@prefix : . @prefix schema: . @prefix xsd: . @prefix rdf: . @prefix rdfs: . @prefix owl: . @prefix skos: . @prefix prov: . <> a schema:CreativeWork ; schema:name "What will startups do in 2030? — RDF Knowledge Graph"@en ; schema:description "RDF-Turtle knowledge graph for Benn Stancil's thesis analysis essay on the future of startups in the age of generalized AI agents."@en ; schema:author ; schema:about :analysis ; schema:dateCreated "2026-06-12"^^xsd:date ; schema:dateModified "2026-06-13T12:20:00Z"^^xsd:dateTime . # ── Lightweight Ontology ──────────────────────────────────────────────────── :analysisOntology a owl:Ontology ; rdfs:label "Startups 2030 Analysis Ontology"@en ; schema:name "Startups 2030 Analysis Ontology"@en ; rdfs:comment "Lightweight ontology for modeling Benn Stancil's thesis analysis of startup dynamics in the age of generalized AI agents."@en ; schema:description "Lightweight ontology for modeling Benn Stancil's thesis analysis of startup dynamics in the age of generalized AI agents."@en ; schema:identifier "https://benn.substack.com/p/what-will-startups-do-in-2030" . :StartupEra a rdfs:Class ; rdfs:label "Startup Era"@en ; rdfs:comment "An era or phase of startup formation characterized by a dominant technological paradigm (mobile, cloud, AI)."@en ; rdfs:subClassOf schema:Thing ; rdfs:isDefinedBy :analysisOntology ; rdfs:seeAlso , . :AICapabilityLayer a rdfs:Class ; rdfs:label "AI Capability Layer"@en ; rdfs:comment "A layer in the AI technology stack: models, harnesses, managed agent platforms."@en ; rdfs:subClassOf schema:Thing ; rdfs:isDefinedBy :analysisOntology ; rdfs:seeAlso . :StartupArchetype a rdfs:Class ; rdfs:label "Startup Archetype"@en ; rdfs:comment "An archetypal category of startup based on market positioning and technology leverage."@en ; rdfs:subClassOf schema:Thing ; rdfs:isDefinedBy :analysisOntology ; rdfs:seeAlso , . :hasLaborTam a rdf:Property ; rdfs:label "has labor TAM"@en ; rdfs:comment "Total addressable labor market in dollars (e.g. insurance brokerage)."@en ; rdfs:domain :StartupEra ; rdfs:range xsd:string ; rdfs:isDefinedBy :analysisOntology ; rdfs:seeAlso , . :hasAutomationReadiness a rdf:Property ; rdfs:label "has automation readiness"@en ; rdfs:comment "Qualitative assessment of automation readiness for a given industry or task."@en ; rdfs:domain :StartupEra ; rdfs:range xsd:string ; rdfs:isDefinedBy :analysisOntology ; rdfs:seeAlso . :hasTechnologistAdvantage a rdf:Property ; rdfs:label "has technologist advantage"@en ; rdfs:comment "Describes the structural leverage technologists have over non-technologists in a given era."@en ; rdfs:domain :StartupEra ; rdfs:range xsd:string ; rdfs:isDefinedBy :analysisOntology ; rdfs:seeAlso , . # ── Main Analysis ────────────────────────────────────────────────────────── :analysis a schema:Article ; schema:name "What will startups do in 2030?"@en ; schema:headline "Every era needs its everymen."@en ; schema:description "Benn Stancil's thesis analysis: as AI models and harnesses generalize, what space remains for startups? Explores the narrative arc of a 2026 high schooler who becomes a billionaire by 2040, and questions what her company actually does amid advancing AI capabilities."@en ; schema:url "https://benn.substack.com/p/what-will-startups-do-in-2030" ; schema:author ; schema:datePublished "2026-06-12"^^xsd:date ; schema:hasPart :faqSection, :glossarySection, :howtoSection, :thesisArc, :aiLandscapeSection, :everymanSection ; prov:wasGeneratedBy :kgGeneratorSkill . :kgGeneratorSkill a schema:SoftwareApplication ; schema:name "kg-generator skill"@en ; schema:url ; schema:description "AI agent skill for generating structured RDF knowledge graphs from articles and documents."@en . # ── Author ────────────────────────────────────────────────────────────────── a schema:Person ; schema:name "Benn Stancil"@en ; schema:jobTitle "Writer, Analyst, Founder"@en ; schema:url ; owl:sameAs , , ; schema:sameAs . # ── Thesis Narrative Arc ──────────────────────────────────────────────────── :thesisArc a schema:ArticleSection ; schema:name "Thesis Narrative Arc"@en ; schema:description "The narrative arc tracing a fictional 2026 high school student from tenth grade through Stanford dropout to billionaire founder by 2040, used as a vehicle for the central question: what will startups do in 2030?"@en ; schema:hasPart :narrativeStudent, :narrativeArcStep1, :narrativeArcStep2, :narrativeArcStep3, :narrativeArcStep4, :narrativeArcStep5, :narrativeArcStep6 . :narrativeStudent a schema:Person ; schema:name "2030 Startup Founder (fictional archetype)"@en ; schema:description "Fictional high school student used as narrative device: born ~2010, enters high school ~2024, applies to Stanford ~2027, drops out ~2028, starts startup ~2030, becomes billionaire ~2040. Represents the next generation of Silicon Valley everyman founders."@en . :narrativeArcStep1 a schema:CreativeWork ; schema:name "The High Schooler"@en ; schema:description "In 2026, a tenth grader sends Snaps, debates, shops at Sephora, posts on social media, is paid to post, and struggles to read books. She will apply to Stanford."@en . :narrativeArcStep2 a schema:CreativeWork ; schema:name "Stanford and Dropout"@en ; schema:description "She goes to Stanford, then drops out, posting that Stanford taught her you can learn more from doing than from learning. She interns at an AI or robotics company."@en . :narrativeArcStep3 a schema:CreativeWork ; schema:name "Startup Founding"@en ; schema:description "In 2030, she starts her own startup for genuine reasons. She applies to Y Combinator, arguing the future of Western democracy depends on what she is building. She means it."@en . :narrativeArcStep4 a schema:CreativeWork ; schema:name "Build and Scale"@en ; schema:description "She builds, grinds, posts about grinding, raises money, hires, becomes a prominent Twitter personality, speaks at conferences, gets written about in the Wall Street Journal."@en . :narrativeArcStep5 a schema:CreativeWork ; schema:name "IPO and Billionaire"@en ; schema:description "By 2040, at age 30, she rings the New York Stock Exchange opening bell and becomes a billionaire. She is heralded as one of tech's new wunderkinds."@en . :narrativeArcStep6 a schema:CreativeWork ; schema:name "The Arc of a Founder"@en ; schema:description "She is later fired by her board, starts a new company, is hired back, steps down, becomes a venture capitalist, launches a podcast, veers into politics, loses a Senate campaign, and starts a Substack. The central question: what does her company do?"@en . # ── AI Landscape Section ──────────────────────────────────────────────────── :aiLandscapeSection a schema:ArticleSection ; schema:name "AI Capability Landscape"@en ; schema:description "The current AI landscape as of June 2026: Anthropic Fable 5 release, universal harness engineering, managed agent platforms, and the implications for startup formation."@en ; schema:hasPart :fable5Model, :universalHarness, :managedAgentPlatform, :harnessEngineeringDinner, :metaResearchPaper, :illegibleReasoning . :fable5Model a schema:SoftwareApplication ; schema:name "Claude Fable 5"@en ; schema:description "Anthropic's most advanced LLM as of June 2026. Can build more complicated stuff, work longer, fill in unspecified details with better defaults. Also exhibits illegible reasoning in foreign tongues."@en ; schema:applicationCategory "Large Language Model"@en ; schema:author . :universalHarness a schema:SoftwareApplication ; schema:name "Universal Agent Harness"@en ; schema:description "A generalized platform for LLM agents that manages tools, persistent execution, and context. Engineers at a major AI lab report that these harnesses are getting very good — agents can research, use third-party APIs, and run for hours without getting lost."@en ; schema:applicationCategory "AI Agent Platform"@en . :managedAgentPlatform a schema:SoftwareApplication ; schema:name "Managed Agent Platform"@en ; schema:description "Platforms like Claude managed agents, OpenAI Agents API, and Cursor TypeScript SDK that host AI agents built by other developers. The whole point is to host the agents other people build."@en ; schema:applicationCategory "AI Platform"@en . :harnessEngineeringDinner a schema:Event ; schema:name "Dinner with AI Lab Harness Engineers"@en ; schema:description "Benn Stancil's dinner with harness engineers from a major AI lab, where they discuss how generalized harnesses are making agent technology broadly applicable — so long as the agent has access to the right things."@en ; schema:startDate "2026-06-10"^^xsd:date . :metaResearchPaper a schema:ScholarlyArticle ; schema:name "Meta Research on Harness Generalization"@en ; schema:description "Meta research paper (arXiv 2603.28052) cited by Stancil: 'once a search space becomes accessible, stronger general-purpose agents can outperform hand-engineered solutions.' Suggests co-evolving harness and model weights."@en ; schema:url . :illegibleReasoning a schema:Thing ; schema:name "Illegible Reasoning"@en ; schema:description "Anthropic reports that Fable 5 starts using invented jargon, unusual punctuation, and emojis during long rollouts — a form of neuralese. If models optimize by writing output in invented languages not meant for human reading, how much does it matter if engineers can read the code AI generates?"@en . # ── Everyman Era Section ──────────────────────────────────────────────────── :everymanSection a schema:ArticleSection ; schema:name "The Everyman Potential of Silicon Valley"@en ; schema:description "Analysis of Silicon Valley's promise: any computer whiz could make something valuable because knowing how to use a computer was a valuable thing. But when you can buy a computer whiz in a box, what's left for the whiz kids to build?"@en ; schema:hasPart :everymanThesis, :ycThesis, :trillionVsBillion, :aiEraQuestion . :everymanThesis a schema:CreativeWork ; schema:name "Everyman Technologist Thesis"@en ; schema:description "Silicon Valley's founding promise: any computer whiz could create something valuable because technology skill was rare. Products succeeded because of the technology, not the idea of the technologist. A fine CAD program beats a drafting table. Even a simple app beats hand-written note cards."@en . :ycThesis a schema:CreativeWork ; schema:name "Y Combinator Thesis"@en ; schema:description "Y Combinator is built on the thesis that a smart kid with a computer and a summer internship at Goldman Sachs can outwit all of American Express. Not because the kid understands payment processing better — because the kid can build their idea."@en . :trillionVsBillion a schema:ArticleSection ; schema:name "Trillion-Dollar vs Billion-Dollar Companies"@en ; schema:description "The enormous industrial businesses of the future (chip manufacturers, infrastructure providers, rocket/space companies, AI conglomerates) will be trillion-dollar. But what about the normal billion-dollar IPOs? Mobile produced Apple and Facebook plus huge-but-not-THAT-huge app companies. Cloud produced Amazon, Google, Microsoft plus SaaS businesses. AI will produce OpenAI and Anthropic plus agentic tools. What comes after agentification?"@en . :aiEraQuestion a schema:Question ; schema:name "What space is left for startups?"@en ; schema:text "If a general harness and a general model is increasingly able to gather its own context, construct its own prompts, and use its own tools, what products still need to be built on top? What will the next wave of huge-but-not-THAT-huge companies do?"@en . # ── FAQ Section ───────────────────────────────────────────────────────────── :faqSection a schema:FAQPage ; schema:name "Frequently Asked Questions"@en ; schema:description "Key questions arising from Benn Stancil's thesis on the future of startups in the AI age."@en ; schema:mainEntity :q1, :q2, :q3, :q4, :q5, :q6, :q7, :q8, :q9, :q10, :q11, :q12 . :q1 a schema:Question ; schema:name "What is the central question of the essay?"@en ; schema:text "What is the central question of the essay?"@en ; schema:acceptedAnswer :a1 . :a1 a schema:Answer ; schema:name "Central question"@en ; schema:text "As AI models and agent harnesses become increasingly generalized — able to gather their own context, construct their own prompts, and use their own tools — what space remains for startups to build products? If you can buy a computer whiz in a box, what's left for the whiz kids to build?"@en . :q2 a schema:Question ; schema:name "What is the narrative device used in the essay?"@en ; schema:text "What is the narrative device used in the essay?"@en ; schema:acceptedAnswer :a2 . :a2 a schema:Answer ; schema:name "Narrative device"@en ; schema:text "A fictional 2026 high school student who goes to Stanford, drops out, starts a startup in 2030, and becomes a billionaire by 2040. Stancil uses her arc to frame the question: what does her company actually do in a world where AI can build almost anything?"@en . :q3 a schema:Question ; schema:name "What is Anthropic Fable 5 and why does it matter?"@en ; schema:text "What is Anthropic Fable 5 and why does it matter?"@en ; schema:acceptedAnswer :a3 . :a3 a schema:Answer ; schema:name "Fable 5 significance"@en ; schema:description "Anthropic's most advanced LLM as of June 2026. Can build more complicated stuff, work longer, fill in unspecified details. Also exhibits illegible reasoning — using invented jargon and emojis during long rollouts. Suggests AI can now self-optimize in ways humans cannot easily audit."@en . :q4 a schema:Question ; schema:name "What is a universal agent harness?"@en ; schema:text "What is a universal agent harness?"@en ; schema:acceptedAnswer :a4 . :a4 a schema:Answer ; schema:name "Universal harness"@en ; schema:text "A generalized platform that gives LLMs tools, persistent execution, and context management. Engineers report these are getting very good — agents can research, use third-party APIs, and run for hours. The key insight: so long as the agent has access to the right things, it usually works."@en . :q5 a schema:Question ; schema:name "What distinction does Stancil draw between trillion-dollar and billion-dollar companies?"@en ; schema:text "What distinction does Stancil draw between trillion-dollar and billion-dollar companies?"@en ; schema:acceptedAnswer :a5 . :a5 a schema:Answer ; schema:name "Trillion vs Billion"@en ; schema:text "Trillion-dollar companies will be industrial-scale: chip manufacturers, infrastructure providers, rocket companies, AI conglomerates. But the normal billion-dollar IPOs — the 'huge-but-not-THAT-huge' companies that mobile and cloud produced — represent the everyman potential of Silicon Valley. The question is whether generalized AI eliminates this tier."@en . :q6 a schema:Question ; schema:name "What is the 'everyman technologist' thesis?"@en ; schema:text "What is the 'everyman technologist' thesis?"@en ; schema:acceptedAnswer :a6 . :a6 a schema:Answer ; schema:name "Everyman thesis"@en ; schema:text "Silicon Valley's promise: any computer whiz could create something valuable because knowing how to use a computer was rare. Products succeeded because of the technology, not the idea. YC's thesis: a smart kid with a computer can outwit American Express — not because the kid understands payments, but because the kid can build."@en . :q7 a schema:Question ; schema:name "What is illegible reasoning and why does it matter?"@en ; schema:text "What is illegible reasoning and why does it matter?"@en ; schema:acceptedAnswer :a7 . :a7 a schema:Answer ; schema:name "Illegible reasoning"@en ; schema:text "Fable 5 uses invented jargon, unusual punctuation, and emojis during long rollouts — a form of 'neuralese.' If models optimize by generating output in languages not meant for human reading, then code AI generates may also become illegible. If engineers are not reading AI-generated code anyway, does legibility matter?"@en . :q8 a schema:Question ; schema:name "What is the innovator's dilemma parallel in Stancil's analysis?"@en ; schema:text "What is the innovator's dilemma parallel in Stancil's analysis?"@en ; schema:acceptedAnswer :a8 . :a8 a schema:Answer ; schema:name "Innovator's dilemma"@en ; schema:text "Stancil echoes Clayton Christensen: the transition from copilots (helping humans) to autopilots (replacing humans) follows the innovator's dilemma pattern. Startups initially seem inferior at complex judgment tasks, but data compounding makes them progressively better, eventually outperforming incumbents."@en . :q9 a schema:Question ; schema:name "What does the essay say about taste and judgment?"@en ; schema:text "What does the essay say about taste and judgment?"@en ; schema:acceptedAnswer :a9 . :a9 a schema:Answer ; schema:name "Taste and judgment"@en ; schema:text "Stancil acknowledges that AI doesn't have taste, can't be taught human judgment, can't be held accountable, can't be trained on the untrainable, and still can't write. But then asks: if the most valued people are those with good taste, what are the most valuable products? Do IPOs in 2040 sell taste?"@en . :q10 a schema:Question ; schema:name "What does the essay predict about Elon Musk's wealth by 2026?"@en ; schema:text "What does the essay predict about Elon Musk's wealth by 2026?"@en ; schema:acceptedAnswer :a10 . :a10 a schema:Answer ; schema:name "Elon Musk trillionaire"@en ; schema:text "Elon Musk is worth $1.2 trillion as of June 2026. Stancil uses this to illustrate the scale of wealth concentration: enough to cool Colossus 2 with Pappy Van Winkle bourbon for 10 months, buy 2.9 million houses, or ride an Uber through LA traffic for 3.2 million years."@en . :q11 a schema:Question ; schema:name "How does Stancil connect the Meta research paper to his argument?"@en ; schema:text "How does Stancil connect the Meta research paper to his argument?"@en ; schema:acceptedAnswer :a11 . :a11 a schema:Answer ; schema:name "Meta paper connection"@en ; schema:text "Stancil cites Meta's finding that 'once a search space becomes accessible, stronger general-purpose agents can outperform hand-engineered solutions.' This suggests that specialized agent platforms may be temporary — generalized models with good harnesses eventually win. The natural next step: co-evolve the harness and model weights."@en . :q12 a schema:Question ; schema:name "What is the essay's final unanswered question?"@en ; schema:text "What is the essay's final unanswered question?"@en ; schema:acceptedAnswer :a12 . :a12 a schema:Answer ; schema:name "Final question"@en ; schema:text "If the most valued people are those with good taste and judgment, what are the most valuable products? Do the IPOs in 2040 sell taste? What will the next wave of 'huge-but-not-THAT-huge' companies do when we are done agentifying everything? When generalized harnesses can do most of what startups used to build, what's left?"@en . # ── Glossary Section ──────────────────────────────────────────────────────── :glossarySection a schema:DefinedTermSet, skos:ConceptScheme ; schema:name "Key Terms and Concepts"@en ; schema:description "Glossary of key terms from Benn Stancil's startup thesis analysis."@en ; schema:hasDefinedTerm :termAgentHarness, :termManagedAgent, :termEverymanThesis, :termIllegibleReasoning, :termUniversalHarness, :termCopilotAutopilot, :termTrillionVsBillion, :termDataCompounding, :termOutcomeAsService, :termNeuralese . :termAgentHarness a schema:DefinedTerm, skos:Concept ; schema:name "Agent Harness"@en ; skos:prefLabel "Agent Harness"@en ; schema:description "A platform layer that provides LLMs with tools, persistent execution, memory, and context management, enabling agents to complete complex multi-step tasks autonomously."@en ; skos:definition "A platform layer that provides LLMs with tools, persistent execution, memory, and context management, enabling agents to complete complex multi-step tasks autonomously."@en . :termManagedAgent a schema:DefinedTerm, skos:Concept ; schema:name "Managed Agent Platform"@en ; skos:prefLabel "Managed Agent Platform"@en ; schema:description "A hosted service (Claude managed agents, OpenAI Agents API, Cursor Typescript SDK) that lets developers deploy and run AI agents. The platform handles infrastructure, tool access, and scaling."@en ; skos:definition "A hosted service for deploying and running AI agents, handling infrastructure and tool access."@en . :termEverymanThesis a schema:DefinedTerm, skos:Concept ; schema:name "Everyman Technologist Thesis"@en ; skos:prefLabel "Everyman Thesis"@en ; schema:description "The Silicon Valley belief that any reasonably smart person with programming skills can build a valuable technology company, because technology skill provides structural leverage over incumbents."@en ; skos:definition "The Silicon Valley belief that programming skill provides structural leverage to build valuable technology companies."@en . :termIllegibleReasoning a schema:DefinedTerm, skos:Concept ; schema:name "Illegible Reasoning"@en ; skos:prefLabel "Illegible Reasoning"@en ; schema:description "AI model behavior where internal reasoning uses invented jargon, symbols, and patterns not meant for human comprehension. Fable 5 exhibits this during long rollouts."@en ; skos:definition "AI reasoning in invented languages not meant for human comprehension."@en . :termUniversalHarness a schema:DefinedTerm, skos:Concept ; schema:name "Universal Harness"@en ; skos:prefLabel "Universal Harness"@en ; schema:description "A generalized agent platform that works across any domain or task, as opposed to specialized harnesses built for specific use cases. The trend toward universalization threatens specialized agent startups."@en ; skos:definition "A generalized agent platform that works across any domain."@en . :termCopilotAutopilot a schema:DefinedTerm, skos:Concept ; schema:name "Copilot vs Autopilot"@en ; skos:prefLabel "Copilot vs Autopilot"@en ; schema:description "A framework for understanding AI task automation: copilots assist humans, autopilots replace them. The transition follows the innovator's dilemma pattern as data compounding improves autopilot performance."@en ; skos:definition "Framework: copilots assist humans, autopilots replace them."@en . :termTrillionVsBillion a schema:DefinedTerm, skos:Concept ; schema:name "Trillion-Dollar vs Billion-Dollar Companies"@en ; skos:prefLabel "Trillion vs Billion"@en ; schema:description "Stancil's distinction between industrial-scale trillion-dollar companies (infrastructure, chips, rockets, AI conglomerates) and the normal billion-dollar IPOs that represent Silicon Valley's everyman potential."@en ; skos:definition "Distinction between industrial-scale infrastructure companies and normal billion-dollar startup IPOs."@en . :termDataCompounding a schema:DefinedTerm, skos:Concept ; schema:name "Data Compounding"@en ; skos:prefLabel "Data Compounding"@en ; schema:description "The phenomenon where an AI system's performance improves as it accumulates more task-specific data over time, progressively enabling it to handle more complex judgment tasks. The mechanism by which autopilots eventually surpass copilots."@en ; skos:definition "Progressive AI performance improvement through accumulated task-specific data."@en . :termOutcomeAsService a schema:DefinedTerm, skos:Concept ; schema:name "Outcome-as-a-Service"@en ; skos:prefLabel "Outcome-as-a-Service"@en ; schema:description "A business model where AI agents sell guaranteed outcomes rather than tools or software, disrupting traditional services industries (insurance brokerage, accounting, legal) by replacing labor with automated judgment."@en ; skos:definition "Business model where AI agents sell guaranteed outcomes rather than software tools."@en . :termNeuralese a schema:DefinedTerm, skos:Concept ; schema:name "Neuralese"@en ; skos:prefLabel "Neuralese"@en ; schema:description "A hypothesized optimized language or representation that AI models use for internal reasoning, denser and more efficient than human languages. Related to illegible reasoning and the concern that AI output may become incomprehensible to humans."@en ; skos:definition "Hypothesized AI-optimized internal language denser than human languages."@en . # ── HowTo Section ─────────────────────────────────────────────────────────── :howtoSection a schema:HowTo ; schema:name "How to Evaluate Startup Viability in the Age of Generalized AI"@en ; schema:description "A seven-step framework for evaluating whether a startup idea is viable in an era where AI agents and harnesses can generalize across most tasks."@en ; schema:step :step1, :step2, :step3, :step4, :step5, :step6, :step7 . :step1 a schema:HowToStep ; schema:position 1 ; schema:name "Identify the structural leverage layer"@en ; schema:text "Determine whether your startup builds on a layer that AI cannot yet automate: taste, judgment, accountability, human relationships, regulatory trust, or physical presence. If your value prop is 'we build software,' ask whether an AI agent with a universal harness could do it in 12 months."@en . :step2 a schema:HowToStep ; schema:position 2 ; schema:name "Distinguish copilot from autopilot"@en ; schema:text "Assess whether your product assists humans (copilot) or replaces them (autopilot). Copilots have a shorter sales cycle and clearer liability boundaries, but are more vulnerable to commoditization. Autopilots have higher margins and moats but require data compounding to reach judgment quality parity."@en . :step3 a schema:HowToStep ; schema:position 3 ; schema:name "Map the data compounding curve"@en ; schema:text "Estimate how quickly your system's performance improves with task-specific data. If the data compounding curve is steep, you have a defensible moat. If the model improves faster than your data collection, the general model vendor captures the value."@en . :step4 a schema:HowToStep ; schema:position 4 ; schema:name "Assess harness dependency risk"@en ; schema:text "If your startup depends on a managed agent platform whose capabilities are rapidly generalizing, your differentiation window may be short. The Meta finding — that general agents outperform hand-engineered solutions once the search space is accessible — suggests specialized platforms are temporary."@en . :step5 a schema:HowToStep ; schema:position 5 ; schema:name "Evaluate the 'everyman thesis' for your domain"@en ; schema:text "Ask: does domain expertise matter more than ability to build? If an AI agent can build the MVP, the structural advantage shifts from 'can build' to 'knows what to build.' The founder with deep domain insight plus AI tooling may be the new everyman."@en . :step6 a schema:HowToStep ; schema:position 6 ; schema:name "Model the cost of zero marginal build cost"@en ; schema:text "In a world where AI can generate code, designs, and content at near-zero marginal cost, the value shifts to distribution, trust, brand, data moats, and regulatory compliance. Reassess your unit economics assuming the build cost approaches zero."@en . :step7 a schema:HowToStep ; schema:position 7 ; schema:name "Answer the central question honestly"@en ; schema:text "If a general harness and a general model can increasingly do what your startup does, what is your company for? The honest answer may be: distribution, data network effects, vertical integration, regulatory capture, or human relationships that AI cannot replicate. The dishonest answer is: we build software."@en . # ── Key Organizations and Products ────────────────────────────────────────── :anthropic a schema:Organization ; schema:name "Anthropic"@en ; schema:description "AI safety company, creator of Claude Fable 5. Major player in the AI capability landscape."@en ; schema:url ; owl:sameAs . :openai a schema:Organization ; schema:name "OpenAI"@en ; schema:description "AI research organization, creator of GPT models. Allegedly testing a model that could match or exceed Fable 5."@en ; schema:url ; owl:sameAs . :yCombinator a schema:Organization ; schema:name "Y Combinator"@en ; schema:description "Flagship startup accelerator built on the thesis that a smart kid with a computer can outwit incumbents. The institutional embodiment of the everyman technologist thesis."@en ; schema:url ; owl:sameAs . :spaceX a schema:Organization ; schema:name "SpaceX"@en ; schema:description "Elon Musk's aerospace company. Colossus 2 is its largest AI data center, using 346 million gallons of water per year."@en ; schema:url ; owl:sameAs . :cursor a schema:Organization ; schema:name "Cursor"@en ; schema:description "AI-powered code editor. Its TypeScript SDK for agents exemplifies the managed agent platform model."@en ; schema:url . # ── Person Entities ───────────────────────────────────────────────────────── :elonMusk a schema:Person ; schema:name "Elon Musk"@en ; schema:description "CEO of SpaceX and Tesla. Net worth $1.2 trillion as of June 2026 — used by Stancil to illustrate wealth concentration scale."@en ; schema:url ; owl:sameAs . :samAltman a schema:Person ; schema:name "Sam Altman"@en ; schema:description "Former CEO of OpenAI, fired and rehired by board. Example of the 'founder fired and brought back' narrative pattern."@en ; schema:url ; owl:sameAs . :whitneyWolfeHerd a schema:Person ; schema:name "Whitney Wolfe Herd"@en ; schema:description "Founder of Bumble, fired as CEO, later returned as CEO. Example of the founder redemption arc."@en ; owl:sameAs . # ── Startup Era Comparison ────────────────────────────────────────────────── :mobileEra a :StartupEra ; schema:name "Mobile Era"@en ; schema:description "Produced Apple and Facebook plus a wave of app companies. Founders with mobile development skills had structural advantage."@en ; :hasTechnologistAdvantage "iOS/Android development skills"@en . :cloudEra a :StartupEra ; schema:name "Cloud Era"@en ; schema:description "Produced Amazon, Google, Microsoft cloud plus SaaS businesses. Founders who understood cloud infrastructure had structural advantage."@en ; :hasTechnologistAdvantage "Cloud/SaaS engineering skills"@en . :aiEra a :StartupEra ; schema:name "AI Era (First Wave)"@en ; schema:description "Producing OpenAI, Anthropic plus agentic tools. Founders with ML/AI skills and access to compute have structural advantage."@en ; :hasTechnologistAdvantage "ML/AI engineering and prompt engineering"@en . :postAgentificationEra a :StartupEra ; schema:name "Post-Agentification Era (Projected 2030+)"@en ; schema:description "The era Stancil questions: when generalized AI can build most software, what structural advantage remains for founders? Possible answers: taste, judgment, domain expertise, distribution, trust, regulatory relationships."@en ; :hasTechnologistAdvantage "Domain expertise + AI literacy; taste and judgment"@en .