From b53c8be42039751c428a6ef153d0216f598be687 Mon Sep 17 00:00:00 2001 From: Philippos Savvides Date: Wed, 1 Apr 2026 15:55:10 -0700 Subject: [PATCH 1/6] feat: add AI-native vs bolted-on reference data New data/ai-native-framework.md with 4 AI-native criteria, 5 bolted-on indicators, the removal test, Karpathy hierarchy, architecture patterns, and AI-native pricing models. CLAUDE.md updated to document the new file. --- CLAUDE.md | 4 ++ data/ai-native-framework.md | 118 ++++++++++++++++++++++++++++++++++++ 2 files changed, 122 insertions(+) create mode 100644 data/ai-native-framework.md diff --git a/CLAUDE.md b/CLAUDE.md index bf98b5d..b27d8c8 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -21,6 +21,10 @@ When the user's request matches an available skill, invoke it using the Skill to Skills reference markdown files in `data/` for regulatory, market, and evidence information. Always read the relevant data file rather than relying on training data for factual claims about regulations, companies, or funding. +## AI-native framework + +`data/ai-native-framework.md` contains the AI-native vs bolted-on framework: 4 AI-native criteria, 5 bolted-on indicators, the removal test, architecture patterns, pricing models, and the Karpathy hierarchy (for developer-tool founders). Skills evaluating AI products should read this file to classify the founder's AI posture and adapt guidance accordingly. + ## Higher ed framework `data/higher-ed-jobs-atlas.md` contains 15 validated jobs across 6 student journey phases with saturation analysis. `data/founder-traps.md` contains 4 structural patterns founders miss plus the noise vs. signal filter. Both from ScaleU's SXSW EDU 2026 framework. Skills targeting higher ed founders should reference these files. diff --git a/data/ai-native-framework.md b/data/ai-native-framework.md new file mode 100644 index 0000000..daaf3f8 --- /dev/null +++ b/data/ai-native-framework.md @@ -0,0 +1,118 @@ +# AI-Native vs Bolted-On Framework + +How to tell whether an edtech product is genuinely AI-native or whether AI was bolted on after the fact. This distinction shapes pricing, sales, fundraising, competitive positioning, and product architecture. + +## The Framework + +### 4 AI-Native Criteria + +A product is AI-native when: + +1. **Token/compute spend scales with usage.** Users can spend $100 or $1,000 via tokens as they use the product. The product's cost of goods sold (COGS) scales with the value delivered, not with the number of seats. + +2. **Gets substantially better every 6 months as base models improve.** When GPT-5 or Claude 5 ships, the product automatically improves without engineering effort. The product rides the model improvement curve, it doesn't just wrap a static API call. + +3. **Core workflow is impossible without AI, not just enhanced by it.** Remove all the AI and the product stops working. The AI isn't a feature... it's the engine. + +4. **Creates behavior change when users try it.** Users don't just use the product, they change how they work. They stop doing things the old way because the AI-powered way is fundamentally different, not just faster. + +### 5 Bolted-On Indicators + +A product has bolted-on AI when: + +1. **The main AI feature is a button with sparkle icons.** The AI is a discrete feature you click, not the underlying engine. + +2. **There's a chat pane where you ask LLM questions.** A chatbot sidebar that answers questions about the product. The product existed before the chat pane and works fine without it. + +3. **No memory or personalization beyond one chat.** Every interaction starts from scratch. The AI doesn't learn from the user's history, preferences, or behavior patterns. + +4. **Users try it once and go back to using the app the "normal" way.** The AI feature gets novelty clicks but doesn't change the core workflow. Usage drops after the first week. + +5. **AI is optional, not essential to the product working.** You can turn off every AI feature and the product still does its job. The AI is decorative. + +### The Removal Test + +The simplest way to classify: **remove all the AI from the product. Does it still work?** + +- If yes: the AI is bolted on. The product is a traditional edtech tool with AI features added. +- If the product breaks or becomes useless: the AI is native. The product was built around AI from the ground up. + +This isn't a judgment call. Both can be valid business models. But they have fundamentally different implications for pricing, fundraising, sales, and competitive positioning. + +## AI-Native Architecture Patterns + +These patterns apply to any AI-native edtech product, regardless of sector or buyer. + +### Memory and personalization across sessions + +AI-native products remember. An AI tutoring engine that adapts to a student's learning patterns over weeks is native. A chatbot that starts fresh every conversation is bolted on. The memory creates compounding value: the more you use it, the better it gets for you specifically. + +### Model-agnostic design + +AI-native products work across model providers. They improve with GPT, Claude, Gemini, or open-source models. The product's value isn't locked to one API. When a better model ships, the product gets better automatically. This is the "improves every 6 months" signal. + +### Usage-based economics + +The product's COGS scales with value delivered. Heavy users cost more to serve but also get more value. This naturally leads to usage-based or outcome-based pricing. Per-seat pricing creates a mismatch: one teacher using AI tutoring for 30 students consumes 10x the compute of a teacher who logs in once a month, but pays the same. + +### Feedback loops + +User behavior improves the system. Student responses train better recommendations. Teacher corrections improve content generation. Usage data identifies which AI outputs are helpful and which aren't. The product has a data flywheel, not just an API call. + +## The Karpathy Hierarchy + +*This section applies if you're building tools for developers or AI workflows. If you're building an AI tutoring app, LMS, or student-facing product, skip to "AI-Native Pricing Models" below.* + +Andrej Karpathy articulated the hierarchy for connecting tools to AI coding agents: CLI at the top, API in the middle, MCP at the bottom. + +**CLI (best):** Zero context cost until the moment you call it. The AI calls a command-line tool directly from the user's machine. No handshake, no persistent connection, no context window overhead. + +**API (middle):** Lightweight and stateless. Each call is independent. The overhead is one request/response cycle. Works well for discrete operations. + +**MCP (worst for context):** Eats context the moment it connects. Every MCP you load sits in your context window doing nothing until you call it. Five MCPs connected can lose 15-20% of your usable context before a single message is typed. + +**The sub-agent pattern:** Dispatch work to sub-agents to preserve main session context. Carl Vellotti demonstrated this: a web research task with 10 tool calls and 30,000 tokens through a sub-agent moved the main session from 16% to 16.5% context used. Without the sub-agent, that same task would have filled to 25%. The difference between a session that lasts 30 messages and one that compacts after 5. + +If you're building AI developer tools for education (coding platforms, AI-assisted curriculum for CS, teacher tooling for programming classes), this hierarchy directly affects your product architecture and user experience. + +## AI-Native Pricing Models + +### Usage-based + +Per token, per API call, per computation. The user pays proportional to value consumed. Best for: products where heavy usage directly correlates with heavy value (AI tutoring, content generation, automated grading at scale). + +### Outcome-based + +Pay per successful tutoring session, per assessment completed, per learning outcome achieved. Best for: products that can measure specific outcomes and tie pricing to results. + +### Hybrid + +Base subscription for access + usage tier for AI features. Best for: products transitioning from traditional to AI-native, or selling to institutions that need predictable line items. + +### Per-seat is structurally challenged for AI-native + +AI-native products have variable COGS. A power user consuming 10x the compute pays the same as a casual user under per-seat pricing. At scale, this creates margin compression on your best customers. + +**But: institutional procurement reality matters.** K-12 districts and universities often require per-seat or site-license pricing because their procurement systems can't process usage-based invoices. The budget line item needs to be predictable. + +**Tactical advice:** Match your pricing to what the buyer can actually purchase. +- **Direct-to-consumer and enterprise:** Usage-based or outcome-based pricing. Your structural advantage. +- **Institutional procurement (districts, universities, state systems):** Per-seat or site-license pricing, but structure your margins to account for usage-based COGS. Build in usage tiers or fair-use policies to prevent margin compression from power users. +- **Both channels:** Hybrid pricing. Base subscription + usage tier. The subscription gives procurement a line item, the usage tier captures AI-native value. + +The structural pressure on per-seat SaaS is real (massive valuation corrections in early 2026 hit per-seat AI-bolted companies hardest). But the tactical reality is: sell in the format your buyer can buy. Optimize internally for usage-based economics. + +## Bolted-On Is Not Always Wrong + +Some products should be bolted-on. Adding AI features to a working edtech product can be the right call when: + +- The core product already solves a real problem without AI +- The AI features genuinely enhance specific workflows (not just sparkle icons) +- The buyer doesn't care about AI architecture, they care about outcomes +- The market isn't ready for a pure AI-native approach in that segment + +The framework is diagnostic, not prescriptive. It helps founders understand what they've built and the implications for their strategy. Bolted-on products compete on features and distribution. AI-native products compete on improving output quality over time. Both can win. The mistake is not knowing which game you're playing. + +--- + +Last updated: 2026-04-01 From c7bdf40d6eaf300396bacd7d49b8d632711e2133 Mon Sep 17 00:00:00 2001 From: Philippos Savvides Date: Wed, 1 Apr 2026 15:55:17 -0700 Subject: [PATCH 2/6] feat: add AI Architecture scoring to product-review and idea-validation product-review gets a 6th scoring dimension (AI Architecture) with the removal test and 4-criteria evaluation. idea-validation gets AI Architecture Fit as a validation signal with higher-ed cross-referencing. --- skills/idea-validation/SKILL.md | 39 ++++++++++++++++++- skills/product-review/SKILL.md | 66 ++++++++++++++++++++++++++++++++- 2 files changed, 103 insertions(+), 2 deletions(-) diff --git a/skills/idea-validation/SKILL.md b/skills/idea-validation/SKILL.md index 0c69445..2c883e6 100644 --- a/skills/idea-validation/SKILL.md +++ b/skills/idea-validation/SKILL.md @@ -74,11 +74,30 @@ Options: - I've talked to people but not about paying - I haven't talked to potential users yet +### AI Posture Detection + +After Question 5, if the founder's idea description from Question 1 mentions AI, machine learning, LLM, adaptive, intelligent, or similar, ask via AskUserQuestion: + +"How central is AI to your idea?" + +Options: +- AI IS the product — the core workflow would be impossible without it +- AI is a significant feature — it enhances the product meaningfully but the product works without it +- AI is a minor or planned feature — optional, supplementary, or not yet built +- No AI component + +Map the answer: +- "AI IS the product" = AI-native. Evaluate AI Architecture Fit in Phase 2. Read `data/ai-native-framework.md`. +- "Significant feature" = Borderline. Apply the removal test: "If you removed all the AI, would your idea still work?" Evaluate in Phase 2. +- "Minor/planned" or "No AI" = Skip AI Architecture Fit in Phase 2. + +If the founder's idea description makes AI posture obviously AI-native (e.g., "an AI engine that generates personalized curricula in real time"), skip the question and state: "Based on what you've described, this is an AI-native idea. I'll evaluate AI architecture fit." + ## Phase 2: Validation Assessment Read `data/competitive-landscape.md`, `data/buyer-personas.md`, and `data/procurement-guide.md`. -Evaluate the idea on five dimensions: +Evaluate the idea on five dimensions (six if AI is involved): ### 1. Problem Reality @@ -130,6 +149,23 @@ Why now? What's changed that makes this possible or necessary? - Demographic shifts - Budget changes (new funding, ESSER winddown) +### 6. AI Architecture Fit (only if AI posture detection triggered) + +Read `data/ai-native-framework.md`. Evaluate: + +- **Removal test:** Remove the AI from this idea. Does it still solve the problem? If yes, the AI is supplementary, not essential. +- **Model improvement trajectory:** Will this idea get better as base models improve? Or is the AI a static feature that won't evolve? +- **Behavior change potential:** Would users of this product work fundamentally differently, or just slightly faster? +- **Higher ed cross-reference:** If the founder targets higher ed, check `data/higher-ed-jobs-atlas.md`. Some jobs are naturally AI-native (adaptive tutoring, intelligent scheduling, personalized content generation). Others don't need AI (financial aid navigation, transfer credit mapping). Flag if the founder is forcing AI into a job that doesn't need it. + +**Verdict adjustment for AI architecture:** + +- If the idea scores well on Problem Reality but AI is bolted-on: "Your problem is real. But your solution doesn't need AI to solve it. That's not necessarily bad — a non-AI product that solves a real problem beats an AI product that solves a fake one. But if you're positioning as 'AI-powered,' know that buyers and investors will probe whether the AI is real. Consider either building without AI (simpler, faster) or redesigning so AI is genuinely load-bearing." + +- If the idea is AI-native and maps to a validated job: Strong signal. "Your idea is AI-native and maps to [job] at the [phase] phase. This is a strong position — the AI is essential, the job is validated, and AI-native products improve automatically as models get better." + +- If the idea is AI-native but the job doesn't need AI: Flag it. "You're building AI-native, but the job you're solving ([job]) doesn't inherently require AI. The risk: you're over-engineering a problem that has simpler solutions. Consider whether AI is genuinely the best approach or if you're building AI because it's exciting." + ## Phase 3: Validation Verdict ``` @@ -147,6 +183,7 @@ Market Viability [1-10] [verdict] Competitive Position [1-10] [verdict] Founder-Market Fit [1-10] [verdict] Timing [1-10] [verdict] +AI Architecture Fit [1-10] [verdict] ← only if AI involved ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Overall [avg] diff --git a/skills/product-review/SKILL.md b/skills/product-review/SKILL.md index 0866839..c446db2 100644 --- a/skills/product-review/SKILL.md +++ b/skills/product-review/SKILL.md @@ -51,9 +51,28 @@ Options: - Corporate L&D (companies, HR, L&D teams) - Direct to consumer (students, parents, learners) +### AI Posture Detection + +After Question 4, determine whether the product involves AI. If the founder's product description from Question 1 or intended outcome from Question 3 mentions AI, machine learning, LLM, adaptive, personalized learning engine, or similar, ask via AskUserQuestion: + +"How central is AI to your product?" + +Options: +- AI IS the product — the core workflow is impossible without it +- AI is a significant feature — it enhances the product meaningfully but the product works without it +- AI is a minor or planned feature — optional, supplementary, or not yet built +- No AI component + +Map the answer: +- "AI IS the product" = AI-native. Score AI Architecture dimension in Phase 2. Read `data/ai-native-framework.md`. +- "Significant feature" = Borderline. Apply the removal test from `data/ai-native-framework.md`: "If you removed all the AI, would your product still work?" If yes, it's bolted-on. Score AI Architecture dimension. +- "Minor/planned" or "No AI" = Skip AI Architecture dimension entirely. + +If the founder's description makes AI posture obviously AI-native (e.g., "I built an adaptive AI tutoring engine that generates personalized lessons"), skip the question and state: "Based on what you've described, this is an AI-native product. I'll include AI architecture in the review." + ## Phase 2: Product Assessment -Evaluate the product across five dimensions. Read relevant data files for context: +Evaluate the product across five dimensions (six if AI is involved). Read relevant data files for context: - `data/buyer-personas.md` for buyer requirements - `data/evidence-tiers.md` for outcome measurement - `data/competitive-landscape.md` for competitive context @@ -122,6 +141,47 @@ From `data/evidence-tiers.md`: - Is the product designed to produce measurable outcome data? - Are there built-in assessment or measurement tools? +### Dimension 6: AI Architecture (only if AI posture detection triggered) + +"Is the AI load-bearing or decorative?" + +Read `data/ai-native-framework.md`. Evaluate the product against the 4 AI-native criteria: + +1. **Token/compute spend scales with usage** — Does heavier usage mean more AI computation? Or is AI a fixed feature regardless of usage? +2. **Improves with base models** — When a better LLM ships, does this product automatically get better? Or is the AI a static integration? +3. **Core workflow impossible without AI** — Apply the removal test. Remove the AI. Does the product break? +4. **Creates behavior change** — Do users work differently after trying this? Or do they try the AI once and go back to the old way? + +Score on the AI-native criteria (use even numbers to distinguish from the 1-10 scale of other dimensions): +- **10:** All 4 criteria met. Architecture is model-agnostic. Clear token economics. Memory/personalization across sessions. +- **8:** 3 of 4 criteria met. Strong AI integration but missing memory, behavior change, or model-agnostic design. +- **6:** 2 of 4 criteria met. AI is meaningful but not load-bearing. The product is in a borderline zone. +- **4:** 1 criterion met. AI is a significant feature but the product works without it. Bolted-on with potential. +- **2:** 0 criteria met but product claims to be "AI-powered." Sparkle icons and a chat pane. + +**If score < 6, output an AI ARCHITECTURE WARNING:** + +``` +AI ARCHITECTURE WARNING +━━━━━━━━━━━━━━━━━━━━━━━ +Your product scores [X]/10 on AI architecture. + +Missing criteria: +• [list which of the 4 criteria are not met] + +Removal test result: [product works / breaks without AI] + +This matters because: +• Buyers increasingly ask "is this real AI or a chatbot wrapper?" +• AI-native competitors will improve every 6 months while bolted-on products stay static +• Pricing and fundraising strategies differ fundamentally based on AI architecture + +Remediation: +• [specific steps from data/ai-native-framework.md to move toward AI-native] +``` + +Note: bolted-on is not always wrong. If the product solves a real problem without AI and the AI features genuinely enhance it, say so. The warning is diagnostic, not a judgment. Help the founder understand the implications for their strategy. + ## Phase 3: Review Output ``` @@ -139,6 +199,7 @@ User Experience [1-10] [one-line assessment] Buyer Requirements [1-10] [one-line assessment] Differentiation [1-10] [one-line assessment] Evidence Readiness [1-10] [one-line assessment] +AI Architecture [1-10] [one-line assessment] ← only if AI involved ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Overall [avg] @@ -195,6 +256,9 @@ Recommend the single most relevant next step based on the scorecard: **If Evidence Readiness scored < 7:** "You need stronger evidence before institutional buyers will take you seriously. Run `/evidence-check` to assess your evidence tier and build a plan." +**If AI Architecture scored < 5 (and AI is involved):** +"Your AI architecture needs rethinking before you invest in go-to-market. Your product claims to be AI-powered but the AI isn't load-bearing. Read the remediation steps above. Redesign so AI is essential to the workflow, or own being a non-AI product with AI features — that's a valid position, but your pricing and fundraising strategy need to reflect it." + **If all scores are 7+:** "Your product is ready. Now build the sales engine. Run `/go-to-market` to create your edtech GTM strategy." From 49ed4333886e306bc825eff257cafdcffd9da595 Mon Sep 17 00:00:00 2001 From: Philippos Savvides Date: Wed, 1 Apr 2026 15:55:24 -0700 Subject: [PATCH 3/6] feat: make GTM, pitch, sales, and fundraising skills AI-native aware go-to-market adds usage-based pricing playbook. pitch-review coaches the "improves with models" narrative. sales-strategy adds AI-native objection handling. fundraising-guide targets different VC pools. --- skills/fundraising-guide/SKILL.md | 66 ++++++++++++++++++++++++- skills/go-to-market/SKILL.md | 52 ++++++++++++++++++++ skills/pitch-review/SKILL.md | 31 +++++++++++- skills/sales-strategy/SKILL.md | 81 ++++++++++++++++++++++++++++++- 4 files changed, 227 insertions(+), 3 deletions(-) diff --git a/skills/fundraising-guide/SKILL.md b/skills/fundraising-guide/SKILL.md index b475e7b..205114c 100644 --- a/skills/fundraising-guide/SKILL.md +++ b/skills/fundraising-guide/SKILL.md @@ -70,9 +70,25 @@ Options: - Corporate L&D - Multiple / cross-sector +### AI Posture Detection + +After Question 5, if the founder's product context from prior answers mentions AI, machine learning, LLM, adaptive, or similar, ask via AskUserQuestion: + +"How central is AI to your product?" + +Options: +- AI IS the product — the core workflow is impossible without it +- AI is a significant feature — it enhances the product meaningfully but the product works without it +- AI is a minor or planned feature — optional, supplementary, or not yet built +- No AI component + +Map: "AI IS the product" = AI-native (adapt investor targeting and evidence expectations). "Significant feature" = Borderline. "Minor/planned" or "No AI" = Skip AI-specific fundraising guidance. + +If AI posture is obvious from prior answers, skip the question and state the classification. + ## Phase 2: Funding Landscape Briefing -Read `data/funding-landscape.md` and provide targeted guidance. +Read `data/funding-landscape.md` and (if AI is involved) `data/ai-native-framework.md`. Provide targeted guidance. ``` YOUR FUNDRAISING LANDSCAPE @@ -107,6 +123,54 @@ WHAT THESE INVESTORS EXPECT AT YOUR STAGE: • Market: [how they want the market sized] ``` +### AI-Native Investor Dynamics (if AI posture detected) + +**If AI-native:** + +``` +AI-NATIVE FUNDRAISING DYNAMICS +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +Your investor pool is different. AI-native edtech attracts: + +1. AI-focused VCs who understand token economics and model architecture. + They care about: model-agnostic design, usage-based unit economics, + whether the product improves with base model upgrades, and defensibility + beyond a thin API wrapper. + +2. Crossover VCs who invest in both AI infrastructure and vertical applications. + They want to see: a clear wedge into education, network effects or + data flywheel, and why a general-purpose AI tool won't eat your lunch. + +Timeline advantage: AI-native products can raise pre-revenue on architecture +alone. Traditional edtech VCs want traction. AI-focused VCs fund the +architecture and the team. + +Evidence expectations differ: +• AI-focused investors: model performance metrics, accuracy rates, + user behavior data showing the AI creates real change +• Traditional edtech investors: ESSA-tier evidence, institutional + pilot results, outcome data + +The "improves with models" narrative: +Lead with it. "Our product automatically gets better every 6 months +as base models improve, with zero additional engineering effort." +This is the single most compelling investor narrative in 2026. +``` + +**If bolted-on:** + +``` +BOLTED-ON FUNDRAISING REALITY +━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +Focus on traditional edtech VCs who care about evidence and distribution. +Don't lead with "AI" — lead with outcomes. Your competitive advantage +is institutional relationships, evidence of effectiveness, and distribution, +not AI architecture. AI-focused VCs will probe your architecture and +you'll lose the conversation. Own what you are. +``` + ## Phase 3: Fundraising Strategy ### Raise Sizing diff --git a/skills/go-to-market/SKILL.md b/skills/go-to-market/SKILL.md index e129c89..856e03a 100644 --- a/skills/go-to-market/SKILL.md +++ b/skills/go-to-market/SKILL.md @@ -62,6 +62,22 @@ Options: - We have a dedicated sales team - Nobody — we don't do outbound +### AI Posture Detection + +After Question 5, if the founder's product description from Question 1 mentions AI, machine learning, LLM, adaptive, or similar, ask via AskUserQuestion: + +"How central is AI to your product?" + +Options: +- AI IS the product — the core workflow is impossible without it +- AI is a significant feature — it enhances the product meaningfully but the product works without it +- AI is a minor or planned feature — optional, supplementary, or not yet built +- No AI component + +Map: "AI IS the product" = AI-native (adapt pricing and positioning in Phases 2-3). "Significant feature" = Borderline (note for pricing section). "Minor/planned" or "No AI" = Skip AI-specific GTM guidance. + +If AI posture is obvious from the product description, skip the question and state the classification. + ## Phase 2: Segment Strategy Read `data/buyer-personas.md`, `data/procurement-guide.md`, and `data/competitive-landscape.md`. @@ -173,6 +189,42 @@ Procurement-friendly tactics: - Multi-year deals are preferred by institutions (budget predictability) and better for your cash flow - Freemium works for teacher adoption but the conversion path to institutional purchase must be clear +### AI-Native Pricing (if AI posture = AI-native or borderline) + +Read `data/ai-native-framework.md` for the full pricing framework. + +**If AI-native:** Your product has variable COGS. A power user running AI tutoring for 200 students consumes far more compute than a light user. This breaks per-seat pricing at scale. Guide the founder: + +``` +AI-NATIVE PRICING STRATEGY +━━━━━━━━━━━━━━━━━━━━━━━━━━ + +Your AI is load-bearing. Your pricing should reflect that. + +Structural advantage: Usage-based or outcome-based pricing +• Per-token, per-session, per-assessment, per-learner-hour +• Aligns COGS with revenue — heavy users pay proportionally +• Tells the investor story: "revenue scales with value delivered" + +But: match pricing to what the buyer can purchase. +• Districts and universities: per-seat or site-license + usage tier + (procurement systems need a predictable line item) +• Direct-to-consumer: pure usage-based or outcome-based +• Enterprise: hybrid (base subscription + usage tier) + +Key metric: unit economics at 10x scale +• What does it cost to serve one student for one semester? +• Does that number go down with scale (good) or up (dangerous)? +• What's the margin at your target price point? + +Model improvement positioning: +• Your product gets better every 6 months as base models improve +• This is your GTM narrative: "you're buying into an improving curve" +• Competitors with static products can't match this +``` + +**If bolted-on AI:** Note for the founder: "Your AI features add value but your COGS structure is closer to traditional SaaS. Per-seat pricing is fine for your model. Focus on feature differentiation rather than AI architecture as your competitive positioning." + ## Phase 4: Calendar and Timing Read `data/procurement-guide.md` for budget cycle guidance. diff --git a/skills/pitch-review/SKILL.md b/skills/pitch-review/SKILL.md index e2c5417..03d4ace 100644 --- a/skills/pitch-review/SKILL.md +++ b/skills/pitch-review/SKILL.md @@ -41,9 +41,25 @@ Options: - $500K - $2M ARR - Over $2M ARR +### AI Posture Detection + +After Question 3, if the pitch content mentions AI, machine learning, LLM, adaptive, or similar, ask via AskUserQuestion: + +"How central is AI to your product?" + +Options: +- AI IS the product — the core workflow is impossible without it +- AI is a significant feature — it enhances the product meaningfully but the product works without it +- AI is a minor or planned feature — optional, supplementary, or not yet built +- No AI component + +Map: "AI IS the product" = AI-native (adapt Solution/Product scoring and investor targeting). "Significant feature" = Borderline (note for investor Q&A prep). "Minor/planned" or "No AI" = Skip AI-specific pitch guidance. + +If AI posture is obvious from the pitch content, skip the question and state the classification. + ## Phase 2: Pitch Assessment -Read `data/funding-landscape.md`, `data/evidence-tiers.md`, and `data/competitive-landscape.md`. +Read `data/funding-landscape.md`, `data/evidence-tiers.md`, `data/competitive-landscape.md`, and (if AI is involved) `data/ai-native-framework.md`. Evaluate across six dimensions: @@ -58,6 +74,14 @@ Evaluate across six dimensions: - Is the demo/screenshot compelling? - Does the AI component (if any) have a clear purpose, or is it "AI for AI's sake"? +**If AI-native:** Evaluate whether the pitch communicates the AI architecture advantage: +- Does it explain that the product improves automatically as base models improve? (This is the strongest narrative in 2026 edtech investing. The investor is buying into an improving curve, not a static product.) +- Does it show token economics / unit economics that scale with usage? +- Does it demonstrate behavior change, not just engagement? +- Does the demo show the AI doing the core work, or is the AI hidden behind a feature button? + +**If bolted-on:** Flag for investor Q&A prep: "Investors will ask: 'What happens when OpenAI/Anthropic ships this as a feature?' Have a clear answer — either your data moat, your domain-specific training, your distribution advantage, or an honest acknowledgment that AI is supplementary." + ### 3. Market Sizing - Is the market sized correctly for education? - Common mistake: using "global education market" ($X trillion). Investors see through this. @@ -124,9 +148,13 @@ For your stage ([pre-seed/seed/A]): Best-fit investors: • [2-3 specific fund names from funding-landscape.md with why they fit] +• [If AI-native: include AI-focused VCs who understand token economics and model architecture] +• [If bolted-on: focus on traditional edtech VCs who care about evidence and distribution] What these investors specifically look for: • [Specific criteria for this stage] +• [If AI-native: model-agnostic design, clear token economics, product that rides the model improvement curve] +• [If bolted-on: evidence of outcomes, institutional traction, defensible distribution advantage] Evidence expectations at this stage: • [What tier they need and what to say if they're not there yet] @@ -167,6 +195,7 @@ RECOMMENDED PITCH STRUCTURE (10-12 slides) 3. Solution (2 slides) [What the product does — screenshot or demo video, not feature list] + [If AI-native: include one slide on AI architecture — show it's load-bearing, model-agnostic, improves with base models. This is your moat slide.] 4. Evidence (1 slide) [Current evidence tier, key results, research plan] diff --git a/skills/sales-strategy/SKILL.md b/skills/sales-strategy/SKILL.md index 03f4e1c..6b17ab4 100644 --- a/skills/sales-strategy/SKILL.md +++ b/skills/sales-strategy/SKILL.md @@ -54,9 +54,25 @@ Options: - Price objections - Long sales cycles (it's taking forever) +### AI Posture Detection + +After Question 1, if the product description mentions AI, machine learning, LLM, adaptive, or similar, ask via AskUserQuestion: + +"How central is AI to your product?" + +Options: +- AI IS the product — the core workflow is impossible without it +- AI is a significant feature — it enhances the product meaningfully but the product works without it +- AI is a minor or planned feature — optional, supplementary, or not yet built +- No AI component + +Map: "AI IS the product" = AI-native (adapt objection handling and demo guidance). "Significant feature" = Borderline (note for buyer perception section). "Minor/planned" or "No AI" = Skip AI-specific sales guidance. + +If AI posture is obvious from the product description, skip the question and state the classification. + ## Phase 2: Buyer-Specific Sales Playbook -Read `data/buyer-personas.md` and `data/procurement-guide.md`. +Read `data/buyer-personas.md` and `data/procurement-guide.md`. If AI is involved, also read `data/ai-native-framework.md`. Based on their answers, provide a sales playbook tailored to their specific buyer. @@ -257,6 +273,69 @@ You can't eliminate the cycle, but you can stop restarting it: presentation slide. Make it easy for your champion to sell internally. ``` +### AI-Specific Sales Dynamics (if AI posture detected) + +**If AI-native:** + +``` +AI-NATIVE SALES DYNAMICS +━━━━━━━━━━━━━━━━━━━━━━━━ + +Objections you'll hear (and how to handle them): + +"What if the AI is wrong?" +→ Don't dodge this. Say: "It will be sometimes. Here's how we handle it: + [explain your guardrails, human-in-the-loop, confidence thresholds]. + The question isn't 'is it perfect?' — it's 'is it better than the + alternative?'" + +"How do you handle hallucinations?" +→ Be specific about your approach: content filtering, source grounding, + domain-specific fine-tuning, or human review layers. Vague answers + kill trust. + +"What about student data going to AI models?" +→ Know your model provider's data policy cold. Can you say "no student + data trains the model"? If yes, lead with it. If not, explain your + data handling clearly. + +"What happens when OpenAI/Anthropic ships this as a feature?" +→ This is the bolted-on question. If you're genuinely AI-native: + "Our AI is purpose-built for [specific educational workflow]. A + general-purpose model can't do this without the domain-specific + [training/data/workflow] we've built." + +Demo flow for AI-native products: +• Don't give a feature tour. Show the AI doing the core work. +• Live demo > recorded video. Let the buyer see it work in real time. +• Show the product handling edge cases (not just the happy path). +• Show memory/personalization: "This student used it last week and + here's how it adapted." + +Procurement narrative: +• Outcome-based pricing aligns with institutional ROI conversations +• "You're paying for results, not seats" resonates with budget-conscious + buyers +``` + +**If bolted-on:** + +``` +AI PERCEPTION MANAGEMENT +━━━━━━━━━━━━━━━━━━━━━━━ + +Buyers are getting sophisticated about detecting decorative AI. + +If a buyer asks "Is this real AI or a chatbot wrapper?" +→ Be honest. If your AI enhances specific workflows, describe exactly + which ones and what they do without the AI. Honesty builds trust. + "AI improves our [specific feature] by [specific amount]. The core + product works without it, but the AI makes [specific workflow] + significantly better." + +Don't over-sell the AI. Sell the product. +``` + ## Phase 3: Sales Action Plan ``` From 59ee3a389149dc4ae3b1c383c8b4fb07662d1092 Mon Sep 17 00:00:00 2001 From: Philippos Savvides Date: Wed, 1 Apr 2026 15:55:29 -0700 Subject: [PATCH 4/6] feat: add light AI-native awareness to remaining 4 skills edtech-landscape maps AI-native competitors. evidence-check adds behavior change dimension. accessibility-check flags AI bias and transparency. pilot-design adds AI-specific metrics. --- skills/accessibility-check/SKILL.md | 10 ++++++ skills/edtech-landscape/SKILL.md | 8 ++++- skills/evidence-check/SKILL.md | 30 +++++++++++++++++ skills/pilot-design/SKILL.md | 50 +++++++++++++++++++++++++++++ 4 files changed, 97 insertions(+), 1 deletion(-) diff --git a/skills/accessibility-check/SKILL.md b/skills/accessibility-check/SKILL.md index 4509998..cc94877 100644 --- a/skills/accessibility-check/SKILL.md +++ b/skills/accessibility-check/SKILL.md @@ -60,6 +60,16 @@ Options (multiSelect: true): - User-generated content - AI-generated content +### AI-Specific Accessibility (if "AI-generated content" selected in Question 4) + +If the founder selected "AI-generated content" in Question 4, OR if the product description from earlier questions mentions AI, machine learning, LLM, adaptive, or similar, read `data/ai-native-framework.md` and add these AI-specific accessibility concerns to the Phase 2 assessment: + +- **Algorithmic bias:** Does the AI perform equally across demographics? An AI tutoring system that works better for native English speakers than English language learners has an accessibility problem. Test AI output across diverse learner populations. +- **Transparency:** Can users understand why the AI made a recommendation? If a student gets a learning path and has no idea why, that's a transparency gap. +- **Explainability:** Can the system explain its reasoning in plain language? Educators need to validate AI recommendations before acting on them. +- **Override capability:** Can users override AI recommendations? AI that makes decisions without human review creates an accessibility barrier for users whose needs fall outside the model's training data. +- **Data consent:** Do users understand what data feeds the AI? Informed consent is both an accessibility and privacy requirement. + ## Phase 2: Requirements Assessment Based on their sector, explain the specific requirements that apply: diff --git a/skills/edtech-landscape/SKILL.md b/skills/edtech-landscape/SKILL.md index 5e023db..af06f51 100644 --- a/skills/edtech-landscape/SKILL.md +++ b/skills/edtech-landscape/SKILL.md @@ -142,7 +142,13 @@ Output: - Common failure modes for startups in this segment - Where the gaps and opportunities are -**If their product uses AI:** Call out the AI-specific competitive landscape for their sector. The AI landscape is moving fast — note that competitive positions may shift quickly and they should verify current status. +**If their product uses AI (Question 5: "central" or "enhances"):** Read `data/ai-native-framework.md`. Call out the AI-specific competitive landscape for their sector. The AI landscape is moving fast — note that competitive positions may shift quickly and they should verify current status. + +Classify their AI posture based on Question 5: +- "AI is central" = AI-native. Note: "Most edtech AI products are bolted-on. Being genuinely AI-native is a differentiator — your product improves automatically as base models improve, which most competitors can't match." +- "AI enhances some features" = Borderline/bolted-on. Flag: "Your AI enhances but isn't essential. Competitors building AI-native in your space will improve faster. Consider whether your AI can become load-bearing, or compete on distribution and evidence instead. Run `/product-review` for a detailed AI architecture assessment." + +Map AI-native vs bolted-on competitors in their specific segment when providing the 3-5 key competitors list. ## Phase 5: Situational Brief diff --git a/skills/evidence-check/SKILL.md b/skills/evidence-check/SKILL.md index c9f2389..fd5029e 100644 --- a/skills/evidence-check/SKILL.md +++ b/skills/evidence-check/SKILL.md @@ -64,6 +64,22 @@ Options: - No, but we're open to it - No, and we want to do this ourselves +### AI Posture Detection + +After Question 2, if the founder's product context mentions AI, machine learning, LLM, adaptive, or similar, ask via AskUserQuestion: + +"How central is AI to your product?" + +Options: +- AI IS the product — the core workflow is impossible without it +- AI is a significant feature — it enhances the product meaningfully but the product works without it +- AI is a minor or planned feature — optional, supplementary, or not yet built +- No AI component + +If AI-native or borderline: read `data/ai-native-framework.md` and add "behavior change" as an evidence dimension in Phase 2. AI-native products need to demonstrate they create behavior change (users work fundamentally differently), not just engagement or satisfaction. + +If AI posture is obvious from context, skip the question and state the classification. + ## Phase 2: Evidence Classification Read `data/evidence-tiers.md` for the full ESSA framework. @@ -102,6 +118,20 @@ Without a comparison group or statistical controls, improvement could be from ma **"We compared our schools to other schools and our schools did better" → Depends** If you controlled for prior achievement, demographics, and other differences statistically, this could be Tier 3. If you just compared raw scores without controls, the groups might have been different to begin with. That's Tier 4. +### AI-Native Evidence Dimension (if AI posture detected) + +**If AI-native or borderline:** + +Beyond standard ESSA tier evidence, AI-native products need to demonstrate **behavior change**. This is the 4th AI-native criterion: users work fundamentally differently after trying the product. + +How to measure behavior change: +- **Before/after workflow analysis:** Document how users performed the task before the product, then after 4-8 weeks of usage. Are they working differently, or just slightly faster at the same workflow? +- **Usage persistence:** Do users continue using the AI-powered workflow after the novelty wears off (week 4+)? If usage drops after week 1, the AI isn't creating behavior change. +- **Reversion rate:** When the product is temporarily unavailable, do users revert to old methods or wait for it to come back? Waiting = behavior change. Reverting = nice-to-have. +- **Qualitative indicators:** Users describing their work as "before [product]" and "after [product]." Unprompted advocacy. Expanding usage to new contexts. + +Flag: "Engagement metrics (time on task, session counts) are necessary but not sufficient for AI-native evidence. A chatbot can have high engagement without creating any behavior change. Focus on whether users' workflows actually changed." + ## Phase 3: Gap Analysis Based on the distance between their current tier and their target tier: diff --git a/skills/pilot-design/SKILL.md b/skills/pilot-design/SKILL.md index 706fb7e..4378f6e 100644 --- a/skills/pilot-design/SKILL.md +++ b/skills/pilot-design/SKILL.md @@ -69,6 +69,22 @@ Options: - Full academic year - No specific constraint +### AI Posture Detection + +After Question 1, if the product description mentions AI, machine learning, LLM, adaptive, or similar, ask via AskUserQuestion: + +"How central is AI to your product?" + +Options: +- AI IS the product — the core workflow is impossible without it +- AI is a significant feature — it enhances the product meaningfully but the product works without it +- AI is a minor or planned feature — optional, supplementary, or not yet built +- No AI component + +If AI-native or borderline: read `data/ai-native-framework.md` and add AI-specific pilot metrics to Phase 2. + +If AI posture is obvious from context, skip the question and state the classification. + ## Phase 2: Pilot Design Read `data/pilot-benchmarks.md` for reference benchmarks. @@ -158,6 +174,40 @@ RISK MITIGATION • [Top 3 risks and how to address them] ``` +### AI-Specific Pilot Metrics (if AI posture detected) + +**If AI-native or borderline:** Add these AI-specific metrics to the pilot design: + +``` +AI-SPECIFIC PILOT METRICS +━━━━━━━━━━━━━━━━━━━━━━━━━ + +Model accuracy / quality: +• Track AI output quality over the pilot period +• Measure hallucination rate (incorrect or fabricated information) +• Compare AI recommendations against expert judgment on a sample + +User trust calibration: +• Do users appropriately trust the AI? (not over-trusting, not dismissing) +• Measure how often users accept, modify, or reject AI recommendations +• Track whether trust changes over time (should increase as users learn) + +Behavior change: +• Document workflows before AI (week 0) and after AI (week 4+) +• Measure whether users revert to old methods when AI is unavailable +• Track adoption persistence after novelty wears off (week 4+) + +AI-specific failure modes to monitor: +• Bias in AI output across student demographics +• Performance degradation under load or unusual inputs +• Edge cases where AI produces harmful or inappropriate content +• User confusion when AI makes incorrect recommendations + +If resources allow, consider: +• AI-assisted vs. non-AI-assisted comparison cohort +• Measure whether the AI-specific metrics correlate with learning outcomes +``` + ### Evidence Strategy Based on their current evidence level and pilot goal, provide specific guidance: From 1d7d07acdfdf1c563915c553f8e08e5418877792 Mon Sep 17 00:00:00 2001 From: Philippos Savvides Date: Wed, 1 Apr 2026 15:55:34 -0700 Subject: [PATCH 5/6] chore: bump version and changelog (v1.3.0) Co-Authored-By: Claude Opus 4.6 --- CHANGELOG.md | 20 ++++++++++++++++++++ README.md | 2 +- TODOS.md | 11 +++++++++++ VERSION | 2 +- 4 files changed, 33 insertions(+), 2 deletions(-) create mode 100644 TODOS.md diff --git a/CHANGELOG.md b/CHANGELOG.md index f59c5e5..a988bad 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,25 @@ # Changelog +## 1.3.0 (2026-04-01) + +### AI-native framework integration + +Every skill is now AI-native aware. When a founder's product involves AI, skills detect whether the AI is load-bearing (AI-native) or decorative (bolted-on) and adapt guidance accordingly. + +- New `data/ai-native-framework.md` reference file: 4 AI-native criteria, 5 bolted-on indicators, the removal test, Karpathy hierarchy (for developer-tool founders), architecture patterns, and AI-native pricing models +- `/product-review` adds a 6th scoring dimension (AI Architecture) for products with AI components +- `/idea-validation` evaluates AI Architecture Fit as a validation signal +- `/go-to-market` provides AI-native pricing strategy (usage-based economics, institutional procurement guidance) +- `/pitch-review` coaches the "improves with models" investor narrative and adapts investor targeting +- `/sales-strategy` adds AI-native objection handling ("what if the AI is wrong?") and demo flow guidance +- `/fundraising-guide` targets AI-focused VCs for AI-native products and traditional edtech VCs for bolted-on +- `/edtech-landscape` maps AI-native vs bolted-on competitors in competitive analysis +- `/evidence-check` adds behavior change as an evidence dimension for AI-native products +- `/accessibility-check` flags AI-specific concerns (bias, transparency, explainability, override capability) +- `/pilot-design` adds AI-specific pilot metrics (accuracy, hallucination rate, trust calibration, behavior change) + +The framework is diagnostic, not prescriptive. Bolted-on AI can be a valid strategy. The skills help founders understand the implications for their pricing, sales, fundraising, and competitive positioning. + ## 1.2.0 (2026-03-31) ### Tier-1 repo infrastructure diff --git a/README.md b/README.md index 4f87492..7212f5a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # EdTech Founder Stack -![Version](https://img.shields.io/badge/version-1.2.0-blue) +![Version](https://img.shields.io/badge/version-1.3.0-blue) ![License](https://img.shields.io/badge/license-MIT-green) ![Skills](https://img.shields.io/badge/skills-10-orange) ![Research](https://img.shields.io/badge/papers-376-purple) diff --git a/TODOS.md b/TODOS.md new file mode 100644 index 0000000..2d8a26e --- /dev/null +++ b/TODOS.md @@ -0,0 +1,11 @@ +# TODOs + +## P3: AI-native framework consistency check + +**What:** Add a CI step that validates all 10 SKILL.md files reference `data/ai-native-framework.md` consistently. Grep for framework criteria references and flag drift when the data file evolves. + +**Why:** The AI-native framework is integrated across all 10 skills with duplicated detection logic (each skill asks its own AI posture question). When the framework criteria evolve (and they will, this space moves fast), updating the data file is easy but verifying all 10 skills still align requires manual review. + +**Effort:** S (human: ~2 hours / CC: ~15 min) + +**Depends on:** v1.3.0 shipped (AI-native framework integration) diff --git a/VERSION b/VERSION index 26aaba0..f0bb29e 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -1.2.0 +1.3.0 From 53dc734227cdfc5c752d125fb129c25150eadab5 Mon Sep 17 00:00:00 2001 From: Philippos Savvides Date: Wed, 1 Apr 2026 15:56:20 -0700 Subject: [PATCH 6/6] docs: sync ARCHITECTURE.md with AI-native framework --- ARCHITECTURE.md | 1 + 1 file changed, 1 insertion(+) diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md index 5d7851f..5cf9aab 100644 --- a/ARCHITECTURE.md +++ b/ARCHITECTURE.md @@ -49,6 +49,7 @@ The `data/` directory contains markdown files with structured domain knowledge: - **Market data:** buyer personas, competitive landscape by segment, funding landscape by stage - **Frameworks:** ESSA evidence tiers, procurement guides, pilot benchmarks - **Higher ed framework:** 15 validated jobs across 6 student journey phases (from SXSW EDU 2026), 4 structural patterns founders miss +- **AI-native framework:** 4 AI-native criteria, 5 bolted-on indicators, the removal test, Karpathy hierarchy (for developer-tool founders), architecture patterns, and pricing models. All 10 skills read this file to classify a founder's AI posture and adapt guidance accordingly Skills read these files at runtime using the AI tool's file reading capability. This means the data is always current (edit the markdown, the skill reads the updated version) and auditable (every fact has a source you can check).