The New Jobs AI Will Create - CourtneyR.dev's 2nd Brain

The New Jobs AI Will Create ![rw-book-cover](https://img.youtube.com/vi/WhAAKxPlMhw/maxresdefault.jpg) 🔗 **Source:** [The New Jobs AI Will Create](https://www.youtube.com/watch?v=WhAAKxPlMhw) > [!note]+ Document Notes > --- > tags: #artificial-intelligence #future-of-work #economic-transformation #job-creation #healthcare #demand-elasticity #human-premium > author: Nathan Lambert > source: YouTube > reference: https://www.youtube.com/watch?v=WhAAKxPlMhw > date created: 2024-12-19 10:30 > --- > # 🏷️ Title: The New Jobs AI Will Create > ## 🧰 Action Inbox > - [H1] Research demand elasticity patterns in your industry — identify which of the 6 types apply #artificial-intelligence > - [H2] Map human premium categories to current role — assess which values transfer beyond AI capability #future-of-work > - [H3] Explore continuous service models in your sector — look for possibility unlock opportunities #economic-transformation > ## 🧭 Table of Contents > - [[#📝 Summary]] > - [[#📖 Main Content]] > - [[#🗒️ Notes]] > - [[#📚 References]] > - [[#🗣️ Paraphrase]] > - [[#📰 Newsletter]] > - [[#✅ Action Items]] > - [[#🧪 Hypotheses]] > - [[#📊 Methodology]] > - [[#❓ Questions]] > - [[#🔮 Future Research]] > - [[#💡 Implications]] > - [[#🧠 Learning Styles Assessment]] > - [[#📊 Accessibility Assessment]] > - [[#🌍 DEIB Assessment]] > - [[#✅ Logical Validity]] > - [[#🛡️ DARVO Analysis]] > - [[#🎯 Conclusion]] > ### 🔗 References > - [AI Daily Brief](https://www.youtube.com/watch?v=WhAAKxPlMhw) > - [Alex Emas "What Will Be Scarce" Essay](URL) > ## 📝 Summary > - Nathan Lambert challenges the AI job apocalypse narrative by arguing AI will create new categories of employment through demand expansion > - The hidden assumption in job displacement theories is that demand stays constant while AI increases labor supply > - Six types of demand elasticity explain how AI enables market expansion: price, access, complexity, continuity, personalization, and relational > - The "human premium" - seven categories of value requiring human delivery - protects many roles from AGI replacement > - Healthcare case study demonstrates how continuous preventative care could create 276,000 to 1.2 million new jobs > - Bottom line: AI creates both affordability unlocks (cheaper existing services) and possibility unlocks (entirely new service categories) > ## 📖 Main Content > Lambert argues the AI industry has focused almost exclusively on which jobs are at risk rather than exploring what new employment categories will emerge. This represents a fundamental analytical failure that leaves optimists with only vague handwavy arguments about job creation. > The core flaw in job apocalypse narratives is treating AI purely as a labor supply story - more AI means cheaper labor and displaced workers. This assumes demand remains constant, embodying the "lump of labor fallacy" that there's a fixed amount of work to do. > Lambert identifies six types of demand elasticity that explain how increased AI-powered supply can actually expand markets: price elasticity (cheaper access), access elasticity (reducing scarcity and barriers), complexity elasticity (making opaque systems navigable), continuity elasticity (enabling always-on support), personalization elasticity (custom solutions), and relational elasticity (human-centered value). > These elasticities create two types of unlocks: affordability unlocks where existing services reach new buyers at lower prices, and possibility unlocks where entirely new service models become operationally viable for the first time. > ## 🗒️ Notes > #### ⏱️ [00:00](https://youtu.be/WhAAKxPlMhw?t=0) — [H1] AI jobs narrative shift beginning > - Key idea: Subtle movement away from pure job apocalypse thinking > - Evidence: "More people at least question the premise that AI and agents getting better means people will have less work" > #### ⏱️ [02:30](https://youtu.be/WhAAKxPlMhw?t=150) — [H2] Industry failure to explore new job categories > - Key idea: Massive imbalance between risk assessment and opportunity exploration > - Evidence: "Sheer tonnage of time spent on assessing which jobs are most at risk, compared to the almost zero time exploring what types of new jobs will be created" > #### ⏱️ [04:15](https://youtu.be/WhAAKxPlMhw?t=255) — [H1] Hidden assumption in job displacement narratives > - Key idea: AI analyzed only as labor supply increase, not demand expansion > - Evidence: "All of this rests upon the presumption that demand stays constant" > #### ⏱️ [06:45](https://youtu.be/WhAAKxPlMhw?t=405) — [H2] Six types of demand elasticity > - Key idea: Different ways AI-increased supply can expand market demand > - Evidence: "Price elasticity, access elasticity, complexity elasticity, continuity elasticity, personalization elasticity, relational elasticity" > #### ⏱️ [09:20](https://youtu.be/WhAAKxPlMhw?t=560) — [H2] Complexity elasticity example > - Key idea: AI helps navigate opaque systems people avoided > - Evidence: "Think help with taxes, insurance, immigration, benefits... making opaque systems navigable" > #### ⏱️ [12:10](https://youtu.be/WhAAKxPlMhw?t=730) — [H2] Small business professional services unlock > - Key idea: Affordability unlock activates long-tail market > - Evidence: "If AI reduces the cost of those jobs from 5,000 to 500 and 3,000 to 300, an enormous longtail market activates" > #### ⏱️ [15:30](https://youtu.be/WhAAKxPlMhw?t=930) — [H1] Possibility unlock vs affordability unlock > - Key idea: Two distinct ways AI creates new markets > - Evidence: "Affordability unlock is the same menu at a lower price, the possibility unlock is a new menu entirely" > #### ⏱️ [18:45](https://youtu.be/WhAAKxPlMhw?t=1125) — [H1] AGI objection and capability vs service design > - Key idea: Jobs question is not just about AI capability but service delivery requirements > - Evidence: "Better question I believe is does AI only delivery satisfy the demand?" > #### ⏱️ [21:00](https://youtu.be/WhAAKxPlMhw?t=1260) — [H1] Seven categories of human premium > - Key idea: Value that remains attached to human involvement despite AI capability > - Evidence: "Relationship, embodied presence, trust, accountability, translation, behavior change, provenance and status" > #### ⏱️ [24:30](https://youtu.be/WhAAKxPlMhw?t=1470) — [H2] Healthcare has every demand elasticity type > - Key idea: Healthcare demonstrates all six elasticity categories > - Evidence: "Healthcare has potentially every type of demand elasticity that we discussed before" > #### ⏱️ [27:15](https://youtu.be/WhAAKxPlMhw?t=1635) — [H2] Continuous preventative care paradigm > - Key idea: AI enables shift from episodic reactive to continuous preventative healthcare > - Evidence: "AI enabled version of healthcare that is continuous, preventative and personalized" > #### ⏱️ [30:00](https://youtu.be/WhAAKxPlMhw?t=1800) — [H2] Continuous care navigator role > - Key idea: Human layer between patients and AI monitoring systems > - Evidence: "276,000 jobs in conservative case, 1.2 million in aggressive case" > #### ⏱️ [34:20](https://youtu.be/WhAAKxPlMhw?t=2060) — [H3] Care plan outcome specialist role > - Key idea: Human interface for care plan implementation and problem-solving > - Evidence: "Owns the gap between medical advice and real world execution" > #### ⏱️ [37:45](https://youtu.be/WhAAKxPlMhw?t=2265) — [H3] Health data operations specialist > - Key idea: Technical role ensuring reliability of AI-enabled healthcare data > - Evidence: "20,000, 30,000, 40,000 jobs... owns reliability, integration, governance" > #### ⏱️ [41:10](https://youtu.be/WhAAKxPlMhw?t=2470) — [H3] Six broad categories of new roles > - Key idea: Patterns of job creation across industries > - Evidence: "Navigators, continuous support workers, AI augmented service operators, data and operations specialists, QA safety and compliance, escalation specialists" > ## 📚 References > - [AI Daily Brief YouTube Channel](https://www.youtube.com/@aidailybrief) > - [Alex Emas Research on Relational Economy](URL) > - [Labor Economics Literature on Technological Change](URL) > ## 🗣️ Paraphrase > - AI job discussions focus too heavily on displacement risk and ignore job creation opportunities through market expansion > - The assumption that demand stays constant while AI increases labor supply is fundamentally flawed and ignores historical patterns > - Six types of demand elasticity show how AI can expand markets: making things cheaper, more accessible, less complex, continuous, personalized, and more relational > - Human premium categories like trust, accountability, and relationship preserve job value even when AI can perform underlying tasks > - Healthcare demonstrates how AI could create hundreds of thousands of new jobs in continuous care, implementation support, and data operations roles > ## 📰 Newsletter > **AI's Job Creation Opportunity: Moving Beyond Displacement Fears** > The AI job debate has been stuck in apocalypse mode, but Nathan Lambert offers a refreshing economic perspective that could reshape how we think about AI's impact on work. > Lambert argues we've been asking the wrong question. Instead of just "Can AI do this job?", we should ask "Does AI-only delivery satisfy market demand?" This shift reveals how AI creates new employment through demand expansion, not just job displacement. > Key insight: Six types of demand elasticity show how AI enables market growth - making services cheaper, more accessible, less complex, continuous, personalized, and relationship-focused. Healthcare exemplifies this with potential for 276,000 to 1.2 million new jobs in continuous preventative care. > The "human premium" - seven categories of value requiring human delivery including trust, accountability, and relationships - protects many roles from AI replacement while creating entirely new job categories. > **Resources:** [Learn WordPress](https://learn.wordpress.org/), [Developer Docs](https://developer.wordpress.org/), [WP-CLI](https://wp-cli.org/) > ## ✅ Action Items > #### DevRel > - Developer advocacy: Create content on AI-augmented developer workflows, present at conferences on human-AI collaboration in coding > - Education: Develop workshops on prompt engineering for developers, AI pair programming best practices > - Developer experience: Research how AI tools can improve documentation, SDK usage patterns > - Community management: Survey developer attitudes toward AI tools, moderate discussions on AI impact > #### OSPO > - Strategic Alignment: Track AI adoption patterns in open source projects, measure impact on contribution models > - Developer Support: Provide training on AI-assisted development, evaluate AI coding tools for enterprise use > - Community Engagement: Fund research on AI's impact on maintainer workload, open source sustainability > #### Community > - Contributors: Create pathways for AI-assisted contributions, first-issue programs leveraging AI tools > - Ecosystem: Coordinate cross-project standards for AI tool integration in development workflows > #### WordPress > - Core AI: Develop guardrails for AI-generated content, create evaluation frameworks for AI features > - Training: Design lesson plans for AI-assisted WordPress development, content creation workflows > - Community: Support meetups on AI tools for WordPress developers, content creators > - Documentation: Use AI to improve DevHub content generation while maintaining human editorial oversight > ## 🧪 Hypotheses > - Industries with high complexity elasticity (legal, healthcare, finance) will see more AI-human hybrid roles emerge than those with primarily price elasticity > - Roles with multiple human premium categories (trust + accountability + relationship) will be more resistant to AI displacement than those with single premiums > - Possibility unlocks will create larger employment categories than affordability unlocks in sectors where continuous service models weren't previously viable > - The transition period between affordability and possibility unlocks will determine whether AI adoption creates net job growth or displacement in specific sectors > ### 📊 Methodology > - Industry analysis comparing current service delivery models to AI-enabled continuous models across healthcare, legal, education, and financial services > - Survey research on consumer willingness to pay for human premium categories in different service contexts > - Economic modeling of demand elasticity coefficients for professional services at different AI-driven price points > ### ❓ Questions > - How do we measure and validate the seven human premium categories across different industries and cultural contexts? > - What policy interventions could accelerate the transition from displacement to job creation during AI adoption waves? > - How might the timeline and sequence of AI capabilities development affect which possibility unlocks become viable first? > ### 🔮 Future Research > - Longitudinal study tracking job category evolution in early AI-adopting industries like customer service and content creation > - Cross-cultural research on human premium valuation differences across global markets > - Economic impact modeling of continuous service paradigms across multiple sectors beyond healthcare > ### 💡 Implications > - Practice: Service businesses should identify which demand elasticity types apply to their market and design AI-human hybrid offerings accordingly > - Policy: Workforce development programs need to focus on human premium skills rather than just technical AI literacy > - Education: Business schools should teach demand elasticity analysis as core framework for understanding AI economic impact > - Funding: Investors should evaluate AI startups based on their ability to unlock new demand categories, not just automate existing processes > ### 🧠 Learning Styles Assessment > Reading level estimate: Grade 14-15 (College/Graduate level) > #### 👀 Visual learners > - Present: Economic frameworks clearly explained with categorization > - Improve: Demand elasticity diagrams, healthcare system flow charts, job growth visualizations > #### 🔊 Aural learners > - Present: Full video presentation with clear verbal explanation > - Improve: Podcast-style discussion with industry experts on specific sectors > #### 🗣️ Verbal learners > - Present: Strong use of analogies (menu metaphors), clear definitions of technical terms > - Improve: More concrete industry examples beyond healthcare > #### 🧩 Kinesthetic learners > - Present: Detailed case study walkthrough with specific calculations > - Improve: Interactive worksheets for analyzing demand elasticity in different industries > #### 🧠 Logical learners > - Present: Systematic framework with numbered categories, clear cause-effect relationships > - Improve: Economic modeling tools, quantitative analysis templates > #### 🧑‍🤝‍🧑 Social learners > - Present: YouTube format encourages discussion and community engagement > - Improve: Discussion guides for industry groups, peer analysis exercises > #### 🧘‍♀️ Solitary learners > - Present: Comprehensive framework for independent analysis > - Improve: Self-assessment tools for evaluating personal career implications > #### 🌿 Naturalistic learners > - Present: Healthcare ecosystem thinking, economic system interconnections > - Improve: Broader connections to economic ecosystem health, sustainability implications > ### 📊 Accessibility Assessment > #### Screen reader semantics > - Present: Clear verbal presentation in video format > - Improve: Transcript with proper heading structure, alt text for any visual frameworks > #### Keyboard navigation > - Present: Standard YouTube video controls > - Improve: Chapter markers for easy navigation to specific topics > #### Contrast and readability > - Present: Clear audio delivery > - Improve: High-contrast transcript formatting, readable font sizes > #### Media and captions > - Present: YouTube auto-generated captions available > - Improve: Professional captions with technical term accuracy, downloadable transcript > ### 🌍 DEIB Assessment > #### Cultural breadth > - Present: Economic framework applicable globally > - Improve: Examples from different cultural contexts, consideration of varying labor market structures > #### Inclusive language > - Present: Professional, accessible economic terminology > - Improve: More diverse industry examples beyond healthcare, varied demographic scenarios > #### Neurodiversity support > - Present: Clear logical structure, systematic framework presentation > - Improve: Multiple format options (audio, text, visual), consistent pacing > #### Representation > - Present: Focus on economic analysis rather than demographic specifics > - Improve: Examples considering different socioeconomic situations, global labor market variations > ### ✅ Logical Validity > #### Strong reasoning > - Deductive strength: Solid logical progression from demand elasticity principles to job creation predictions > - Inductive strength: Good use of historical precedent regarding technology adoption and job creation > - Causal reasoning: Clear causal links between AI capability improvements and demand expansion mechanisms > - Analogical strength: Effective use of menu analogy for understanding affordability vs possibility unlocks > #### Fallacies > - Hasty generalization: Healthcare case study generalized to all industries without sufficient evidence for universal applicability, needs more cross-sector validation > - False dichotomy: Presents AI impact as either displacement or creation, could better address mixed scenarios where both occur simultaneously > #### Tone of voice > - Overall tone: Professional and optimistic while acknowledging complexity, uses > --- > ## Full Transcript > [00:00] Today on the AI Daily Brief, the new [00:02] jobs AI will create. The AI Daily Brief [00:05] is a daily podcast and video about the [00:07] most important news and discussions in [00:08] AI. [00:10] All right, friends. One of the big [00:12] points of discussion for this week has [00:13] been the idea that there is this ever so [00:15] subtle shift in the AI jobs narrative. [00:18] It would be going way too far to say [00:20] that this was becoming anywhere near [00:21] mainstream, but you're starting to see [00:23] more people at least question the [00:24] premise that AI and agents getting [00:26] better means people will have less work. [00:28] And yet, even among those who are [00:29] arguing that the AI job apocalypse [00:32] narrative is way overblown, the argument [00:34] tends to be much more backwards looking. [00:36] It's looking at the way that [00:38] productivity has previously impacted [00:39] industries and extrapolating that out to [00:41] the future. That's well and good and all [00:44] of that is important, but I think it is [00:46] a significant failure of the AI industry [00:48] to take the next step and actually start [00:50] to explore the type of jobs that there [00:53] will be in an AI enabled future. The [00:56] sheer tonnage of time spent on assessing [00:59] which jobs are most at risk, compared to [01:01] the almost zero time exploring what [01:04] types of new jobs will be created [01:06] represents one of our great failures and [01:08] leaves people who want to be optimistic [01:10] about the future clinging to vague [01:12] handwavy notions about what those jobs [01:14] might be. Now, from the standpoint of [01:16] sheer epistemic humility, of course, we [01:18] have to be careful about arguing with [01:20] any sort of confidence what specific [01:22] things will be created in the future. In [01:24] other words, the more specific our [01:26] future predictions, the less likely to [01:27] be right they are. But that doesn't mean [01:29] that we can't at least explore from [01:31] first principles how we think the change [01:33] is going to play out if indeed it is not [01:35] going to be the mass destruction of [01:36] white collar work that some are [01:38] promising. So what I'm going to attempt [01:40] today is talk about one the fundamental [01:42] problem and the hidden assumption in [01:44] most of the job apocalypse narratives. [01:46] Two, what opens up when you correct that [01:49] assumption. Three, how to deal with the [01:51] AGI objection. and four, a real walk [01:55] through a specific sector and some jobs [01:57] that I could see very plausibly being a [01:59] part of the future and representing [02:00] meaningful amounts of employment. So, [02:02] let's talk first about that hidden [02:04] assumption. AI is mostly analyzed right [02:06] now as a labor supply story. In other [02:09] words, AI increases the supply of labor, [02:12] so labor gets cheaper and workers get [02:14] displaced. But all of this rests upon [02:16] the presumption that demand stays [02:19] constant. This is another way of framing [02:21] the lump of labor fallacy. the idea that [02:23] there is a set amount of work to do. So [02:25] if technology comes and does some of [02:27] that work, that means that humans aren't [02:29] doing that work, which means that those [02:30] humans don't have any work to do. What [02:32] it doesn't take into account is the [02:34] expansionary nature of economies and the [02:36] ability of demand to expand to absorb [02:38] more supply. Now, I don't want to spend [02:41] a lot of time on the historical [02:42] arguments here. As I mentioned, that's [02:44] the main substance of pretty much all [02:46] other arguments along these lines. Yet, [02:48] it is worth noting, of course, that the [02:49] assumption that demand stays constant as [02:51] the supply of labor increases has never [02:53] held true. And so, if we are just going [02:55] from historical precedent, it is [02:57] unlikely to be true this time either. [02:59] The more important question, if we're [03:01] trying to actually interact with [03:02] people's lives as they're lived now, not [03:04] just provide a history lesson, is to ask [03:07] where demand will expand. Basically, [03:10] what parts of the economy could actually [03:13] grow as AI allows us to produce more? [03:16] And I think what's important to note is [03:17] that demand elasticity actually comes in [03:20] a variety of different forms. The one [03:22] that's most obvious is of course price [03:23] elasticity. I.e. I wanted the thing but [03:26] it costs too much. If AI makes a thing [03:29] cheaper, new buyers can enter the [03:30] market. A second category of demand [03:33] elasticity is access elasticity. I [03:36] wanted it but I couldn't get it. Now, [03:39] whereas AI supported price elasticity [03:41] because it lowered the cost floor, in [03:43] this case, we're talking about AI [03:45] reducing provider scarcity or weight [03:47] times or geographic barriers or other [03:50] types of institutional bottlenecks. [03:52] Basically, instead of it just being [03:54] cheaper, AI makes a good or service more [03:56] readily available. A third type of [03:58] demand elasticity is complexity [04:00] elasticity. I needed it, but the system [04:02] was too confusing. Think help with [04:04] taxes, insurance, immigration, benefits. [04:07] The way that AI supports this type of [04:08] demand elasticity is by making opaque [04:11] systems navigable. You're already [04:13] starting to see this as a major way that [04:15] people are consuming AI, interacting [04:17] with legal ease or contract things or [04:19] even doctor's recommendations they [04:20] didn't really understand before, but now [04:22] have the sherpa and guide that can help [04:23] reduce that complexity. Another category [04:26] of elasticity we'll call continuity [04:28] elasticity. And here perhaps we're [04:30] getting a little bit more abstract, but [04:31] I still think it's worth noting. The [04:33] idea of continuity elasticity is going [04:35] from I get help occasionally to I'd [04:37] benefit from help all the time. So think [04:40] about areas like health or coaching [04:42] where always on monitoring and support [04:44] could become cheap enough to operate at [04:46] scale in a way that wasn't possible [04:48] before. A fifth form of demand [04:50] elasticity we'll call personalization [04:52] elasticity. This is I get the generic [04:55] version now but I'd value something made [04:57] for me. When AI brings the cost and ease [05:00] of production down, it's going to make [05:02] customization cheaper. The last category [05:05] we'll call relational elasticity or [05:07] value elasticity. And the idea is I want [05:10] this thing that I am buying, whether [05:11] it's a good or a service to be more [05:13] human, meaningful or trusted. This is [05:15] what Chicago Booth economist Alex Emas [05:18] wrote about in his what will be scarce [05:19] essay. how the relational sector where [05:22] the provenence of a good or service how [05:24] it was made and who made it and who is [05:26] providing it are actually an integral [05:28] part of the value of the service i.e. [05:30] people deciding that they don't just [05:32] want a coffee, they want an experience [05:33] surrounding that coffee that involves [05:35] interaction with a human and a [05:36] particular quality that comes from some [05:38] sort of human touch. So, we've got these [05:40] different categories of how AI's impact [05:43] on the supply of things in terms of cost [05:45] of production, ease of production, speed [05:47] of production, etc. can interact with [05:49] elasticity of demand to expand demand in [05:52] new and clear ways. And one thing that's [05:54] interesting to look at as we try to [05:56] understand AI's impact on different [05:57] sectors, especially the positive and [05:59] generative impact of AI, is to ask which [06:02] of those elasticities are common in [06:04] those sectors. They're present pretty [06:06] much everywhere, but different sectors [06:08] are going to have different elasticities [06:09] more strongly represented. For example, [06:12] in experiences and travel, that [06:14] relational elasticity and [06:15] personalization elasticity are going to [06:17] be particularly high, but you're not [06:18] likely to see continuity elasticity [06:20] because you're not able to have [06:21] experiences or travel continuously [06:23] throughout your life, as awesome as that [06:24] would be. Meanwhile, on the other end of [06:26] the spectrum, healthcare, I think, has [06:28] every single one of these types of [06:30] elasticity, which is why it shows up as [06:32] our case study in a little bit. Now [06:34] trying to zoom out and consolidate this [06:37] argument, I think we can look at AI as [06:39] expanding the demand frontier in two [06:41] directions at once. It either is [06:44] increasing and democratizing access to [06:46] things that are available now or [06:48] unlocking things that aren't possible [06:49] now. If you go back up to the types of [06:52] elasticity, you'll note that in many of [06:53] these cases, if you were currently rich, [06:56] you could already get access to the [06:58] thing in the way that the people want it [06:59] now under the conditions of the new [07:01] elasticity. If you're rich, your word is [07:03] price sensitive. You could probably pay [07:04] to solve access issues and complexity [07:06] issues. If you're rich enough, you could [07:08] even use money to solve continuity [07:10] issues, and you could probably pay for [07:11] personalization and relational value as [07:13] well. So, the first unlock of AI is the [07:16] affordability unlock. Basically, the [07:18] same menu, but at a lower price. And [07:20] that's most of what you were probably [07:21] thinking about when I was describing [07:22] these six categories of demand [07:23] elasticity. To take one example, let's [07:26] think about small business professional [07:27] services. Right now, the average small [07:30] business owner might not have the budget [07:32] to buy a $5,000 design project, a $3,000 [07:35] marketing campaign, a $2,000 legal [07:37] review, and a $1,500 analytics report. [07:40] This obviously does not mean that the [07:41] business doesn't have demand for design, [07:43] marketing, legal, and analytics. It [07:44] means that at the current state, the old [07:47] version of those services was [07:48] unaffordable. If AI reduces the cost of [07:51] those jobs from 5,000 to 500 and 3,000 [07:54] to 300, an enormous longtail market [07:57] activates. Millions of small businesses [07:59] that were never agency clients before [08:01] become buyers for the first time. This [08:03] is again what I'm calling the [08:04] affordability unlock where there is net [08:07] new demand for services that already [08:08] exist because the changing cost and [08:10] availability of provision opens up new [08:12] buyers for which that service wasn't [08:13] viable before. The second type of unlock [08:16] I'm calling the possibility unlock. To [08:18] use the menu analogy, if the [08:20] affordability unlock is the same menu at [08:22] a lower price, the possibility unlock is [08:24] a new model entirely. This is where AI [08:26] makes a service model operationally [08:28] possible that could not previously exist [08:31] at scale. The category itself becomes [08:34] possible before anyone can demand it. [08:37] Previewing where we're going to get in [08:38] our case study, an example of this, I [08:40] think, is continuous preventative [08:42] healthare. The vast majority of people, [08:44] not named Brian Johnson, don't currently [08:46] have someone continuously watching their [08:48] health data or tracking their care plan [08:50] or noticing drift or coordinating with [08:52] their pharmacy. And to be clear, people [08:54] aren't demanding this exact service. [08:56] This isn't a normal category because it [08:57] hasn't been operationally viable. But as [09:00] AI collapses the cost of the [09:02] informationational layer around care, [09:04] data collection, monitoring, [09:06] summarization, documentation, [09:08] scheduling, escalation, routing, once [09:10] all of those costs fall and the systems [09:12] around them become viable, a totally [09:14] different healthcare model becomes [09:16] possible. One that is personalized, [09:18] datadriven, preventative, and [09:19] continuous. This is entirely net new [09:23] demand because no one had this before [09:25] because it wasn't possible until AI made [09:28] it possible. When it comes to new jobs, [09:31] the affordability unlock opens up whole [09:33] new categories of providers that cater [09:35] to different parts of the long tale of [09:37] the market. The possibility unlock [09:39] creates entire new markets which entire [09:42] new actors will come into to provide [09:43] those goods and services that are now [09:45] actually possible. But now we have to [09:48] get to the question that stops all of [09:50] these conversations midtrack. I would [09:53] argue that the number one reason that [09:55] people claim this time is different and [09:57] that AI is not a so-called normal [10:00] technology is that they say the people [10:02] that are optimistic about the jobs [10:04] created in the future aren't answering [10:06] the question, won't AGI just eat those [10:09] jobs, too? In other words, for those [10:12] small business professional services, if [10:14] the first wave of AI brings cost down by [10:16] a factor of 10 and makes those $5,000 [10:18] jobs into $500 jobs, is that just a [10:21] temporary state on the path to AGI that [10:24] can do those jobs for 50 cents instead [10:25] with no human involved? To be clear, [10:29] from a first principles perspective, [10:31] this is a reasonable question and one [10:33] that again I think AI optimists are [10:35] failing to engage with clarity and good [10:37] faith. For my part, I think that there [10:40] are actually quite clear answers. The [10:43] question that we are asking, which I [10:44] think is the wrong question and the idea [10:46] that is embedded in the assumption that [10:48] AGI will just eat those new jobs too, is [10:51] we think about the question of jobs only [10:53] as a question of can AI perform the [10:55] task. It's a capability question that [10:58] assumes labor demand is grounded [10:59] entirely and only in what humans can do. [11:03] But that's too narrow. A better question [11:05] I believe is does AI only delivery [11:08] satisfy the demand? Here we've moved [11:11] from a capability question to a service [11:13] design question. Many roles exist not [11:17] because of capability gaps but because [11:19] of the constraints of the market's [11:21] expectations in how a service is [11:23] delivered. Things like trust, [11:25] accountability, presence, relationships. [11:27] They are part of the value. And while [11:29] AGI can eat tasks and will eat many [11:32] tasks, it will not automatically eat [11:34] that demand for things like trust and [11:36] accountability. To try to put a name on [11:38] this, I'm calling it the human premium. [11:41] These are seven categories of value that [11:43] do not a priority transfer when you [11:45] remove the human. The human premium is [11:48] the portion of economic value that [11:50] remains attached to human involvement [11:52] even when AI can perform the underlying [11:54] tasks. It is the answer to why doesn't [11:57] AI eat those jobs too. Here are seven [12:01] categories of human premium. The first [12:04] is relationship or relational. Again, [12:06] this is the one that Alex Emos explored. [12:08] The idea that I want this delivered by [12:10] someone who knows me, where there is [12:12] continuity, memory, accumulated trust, [12:15] where the human part of the experience [12:16] actually matters and is integral to the [12:18] experience, where replacing a person [12:21] with an AI makes the service worse [12:23] enough that it actually decreases demand [12:24] for that service. A second kind of [12:27] related category of human premium we'll [12:29] call embodied presence, where the demand [12:32] constraint is I want someone there with [12:34] me. This is where physical presence [12:37] matters. The nurse in the room, the [12:39] trainer correcting form. It's not that [12:42] AI can't do some of the things that [12:44] those people do. It's that the physical [12:47] presence is important enough to many [12:49] that there continues to be demand for [12:51] that human premium of embodied presence. [12:54] A third category of human premium is [12:57] trust. I want to talk to a person before [12:59] I act. Humans are social creatures. We [13:02] value social proof. We value the [13:04] experience of people who have come [13:05] before us. We value understanding the [13:07] context of the personal experience that [13:09] we are trusting of people who came [13:10] before us. As much as AI generates [13:12] recommendations, there are many [13:14] situations in which people will [13:16] absolutely prefer the recommendation of [13:18] a human, even if that human is just [13:20] validating the AI that they're working [13:22] with too. In many circumstances, humans [13:24] will be asked to make AI generated [13:26] recommendations believable, emotionally [13:29] acceptable, and actionable. Which gets [13:31] us to of course the fourth category of [13:32] human premium which is accountability [13:34] where the demand constraint is someone [13:36] has to own this. People want a person [13:39] who signs off, escalates, explains and [13:41] is responsible when things go wrong. [13:44] When I'm taking an experimental new [13:45] peptide regime, which is all the rage [13:47] right now, I don't care if Chat GBT can [13:49] tell me how to titrate things correctly [13:50] at the right ratios. You better believe [13:52] I want someone whose bonafides and [13:54] credentials I can check and who to put [13:56] it crassly I can sue if something goes [13:58] wrong. The questions of legal [14:00] accountability for AIS are going to make [14:02] a lot of lawyers very rich for a very [14:04] long time. But even outside of questions [14:05] of the legal system, there is going to [14:07] be a human premium on accountability for [14:10] sure. A fifth area of human premium is [14:13] translation. AI is interacting with the [14:15] entire accumulated knowledge of the [14:17] world. But some people aren't going to [14:18] know how to ask for what they need. Even [14:20] in a world of AGI, they will want human [14:23] translators. people who can turn messy [14:25] desire and constraint into usable AI [14:27] mediated work. This I think is extremely [14:31] important for the example of small [14:32] business professional services. The AGI [14:35] eats everything answer to this that [14:37] won't it just compress that $500 job [14:39] into a 50cent job eventually that takes [14:41] the job of the intermediary actor fails [14:43] to understand the economic value of this [14:45] translation. A small business owner [14:47] might not want or have time to get good [14:50] enough at using AI for design to take [14:53] advantage of the 50cent cost that AGI [14:55] makes it. The market will find the price [14:58] level at which a human can upsell the [15:00] 50cent AI work in a way that fits better [15:03] with the process and constraints of that [15:05] small business owner. It is a failure of [15:07] economic understanding to assume that [15:09] there is no room for margin on top of [15:11] the underlying cost of goods sold. AI [15:13] might dramatically compress that margin, [15:15] but there will be margin there. A sixth [15:18] category of human premium we'll call [15:19] behavior change. That's where I know [15:22] what to do, but I need help doing it. [15:24] Anyone who has tried to use a chat GBT [15:27] workout plan only to abandon it or at [15:30] least integrate it with a human trainer [15:32] who can help them stay accountable to [15:33] themselves will understand what I'm [15:35] talking about here. Simply put, there [15:38] are certain categories of behavior [15:39] change for which we will listen to a [15:40] human and we will not listen to an AI. [15:43] The last category of human premium we'll [15:45] call provenence and status. This is what [15:47] we were talking about in the relational [15:48] sector that a human made it is part of [15:50] why it matters. This could be arts, [15:52] crafts, custom goods, live performance, [15:54] bespoke services. The human signature is [15:57] part of those products. So with all of [15:59] these concepts in mind, let's look at an [16:01] industry case study in healthcare. I [16:03] want to be very clear. I have no [16:05] expertise in healthcare whatsoever. It's [16:07] not a sector I've worked in. I'm coming [16:09] at this from the perspective of a a [16:11] technologist and b a consumer of [16:13] healthare. But I think it's quite clear [16:15] that healthcare has potentially every [16:17] type of demand elasticity that we [16:19] discussed before. People consume more of [16:21] it if it was cheaper. They would [16:22] probably consume more of it if they had [16:24] easier access like better clinics in [16:26] their hometown. They would certainly [16:27] consume more of it if the complexity was [16:29] reduced or they had better ways to [16:30] navigate that complexity, especially [16:32] when it comes to billing, insurance, [16:33] etc. I think that likely many people [16:35] would want a relational element and a [16:37] personalized element of their healthcare [16:38] rather than just being a number in a [16:39] system. And I think the biggest demand [16:41] elasticity or at least the one that's [16:43] most interesting when it comes to the [16:44] idea of the difference between the [16:45] affordability unlock and a possibility [16:47] unlock is around continuity. My argument [16:51] is that we consume an incredibly small [16:53] part, even a vanishingly small part of [16:56] the amount of healthcare we would [16:57] consume if the labor math underneath it [16:59] made a different kind of healthcare [17:00] possible. Right now, we all live in a [17:03] paradigm of reactive care that is [17:05] episodic by default. Every once in a [17:07] while, we make an appointment. Either [17:08] because it's our annual checkup or [17:10] because there's a thing we're now at the [17:12] age where we have to do once every 3 to [17:13] 5 years or because something stops [17:15] working the way that it's supposed to. [17:17] From there, we have some tests, get a [17:19] diagnosis, which turns into a [17:21] prescription, and then we're left alone [17:22] again to fend for ourselves. At some [17:24] point, a new crisis arises, and the [17:26] whole cycle starts again. There is, I [17:28] believe, an AI enabled version of [17:30] healthcare that is continuous, [17:31] preventative and personalized. It [17:33] involves AI for data collection, [17:35] continuous monitoring and even triage [17:37] and ranking. But in doing so, it creates [17:40] a whole new set of opportunities for [17:42] human roles, net new human roles that [17:45] emerge in this new continuous [17:47] preventative personalized paradigm. [17:49] There's an AI background layer that [17:51] watches the data and a human action [17:53] layer that helps convert that to [17:54] meaning. So for the sake of this episode [17:57] and for coming along this journey with [17:58] me, let's assume that this idea that [18:00] continuous preventative care becomes [18:02] more viable and more normalized. If that [18:05] happens, it will create demand for new [18:07] types of jobs. And these are not AI jobs [18:10] in the narrow sense we think about [18:12] prompt engineers or agent operators. Now [18:14] these are healthcare service jobs that [18:16] the AI layer makes possible. And each of [18:18] them maps to some set of specific human [18:21] premium categories that protect them [18:23] from being eaten by stronger AI. The [18:26] first role I'll call a continuous care [18:28] navigator. This is the human layer [18:30] between a patient and an AI enabled [18:31] monitoring system. They oversee a case [18:34] load of patients in continuous [18:35] monitoring and handle the moments that [18:37] matter such as the pattern having [18:39] changed, the call that has to happen, [18:40] the family that needs reassurance or the [18:42] escalation that has to land. So while an [18:44] AI is doing things like ingesting data [18:47] from devices, labs, patient reports, [18:50] establishing baselines and detecting [18:52] deviations, ranking cases and changes by [18:54] urgency, updating documentation, routing [18:57] escalation, the human, the continuous [18:59] care navigator is doing things like [19:01] reviewing flagged cases, calling [19:03] patients to clarify and ask the [19:05] questions AI didn't know to ask in those [19:07] personal interactions, noticing things [19:09] like fear, shame, avoidance, or [19:11] unexpressed family dynamics. [19:13] coordinating with clinicians to escalate [19:15] things and closing the loop after [19:17] intervention. The human premiums [19:19] involved here include trust, [19:21] accountability, translation, behavior [19:24] change, and relationship. So, how many [19:26] people could the market support? When it [19:28] comes to healthcare, the total [19:30] addressable market is everyone with a [19:31] body. But for the sake of our arguments, [19:34] let's zoom out a decade and take a [19:36] conservative, middle, and aggressive [19:37] case among American adults. We'll call [19:39] conservative 40 million enrolled [19:40] patients, middle 80 million enrolled [19:42] patients, and aggressive 120 million [19:44] enrolled patients. The conservative case [19:46] then is about 14% of US adults enrolled [19:49] with the aggressive being about 43% of [19:51] US adults enrolled. Now, also on the [19:53] conservative end, let's assume that one [19:55] continuous care navigator can handle 150 [19:57] patients. If that's the case, you're [19:59] talking about about 276,000 [20:02] jobs. On the other end of the spectrum [20:04] where we've got 120 million enrolled [20:06] patients and we're assuming that they [20:08] can only handle a 100 people each, [20:10] you're talking about 1.2 million jobs. A [20:13] couple examples of jobs that are in that [20:15] 267,000 person range include personal [20:18] financial adviserss, loan officers, and [20:21] coaches and scouts. For the 1.2 million, [20:24] that's about the number of high school [20:25] teachers there are. And if you took [20:27] advertising, marketing, promotions, PR, [20:29] and sales manager, they combined come in [20:31] at around that number. meaning that [20:32] we're talking about a ton of people and [20:35] a ton of jobs. The next job in our [20:37] hypothetical example, we'll call a care [20:39] plan outcome specialist. This is the [20:41] person who owns the gap between medical [20:43] advice and real world execution. So, if [20:45] the continuous care navigator is the [20:47] person who's helping assess and escalate [20:49] issues as they come up, the care plan [20:51] outcome specialist is the person on the [20:53] other end of the cycle who helps with [20:55] the implementation layer of healthcare. [20:57] If AI is tracking each patient's care [20:59] plan and milestones and monitoring [21:01] medication adherence, tracking lab [21:03] screenings, etc., and integrating all of [21:05] that into their continuous care plan, [21:06] the care plan outcome specialist is the [21:09] person who can provide a human interface [21:11] for talking with patients about why the [21:12] plan isn't working or help solve [21:14] practical problems like cost [21:15] transportation or fear or family. Now, [21:18] I'll leave it to you to decide whether [21:20] there would be a smaller number of those [21:21] folks as relative to the continuous care [21:23] navigators, but you're still talking [21:24] about potentially hundreds of thousands [21:26] of net new jobs that don't exist today [21:28] that only exist because of the way that [21:29] AI changes the overall healthare system. [21:32] Finally, representing another category [21:34] of role that's likely to be fairly [21:35] ubiquitous is the new type of technical [21:38] roles that will emerge to support the AI [21:40] enabled economy. This one I'm calling a [21:42] health data operations specialist. The [21:45] premise is that continuous healthcare is [21:46] only as good as the data flowing through [21:47] it. Wearables, labs, pharmacy systems, [21:50] patient reported data, insurance [21:51] records. The health data operations [21:53] specialist owns reliability, [21:55] integration, governance, and clinical [21:56] usability of that layer. Now, AI is [21:59] going to be doing a lot of the work [22:00] around data. It's going to pull and [22:01] normalize data across sources. It's [22:03] going to flag anomalies and integration [22:04] failures. It's going to generate [22:06] pipeline diagnostics, audit logs. But [22:08] the human that sits on top of them with [22:10] the premium of accountability, trust, [22:11] and translation is going to do things [22:13] like manage permissions, consent, and [22:15] audit governance. They're going to [22:16] resolve device and integration issues. [22:18] They're going to translate between [22:19] clinical and IT requirements, and [22:21] they're going to act as institutional [22:22] accountability for the data layer. Now, [22:25] it's likely that you'll have this type [22:26] of role be able to support a far larger [22:28] number of people, maybe one specialist [22:30] per thousand or 2,000 patients. But even [22:32] in those scenarios, you're talking about [22:34] net new 20,000, 30,000, 40,000 jobs. [22:38] And I want to be clear again that there [22:40] is no guarantee that this is the way [22:42] that healthcare plays out. There's [22:43] obviously a million factors other than [22:45] just the affordability of the care. But [22:47] I also don't think that this is [22:48] implausible and I do think it is the [22:50] type of change that you're going to see [22:51] AI enable where AI unlocks vast amounts [22:55] of new data, new types of data [22:57] coordination, new types of monitoring, [22:59] access provisioning, and the system will [23:02] over time adapt to accommodate those new [23:05] opportunities. And whether it's these [23:07] jobs or other types of jobs, there will [23:09] be new jobs that support the new shape [23:11] of those industries. Some of them will [23:13] be transitional and eventually done by [23:14] the AGI as well, but others will have [23:16] different categories of that human [23:18] premium and will become a durable, [23:20] integral new role in the new AI economy. [23:22] As AI changes industry paradigms, it [23:25] won't just create individual spot jobs, [23:27] but ecosystems of new roles that come to [23:30] meet the new challenges of those new [23:32] paradigms. Now, I picked healthcare, but [23:35] it doesn't take all that much [23:36] imagination to see similar patterns in [23:38] other sectors. We talked about small [23:40] business professional services, where [23:42] human plus AI service operators could [23:44] deliver smaller, cheaper, more frequent [23:45] professional services to businesses that [23:47] weren't agency clients before. It's not [23:49] hard to imagine that people would [23:50] consume a lot more legal services if [23:52] they were both affordable. And we might [23:54] even see more possibility unlocks around [23:55] things like preventative legal [23:56] maintenance that provides ongoing [23:58] support instead of crisis only [23:59] engagement. Education is another area [24:02] where we could see always on [24:03] personalized learning plus human pathway [24:05] guidance. AI provides content. Humans [24:07] help learners persist, choose paths, [24:09] improve competence, and so on and so [24:11] forth. In mental health, we could see a [24:13] broader support layer between nothing [24:14] and licensed therapy, peer support, [24:16] group facilitation, continuous check-ins [24:17] with clear escalation. Personal finance [24:20] could see once again continuous [24:21] financial life support, taxes, benefits, [24:23] insurance, debt, retirement, family [24:25] finances with AI doing analysis and [24:27] human zoning judgment and followrough. [24:29] elder, child care, family support, where [24:31] AI can solve huge amounts of [24:33] coordination and data issues, but is not [24:35] going to solve putting trustworthy [24:36] people in the room with your children or [24:38] parents. Across all of these, you start [24:40] to see new broad categories of roles. [24:43] Navigators that help people enter and [24:45] move through systems that are too [24:47] complex to face alone. Continuous [24:49] support workers who provide ongoing [24:51] human support around AI monitored [24:53] systems. AI augmented service operators [24:56] who use AI to deliver cheaper versions [24:58] of professional services to new market [24:59] tiers. Data and operations specialists [25:02] who make AI enabled service models [25:04] reliable in real institutional systems. [25:06] QA safety and compliance roles who [25:08] ensure AI mediated services are safe, [25:10] auditable, legal, fair, and reliable. [25:12] And a sixth and final family of roles [25:14] are escalation specialists who handle [25:16] the hardest cases that AI routes upward. [25:19] My argument in a nutshell then is that [25:21] the AI jobs question which is really an [25:23] AI economy question can't be discussed [25:26] only in terms of labor supply. It also [25:28] has to be discussed in terms of demand. [25:30] The second part of my argument is that [25:32] demand stretches. It can stretch because [25:34] of price, access, complexity, [25:35] continuity, personalization and [25:37] relational value. The unlocks that [25:40] stretch demand sometimes fall into the [25:42] affordability category where existing [25:44] services reach new buyers while others [25:46] fall into the possibility category where [25:48] new service models become viable for the [25:49] first time. And as those new [25:51] opportunities come online, the reason [25:53] that AGI doesn't just do it all is the [25:55] human premium. These at least seven [25:57] categories of value that require human [25:59] delivery. Relationship, presence, trust, [26:01] accountability, translation, behavior [26:03] change, and providence. And of course, [26:05] when you combine human premium plus [26:07] those unlocks and new economic systems, [26:09] you get new types of roles. Navigators, [26:12] continuous support workers, AI augmented [26:14] service operators, data and ops [26:16] specialists, QA safety and compliance, [26:18] and escalation specialists. Simply put, [26:21] we are heading to a world where there is [26:23] more AI, which yes does create more [26:25] supply of labor, but as that increased [26:27] supply of labor changes the cost and [26:29] availability structure of other [26:30] industries, it also means more demand. [26:33] and more demand even in a scenario of [26:35] AGI means more human work ahead too. My [26:39] argument when it comes to AI and jobs [26:41] has always been that in the short term [26:43] there are going to be painful [26:45] disruptions that require meaningful [26:47] interventions and that we should not be [26:49] polyianish or unconsidered about but [26:51] that on the other side I think things [26:53] are massively better and even bigger [26:55] than they are today. I hope this episode [26:57] goes a little way to showing what I see [26:59] on the other side and why I'm so [27:01] genuinely bullish about what comes in [27:02] the future. I'm sure this is a theme we [27:04] will continue to explore, but for now, [27:06] that's going to do it for today's AI [27:08] daily brief. Appreciate you listening or [27:10] watching as always and until next time, [27:12] peace. Unfortunately, Youtube does not provide subtitles for this video, The New Jobs AI Will Create, YouTube, 01krc7s5smg3zpz3htww6j6jwr.