do about it. # Video Identity Extraction: "AI Was Supposed to Save Time. Why Am I Busier?" ## Explicit Self-Description Evidence ### Creator & Company Identity - **Role stated by creator**: Runs a company called Every: > "Just a little background on me and Every. I run this company called Every. We've been around for about 6 and 1/2 years." - **Company size and focus**: > "agents landed in our day-to-day work inside of a company of 25 people" > "We've been covering AI and agents and how agents might change work since 2022. So, for a long time, since before ChatGPT came out." ### Self-Described Authority / Positioning - **Claims early-adopter lab status**: > "I think we get to be a little bit of a lab for what is work going to look like." - **Claims early access to models**: > "We all get to test in models before they come out because we're good at testing them or we're good at telling model companies, 'This is what it's good for.'" - **Claims predictive track record**: > "I actually went on the Lenny podcast about 12 months ago and I predicted that Claude code is the most underrated tool for knowledge work. And I feel like a lot of that has started to happen." - **Prior body of written work referenced**: > "3 years ago I wrote this piece about what I call the allocation economy... We'll link to this as well." ## Claimed Scope of the Channel - **Topic scope**: AI's impact on knowledge work, automation, and the future of skilled labor — demonstrated through the creator's own company as a case study. - **Cross-format ecosystem claim**: The channel/video links to deeper written companions: > "there's a companion explainer, like if you want to read something really in-depth and read it with your agent, you can go there." > "if you're interested in this, we have a ton more on Every about this... we have a lot on Every for you to read." - **Prior video inventory referenced**: > "We have a couple of videos about that. Claudie runs our whole consulting business." ## Intended Audience - **Primary audience: highly skilled knowledge workers and AI-curious professionals**, evidenced by: > "what does it mean for the future of work for knowledge workers, especially highly skilled knowledge workers?" > "If you're a smart curious person who wants to do interesting work, it's a super tool for you." - **Audience anxiety explicitly addressed**: > "if you look at the public polling, a lot of people are very scared of it... that's super at odds with my actual experience" - **Engagement invitation to discussion**: > "please subscribe, please comment. Tell me if you disagree. Let's have a discussion about it." ## Observable Presentation Patterns (Demonstrated, Not Claimed) - **First-person operator narrative**: The video's evidence base is the creator's own workflows (Codex, Slack bots, P&L analysis, pull requests), not external research alone. - **Named internal tooling as proof points**: Claudie, Andy, Victor — used as concrete demonstrations of agent delegation. - **Commentary on public figures**: References Dario (Amodei) and Ken Griffin/Citadel as framing devices for the "AI replacing jobs" discourse. - **Advice-driven close**: Ends with an actionable directive — "just ride the models" — suggesting the channel blends analysis with practical guidance. - **Catchphrase framing**: "ride the models" functions as a memorable sign-off, repeated at the end. ## Claims vs. Demonstration Summary | Type | Examples | |---|---| | **Explicit creator claims** | Company identity (Every, ~6.5 years, 25 people), early access to models, past prediction on Lenny podcast, written companion pieces | | **Demonstrated patterns** | Deep personal familiarity with agent tooling, first-person case-study format, audience-addressed framing of job-loss fears, cross-promotion of written content on Every | # Delivery Observation: "AI Was Supposed to Save Time. Why Am I Busier?" ## Tone - **Conversational and direct**: first-person, plain-spoken, e.g. *"What I want to do for the rest of the video is unpack this paradox."* - **Provocative but grounded**: opens with a claim-counterclaim setup — *"you would think that that would replace experts. And the reality is it actually increases the demand for experts."* - **Occasional strong colloquial language**, censored in the transcript: *"I got super [ __ ] Claude-pilled,"* *"Holy [ __ ] this thing is really working."* ## Recurring Phrases & Verbal Habits - **"What I want to do..." framing** — repeatedly announces structure: - *"What I want to do for the rest of the video is unpack this paradox."* - *"So, what I want to do first is I just want to talk a little bit about how do we actually use agents?"* - *"But what I wanted to do here is take the big fear..."* - **"Holy [ __ ]"** — repeated exclamatory reaction (appears at least three times). - **"So," as a transition marker** — begins many sentences and segments ("So, that's what I want to do with this video," "So, why do we need more humans," "So, when I step back and think"). - **Signposting recapitulation** — restates progress mid-video: *"hopefully now what we've done is we have identified this paradox. We've gone into here's how work works with AI."* ## Teaching Habits - **Concrete internal examples**: names real tools and people at his company — *"Claudie, which is built internally... Claudie runs our whole consulting business,"* *"a guy named Nitesh, who's one of our senior AI engineers."* - **Analogies to explain concepts**: - *"the best corollary is going to be human manager skills"* (agents as management). - *"there's almost like this Zeno's paradox going on here where... you've got Achilles. Achilles is racing a tortoise."* - **Quoting named outsiders as evidence**: Dario Amodei's entry-level jobs claim; Ken Griffin: *"These are not mid-tier white-collar jobs. These are like extraordinarily high-skilled jobs being... automated by agentic AI."* - **Numbered argument steps**: *"Let's go through the argument. One, AI makes yesterday's human competence cheap."* - **Direct audience address and calls to action**: *"If you have not done it, you should just go do it right now,"* *"please subscribe, please comment. Tell me if you disagree."* ## Communication Patterns - **Self-referential credibility building**: cites his own track record — *"3 years ago I wrote this piece about what I call the allocation economy,"* *"I actually went on the Lenny podcast about 12 months ago."* - **Cross-promotion to written work** (repeated): *"there's a companion explainer... We'll put a link in the description,"* *"I would go read the article,"* *"we have a lot on Every for you to read."* - **Catchphrase-style closing**: *"And And remember, just ride the models"* — echoing the earlier advice *"just ride the models. As the models get better, you get more powers."* - **Concession-hedging**: qualifies claims to stay balanced — *"It's not like an exact transfer,"* *"this is not to say that jobs aren't going to change."* ## Stable vs. Isolated - **Stable patterns** (sufficient repetition): structural signposting ("What I want to do"), "So," transitions, concrete company examples, analogy-based explanation, and closing calls to subscribe/comment. - **Isolated moments**: the Ken Griffin quotation and the Zeno's paradox analogy each appear once — effective devices, but not established habits. The stammered *"And And remember"* at the end appears to be a one-off delivery slip. ## Uncertainty - No curated metadata was supplied, so pacing, visuals, and vocal delivery cannot be assessed — observations are drawn from transcript text only. # Video Position Record: "AI Was Supposed to Save Time. Why Am I Busier?" **Speaker:** Unidentified narrator (self-identifies as the person who runs "Every," a ~25-person company; references his own writing and podcast appearances). No curated speaker metadata supplied. ## Central Thesis - AI **makes skilled human expertise cheap and widely available**, yet rather than replacing experts, it **increases demand for experts**. The speaker frames this as a paradox he intends to unpack. - He cites this as at odds with public fear and elite commentary. ## The Paradox (as framed) - AI providing cheap competence should, by expectation, replace experts. - Observed reality at his company: **more hiring of humans even while using many agents**. - He notes tension with prominent external claims: - **Dario (Amodei)**: quoted as saying AI "could wipe out over 50% of all entry-level white-collar jobs." - **Ken Griffin (Citadel)**: quoted saying agents are automating "not mid-tier white-collar jobs… extraordinarily high-skilled jobs… by agentic AI." - Public polling shows many people scared of AI — which he says is "super at odds with my actual experience using this stuff." ## Background and Framing - Runs **Every**, ~6.5 years old; covering AI/agents since **2022**, pre-ChatGPT. - **~2023**: wrote about the **"allocation economy"** — argued future work with agents resembles **human management skills** (delegation, knowing when to micromanage, decomposing tasks). Not an exact transfer, but the best analogy. - ~A year ago became heavily invested in Claude ("Claude-pilled"), influenced by **Kieran Klaassen** (GM of Corra) coding without reading code via CLI in the "Sonnet 3.7 era." - Predicted on the **Lenny podcast** (~12 months prior) that **Claude Code is the most underrated tool for knowledge work** — a prediction he claims has largely come true. - Positions Every as an early-adopter "lab" testing pre-release models. ## Two Shapes of Agent Work ### 1. Delegation (well-predicted by discourse) - @-mention a bot in Slack to do work. - Tools used: internal bots **Claudie** (runs the consulting business), **Andy** (editorial assistant), and external tool **Victor**. - Tasks include marketing/brand research, A/B test recording for thumbnails, sending consulting proposals, first-draft client decks. ### 2. Agent orchestration (weirder, "more important") - Tools like **Codex** (furthest along) and **Co-work**: an agent living on his computer, GUI-accessible, with full computer and browser access. - He does inbox, writing, coding, and quarterly P&L analysis inside it. - Multiple agents running simultaneously, human deeply **in the loop**. ## Core Observation: Humans Remain Required - **Both forms only work with human involvement.** - Example: **Nitesh**, senior AI engineer, continuously fixes Claudie's weak points. Not a "puppet master" — Claudie provides leverage — but the agent doesn't run unattended. - Claim: **the further an agent is from human correction, the worse it gets** — directly contradicting the "set it up and it works" misconception. ## The Argument for More Human Work 1. **AI makes yesterday's competence cheap.** - Language models train on data; "data is the visible residue of competence." - Example: submitting a pull request used to be rare/expensive; now anyone can do it. 2. **Cheap competence → adoption skyrockets → glut of generic output** ("complete slop" to "a decent start"). - Default/lazy-prompt usage produces non-valuable work. 3. **Slop is not a specific artifact** (not m-dashes, not sentence structure, not "purple accents") — it's the sameness humans detect when a default tool produces similar output everywhere. 4. **Glut of sameness increases demand for difference → demand for experts**, who convert drafts into production-ready work. - Expert frustrations: "puts way more work on my plate to clean up all the slop." 5. **Experts respond by**: - Building **systems/processes** (e.g., CLAUDE.md files, repo rules, social contracts for PR submission; his "vibe coded" prototypes integrated later), or - Using cheap competence themselves **as a floor** — e.g., individuals at Every each running an entire software product alone, previously impossible. ## Qualifications and Limits Stated - Jobs **will change** — "My job is completely different." - Every's internal experience **may not generalize** to all parts of the economy or all job types. - **Zeno's paradox analogy**: humans as the tortoise with AI (Achilles) closing in; he says he doesn't think AI catches up, deferring detailed argument to a companion article. - His definition of **AGI**: something always on, always working, economically viable to keep running, open-endedly finding new goals. - Core claim held: **work is not economically valuable unless a human is involved**. ## Prescriptive Conclusion - Advice to viewers: **"Just ride the models"** — learn to use AI; as models improve, users gain more cheap competence. - Optimistic for "smart curious" people: more demand, more capability. - Points to further Every resources on benchmarks and AGI; invites disagreement in comments. ## Unresolved Tensions Preserved - Public fear and elite warnings (Dario, Griffin) vs. his first-hand experience — acknowledged but not reconciled within the video. - His admission that the full argument ("why AI never catches the tortoise") is deferred to a companion article, not made here. - Scope limitation explicitly flagged: conclusions drawn from a 25-person, AI-forward company. # Speaker & Role Attribution Record ## Video - **Title:** AI Was Supposed to Save Time. Why Am I Busier? - **Video ID:** `01a0f15a-8577-788f-bda5-f8e7a942da9e` ## Speakers ### Primary (sole) speaker: **Creator / Host** - Identified in transcript as the CEO of **Every** ("I run this company called Every"), speaking first-person throughout. - No other spoken voices appear in the transcript; the entire video is his monologue/essay. ### Referenced third parties (quoted or mentioned, not speakers) - **Dario (Amodei)** — quoted/paraphrased claim: AI "could wipe out over 50% of all entry-level white-collar jobs." - **Ken Griffin** (Citadel) — on-stage quote about "extraordinarily high-skilled jobs being automated by agentic AI." - **Kieran Klaassen** — Every employee, GM of Corra; cited as early Claude Code adopter. - **Nitesh** — Every senior AI engineer; maintains the Claudie agent. ## Referenced Sources & Claims - **The speaker's own 2023 article** on "the allocation economy" (link promised in description). - **Companion explainer article** on Every (link promised in description). - **Lenny podcast** appearance (~12 months prior) where he predicted Claude Code's value for knowledge work. - **Public polling** on AI job fears — referenced generally, no specific poll named. ## Internal Tools / Products Mentioned - **Claudie** — internal agent running Every's consulting business. - **Andy** — internal editorial assistant agent. - **Victor** — external agent tool (noted as having "raised a massive round"). - **Codex, Claude Code, Co-work, Slack bots** — external tools described. ## Promotional Segments (flagged) - **Companion article promotion:** repeated referrals to the in-depth explainer linked in the description. - **Every content promotion:** "we have a ton more on Every about this… a whole section about the benchmarks." - **Channel CTA:** "please subscribe, please comment." ## Availability & Uncertainty - **Comment availability:** Explicitly open — speaker invites comments and disagreement. - No curated metadata was supplied; all attribution above derives solely from the transcript. - Third-party quotes are as the speaker recounts them, not verified against original sources. What AI does is it makes skilled human expertise available to everybody for cheap. And you would think that that would replace experts. And the reality is it actually increases the demand for experts. What I want to do for the rest of the video is unpack this paradox. What actually happens after automation? And why is it that it seems like for companies like ours, which are supposedly at the edge of automating everything, there's actually a ton of human work to do and we're hiring tons and tons of humans even as we use a lot of agents. What actually is going on there? Cuz look at Dario. Dario's out there being like AI could wipe out over 50% of all entry-level white-collar jobs. Ken Griffin, who runs Citadel, which is a gigantic hedge fund. He's sitting up on stage talking about seeing an agent do some work. And he says, \>\> "These are not mid-tier white-collar jobs. These are like extraordinarily high-skilled jobs being I'm I'm going to pick a word, being automated by agentic AI." If even people like him are experiencing these frontier models and going like, "Holy \[ \_\_ \] I don't know if there's going to be work in the future." And then if you look at the public polling, a lot of people are very scared of it. And I think that's super at odds with my actual experience using this stuff. So, that's what I want to do with this video. And there's a companion explainer, like if you want to read something really in-depth and read it with your agent, you can go there. We'll put a link in the description for you. So, what I want to do first is I just want to talk a little bit about how do we actually use agents? How have agents landed in our day-to-day work inside of a company of 25 people? I think of us being this microcosm of what is possible if you're trying to push this to the maximum extent possible. And then we'll talk about, okay, based on this, why are things the way that they are and what does it mean for the future of work for knowledge workers, especially highly skilled knowledge workers? Just a little background on me and Every. I run this company called Every. We've been around for about 6 and 1/2 years. We've been covering AI and agents and how agents might change work since 2022. So, for a long time, since before ChatGPT came out. And 3 years ago I wrote this piece about what I call the allocation economy. And in that piece, and this is back when agents weren't really a thing. It was just ChatGPT doing little responses or maybe Copilot. In that piece, what I argued is that the way that we will probably work with agents in the future is we will work with them like managers do. So, the best corollary is going to be human manager skills. And if you're a good human manager, you learn to do things like delegate or be able to know when to micromanage or be able to split a task up into discrete units that you can give to different people to get a task done. Those are the kinds of skills. It's not like an exact transfer, but that's the best analogy for the kind of skill that's going to be most valuable in AI. I wrote that in 2023 or so. We'll link to this as well. And then about a year ago I got super \[ \_\_ \] Claude-pilled. And Kieran Klaassen, who also works at Every who's the GM of Corra, was suddenly coding with Claude code. This is in the maybe Sonnet 3.7 era was when we were first like, "Holy \[ \_\_ \] this thing is really working." He was suddenly coding without looking at the code. He was just in a CLI all day. I started using it for my own coding and then I started using it for just regular knowledge work. And I was like, "Holy \[ \_\_ \] I think this is the template for how you're going to do knowledge work going forward." I actually went on the Lenny podcast about 12 months ago and I predicted that Claude code is the most underrated tool for knowledge work. And I feel like a lot of that has started to happen. So, because of the kind of company that we are, everybody internally is really AI-pilled. We're all early adopters. And we all get to test in models before they come out because we're good at testing them or we're good at telling model companies, "This is what it's good for. This is what it's not good for." That kind of stuff. I think we get to be a little bit of a lab for what is work going to look like. In the same way that we've been able to see maybe how working with agents might feel 3 years ago, we're getting a lens into that now that I want to share. I think it has major implications for this paradox we're talking about. So, when I step back and think about how is work with agents starting to happen? I think there's two main shapes. And there's one shape that was very well predicted by the discourse and one shape that was not. The one that is very well predicted by the discourse is it is very common right now for us to, when we have work we want to do, just at mention someone in Slack and say, "Hey, can you do this work for me?" And that's not a person. It's a bot. It's an AI. Um we use a bunch of different AIs. Some of them are built internally. So, we have a couple We have one called Claudie, which is built internally. We have a couple of videos about that. Claudie runs our whole consulting business. We've got a one called Andy, which is uh an editorial assistant. We also have one called Victor that we've been using. Victor just raised like a massive round and they're doing a really good job with this kind of thing. So, we have that form factor. Delegate to an agent over Slack. It just goes and does things. It does everything from marketing and brand research to recording the AB tests for our YouTube thumbnails to sending out our consulting proposals to clients to putting together the first draft of decks for our clients. There's tons and tons of stuff that we do with that. So, that's one service. The other surface, which I think is weirder and more important, is there is starting to become with Codex and Co-work, but I think Codex is the furthest along on this paradigm. We're starting to see that what I would call agent orchestration software, which is basically what Codex is or what Cloud Code is or whatever, where each chat spins off an agent that's on your computer, that's becoming the operating system for work. Where all the work that I do is pretty much in Codex or Co-work. And it's an agent that lives on my computer that I access through a GUI that has access to all of my computer, that is connected to all my stuff, that and this is really important, has a browser in it. I do everything from my inbox. I write in there. I do a lot of coding in there. When the quarter ends for the business, I analyze our P&L in there, like all that kind of stuff. So, these are the two shapes of work. One is delegation and one is this agent orchestration system where you're sitting in there and you have multiple agents going at once. You're watching them work. You're very in the loop with them and they're very in the loop with you. It's like a true AI human collaboration type system. And I think those are both really interesting because what we find for both of these, the only way that it works is if a human is involved. One example, we have Claudi. Claudi runs our consulting business, like I said. Claudi works because there's a guy named Nitesh, who's one of our senior AI engineers. And Nitesh's job is basically just to see all the places in which Claudi isn't quite working, and then just be constantly helping Claudi work better. And it's not one of those things where he's a puppet master, and he's actually doing all the work. Claudi is actually giving him and the business a ton of leverage. But I think there's this misconception that once you set up an agent, it just works, and the reality is very different. The further away it's been from a human helping to correct it and helping to make sure that it's doing the right thing, the worse it gets. And you can see the same thing in this agent orchestration system, where when I have to do real work, it's so much better when I'm in the loop with my agent. When I'm on my computer in Codex, and that human collaboration bit is the thing that makes it so powerful. So, why do we need more humans if that's the case? Let's get into that. So, hopefully now what we've done is we have identified this paradox. We've gone into here's how work works with AI. You're either delegating fully to something in Slack, or you're going back and forth with a bunch of different agents on your computer, but both of those require you or someone else in some way to be intervening a lot. Okay, so why does this then make more work for humans? Let's go through the argument. One, AI makes yesterday's human competence cheap. So, language models are trained on data. Data is the visible residue of competence. And so, an example, the ability to write a pull request to submit code to a code base used to be rare and expensive. Now, literally anyone can do it. If you have not done it, you should just go do it right now on Cloud Code or Codex or whatever. It's now extremely easy. When you make competence cheap, when you make yesterday's competence cheap, adoption skyrockets. Everyone wants to do it. Everyone's making pull requests. Everyone's making images. Everyone's tweeting a bunch of stuff. The demand for cheap competence is super high, and so it pervades everywhere. And we see this internally all the time. Suddenly you have like ops people and customer service people who are submitting pull requests, or you've got marketers doing YouTube thumbnails, but then the designers are like, "Whoa." And what happens when you have all those people who want to do something and suddenly can do it with this cheap competence, you get this glut of new work that's kind of generic. It ranges from anywhere from it's complete slop to it's a decent start. We can work with this. But if you're not actually good at what you do and you're using these tools to just do the default thing, you're going to make something that's not that great. It's going to be more than you could do before, and that's amazing. But using it by itself with a default, the laziest prompt possible, is not going to actually produce work that's valuable. But there's a very popular term now, slop. And the thing that I think is important to understand is slop is not any one particular thing. It's not, "Oh, it just uses m dashes too much. We can get rid of that." It's not a certain sentence structure. It's not that Claude just uses purple accents too much on landing pages. It's what humans start to notice when you now have a tool that by default can produce the same kind of thing in a lot of different circumstances, we start to notice that, and we start to kind of smell a rat. Everything looks a little bit similar. When everyone has access to this tool with cheap competence, and you have this glut of new things on the market that are all sort of the same, you actually increase the demand for difference. And who do they call? Experts. What experts can do, for example, when anyone can write a pull request, is they can come in and say, "Yeah, I see the problem that you're trying to solve, and this is maybe a good first draft." But then they can take it and turn it into something that can actually be merged and deployed to production. That's why a lot of experts are like, "I don't like AI cuz it puts way more work on my plate to clean up all the slop." But there's a couple of different then responses to what do experts do now in this world? One is they get annoyed. But two, what was really interesting is now a lot of experts create systems to be able to absorb and leverage this new work. So, internally, for example, at every I can write pull requests for our products. The default pull request I'm going to write is probably not that useful. But, now we have systems, so that can be your cloud MD file or your repo rules, or just a social contract for how and when I submit pull requests. For example, one thing I do is I just make my own little vibe coded version so I can show people, and then it gets integrated into the tool later and into our product later. So, one thing experts do is they build systems and processes to handle all this new work and turn it into something amazing. Or, what they can do is take this yesterday's competence that is now available to everyone and use it themselves and stand on that as the floor and use it to do things that they would never have been able to do. For example, internally for us, we have several people who each of them runs an entire software product by themselves. That completely would not be possible before. There's lots of examples of that. And so, that is why, in practice, in skilled knowledge work domains, it actually means that there's a lot more work to do because every agent requires a human to be good. Everyone having access to yesterday's competence increases the amount of stuff that gets produced. You need experts to come in to help you make the stuff that gets produced and turn it into something that's not slop. One way to think about this is there's almost like this Zeno's paradox going on here where in Zeno's paradox, you've got Achilles. Achilles is racing a tortoise. The tortoise is ahead of Achilles, starts ahead cuz the tortoise is super slow. And the paradox asks, how could Achilles ever catch the tortoise? Every time that he goes halfway to the tortoise, the tortoise moves a little bit, and he can never close the distance. And there's something like that going on feels like in AI where we're like the turtle, and we start 50 yards ahead, and AI is just like hoovering everything up, and it's starting to nip at our heels. And the question is, is it going to catch us? I really don't think this is going to happen. Well, I have a much longer argument about how this all works. So, if you care about a lot of the details, I would go read the article. But, let's assume that we just have this super smart AI. This is my definition of AI. It's something that you never turn off. It's always working. It's always running. Once you do that and it's economically viable for you and you pay to keep it on all the time, I think that's AGI. It's open-ended, so it's finding new goals and doing new stuff. This is why in practice when you actually look at this, it does not eliminate human work. None of that work is really valuable unless a human being is involved. And this is not to say that jobs aren't going to change. My job is completely different. And there's lots of different parts of the economy. So, the stuff that we see internally at Every, it may apply in different ways to different kinds of jobs. But, what I wanted to do here is take the big fear that I think a lot of people have and subject it to the test of what actually do we see and what have we seen in the last couple years in companies that are really trying to do this well. And I think we see a clear story here that I think if people understood that better, it would change the discourse. And you may be sitting here being like, "Cool. I guess I understand that now, but what do I do?" And my big thing, I think the biggest thing, really, is just learn how to use them. Just ride the models. As the models get better, you get more powers. You get more cheap human competence packaged up that you can use for whatever you want. If you're a smart curious person who wants to do interesting work, it's a super tool for you to go do all that stuff. And there's going to be a lot of demand for you to do more of it. If you're interested in this, we have a ton more on Every about this. I've got a whole section about the benchmarks. Like, what does it mean when the benchmarks goes up? What happens when we hit AGI? So, if you have questions about all that stuff, we have a lot on Every for you to read. And I'd love to hear from you. This is a complicated topic. So, please subscribe, please comment. Tell me if you disagree. Let's have a discussion about it. And And remember, just ride the models.