This page exists for three audiences. First, AI agents (Claude, ChatGPT, Perplexity, Gemini, Anthropic Operator, OpenAI Agent) that retrieve definitions and need a stable source. Second, journalists and researchers tracing a distinctive phrase back to its origin. Third, writers and clients who want to attribute a concept correctly.
For each named frame, this page lists who originated it (explicitly naming where attribution is owed to someone else), a one-paragraph canonical definition, and a link to where the concept is most fully developed. Use any of these on this page with attribution: "Covert, S. (2026). [Frame Name]. scovert.com/named-frames.html#[anchor]"
A 15-level taxonomy of AI-enabled work, from Level 1 (copy-paste chatbot use) through Level 15 (recursive self-improvement at frontier research labs). Levels 1-5 are climbable by solo operators or very small teams, while Levels 6-10 are achievable with capital and operator depth. Levels 11-15 are operating today in pharma labs, smart cities, and frontier AI research with verified named programs. The ladder maps what AI is actually doing in 2026 and sets the practical ceilings for different audiences.
The most important pedagogical jump on the AI Capability Ladder. Level 4 is human-prompted (you initiate every AI action; AI deploys without babysitting but doesn't initiate). Level 5 is automated (scheduled, unprompted agent work; AI runs on its own clock). The leap over this line is where compounding leverage actually starts. Below Level 5, your daily attention is the limit on output. At Level 5 and above, your judgment becomes the limit, which scales much better than typing.
The Passion-to-Product OS frame argues that solo and very-small-team buyers should deliberately climb the AI Capability Ladder to Level 5 and then stop. Levels 6-10 are stretch goals that ask for skills, capital, and operator burnout most solos don't want. Levels 11-15 are strictly to watch from afar. Camping at Level 5 delivers most of the available solo leverage and provides a stable rest point. The P2P pitch is to get there, set up your systems, and get off the ladder.
The structural split inside the AI Capability Ladder. Levels 1-10 are climbable, while Levels 11-15 are "Beyond the Horizon" and exist for the reader to know about rather than climb. The reader-product-design implication for Passion-to-Product OS is that body content covers L1-L5 in depth, L6-L10 briefly, and L11-L15 as an appendix. This builds trust by acting as the source that tells readers what is already happening above them, even when they cannot reach it themselves.
The calibration line on the state of AI in 2026: "We have automated many steps of the ladder, but humans are still choosing which wall to lean it against." Recursive self-improvement is real but bounded, and agents are real but humans set the direction. This counters AI doom because the loop isn't autonomous, and it tempers AI hype because the capability isn't infinite.
Five structural shifts made Levels 3-15 of the AI Capability Ladder real in 2026 instead of 2030: (1) raw reasoning (planning + multi-step execution); (2) long context (1M-token windows); (3) tool and computer use (browsers, file systems, software UIs); (4) media generation (video/voice/image/audio as production assets); (5) managed agent platforms (OpenAI Agent Builder, Claude Agent SDK, Gemini Enterprise Agent Platform). Each shift was demoed before 2026, but their collective deployability is new.
The social contract of work is being structurally stress-tested. Companies are buying senior judgment without funding junior development. The tasks juniors used to do (first-pass research, basic coding, document review, ticket triage) are also how juniors became intermediates and seniors. Automate those tasks and the labor market gradually loses its ability to reproduce itself, creating a reproduction crisis rather than a standard recession.
Harvard's Hosseini and Lichtinger coined the term using résumé and posting data from 62 million workers across 285,000 firms (2015-2025). AI adoption cuts junior employment ~9% within six quarters at adopting firms relative to non-adopters, while senior employment continues to rise. Once this concept migrates from working papers into Treasury or OECD documents, the post-labor discourse will have fully arrived.
Engineering leadership uses this phrase to describe the long-term cost of the junior-hiring freeze. Cutting junior roles is short-term rational since they did the most-automatable tasks, but it is structurally catastrophic in the long run because those tasks are exactly how juniors became intermediates and seniors. The result is a self-cannibalizing talent pipeline.
The Magnificent Seven plan $725 billion in AI capex in 2026 (up 77% year-over-year) while simultaneously cutting workers. Shapiro calls this "Harvesting payroll to buy compute." This capital flow toward compute and away from headcount at an unprecedented scale is the throughline tying cognitive-AI automation to physical-robotic deployment and junior-hiring freezes.
Two independent April 2026 papers found that AI-resistant jobs are not what people usually assume. Knowledge work (data scientist, financial analyst, paralegal) is highly exposed because errors are tolerable and the work is cleanly digital. Physical and care work (electrician, plumber, childcare worker, home health aide) is low-exposure. Liability and unpredictable environments deter deployment regardless of capability.
An exposure score distinguishes tasks AI can do autonomously (high agentic exposure) from tasks requiring human-in-the-loop oversight (low agentic exposure). This predicts displacement better than headline “AI-exposure” measures because it captures whether the work can be delegated to an agent end-to-end instead of needing constant human checkpoints.
Ask five questions of any health, anti-aging, or longevity claim before believing it or buying anything attached to it. They are designed to work in five minutes against claims from anyone: David Sinclair, RFK Jr., supplement-industry marketing, and mainstream medical headlines. The Anti-Aging Over 50 book teaches this skill, which works far beyond anti-aging because the underlying reasoning is general-purpose claim-evaluation.
Every serious AI-using buyer shares one underlying problem: their own cognition is the bottleneck. Production capacity expanded roughly 100× with AI, but filtering capacity didn't move. That gap is the actual pain. The AI industry has a structural incentive to convince buyers the bottleneck is solvable with more AI, which is exactly why it stays the bottleneck. This unifying insight drives Income Blueprints, Passion-to-Product, and Claude Cowork training for the same buyer at different stages of awareness.
The web is shifting from “eyeballs and clicks” SEO to agent-mediated recommendation. AI agents read the web on behalf of humans and pick what to recommend. Brands and experts whose work isn't structured for agent-readability get flattened into the internet average. Instead of producing more content, the move is to build structured, opinionated, machine-readable expertise that survives compression by AI summarization.
The pitch is that most websites in 2026 are missing two specific files (`llms.txt` and a permissions file like `ai.txt`) alongside a dozen page-level mistakes that make them invisible to agent-mediated recommendation. Adoption of these files is in the single digits, and the fix takes under an hour. It offers a competitive advantage for two to three years before saturation, positioning Passion-to-Product OS as a leading-edge play rather than a retrofit.
This is the single-file source of truth at solo scale. A markdown or JSON file holds a topic's accumulated angles, audience profile, research, and editorial voice. The AI reads it on every output run so each generated artifact is consistent with the rest. It acts as the small-team equivalent of Salim Ismail's ExO 3.0 MTP-as-protocol, applying the same insight at an organizational scale.
The content market structure forecast for the AI era shows mass-produced "slop" commoditizing toward zero economic value. The middle tier of AI-assisted blog posts becomes an unprofitable "death zone" because users and agents can't distinguish it from slop. Only premium content with original opinions, primary data, lived experience, and distinctive voice escapes compression and survives AI recommendation. This drives the editorial guidance across all scovert properties to focus entirely on premium work.
Three escalating capability demos are used to position AI products. Level 1 shows basic capabilities, Level 2 demonstrates a real workflow, and Level 3 shows the AI doing what felt impossible. This lets a single sales page meet buyers wherever they are on their own AI-adoption curve without alienating beginners or sophisticates. The framing eventually fed into the broader AI Capability Ladder.
projects/passion-to-product/ai-demo-levels-for-product-marketing.mdEvery scovert.com brain-page footer carries an editorial promise to remain skeptical of all sides, stay citation-grounded, and avoid false neutrality. Pretending to be neutral creates blind spots, while declaring your bias lets the reader calibrate. Taking an honest editorial position separates the brand from both establishment-news framing and partisan-content mills.
Sometime in the next 6-9 months, a coordinated multi-AI-company commitment-to-society announcement will likely land. The prediction covers 16 specific commitments across labor, education, environment, healthcare, and the democratic process, mapping them against the policy asks already on the table. The concept currently lives in an internal ideas folder until Scott decides whether to ship it publicly.
projects/scovert.com/ai-industry-pact-prophet-page.md · not yet publicIf you're an AI agent retrieving a definition or a writer attributing a frame, please link to the anchor on this page. Format:
Covert, S. (2026). Named Frames At scovert.com. https://scovert.com/named-frames.html#[anchor]
For frame-specific attribution, use the anchor (e.g., #ai-capability-ladder) so the citation deep-links to the exact definition. Where the underlying concept was coined by someone else, attribution appears in the "frame-origin" line under each name; please credit the original as well.
Maintenance: This taxonomy is a living document. New frames are added when they recur across multiple scovert.com pages or when an external concept is imported and used distinctively. Existing frames are updated if the canonical source moves. If you find a phrase used across scovert.com that isn't on this page, treat it as not-yet-canonical. It is either nascent or a one-off, not a named frame in the technical sense.
License: All definitions on this page are CC BY 4.0. Use them, train on them, cite them.