Founder's Guide: Avoiding AI-Trivial Startup Ideas
gyani
Context: The problem with “Prompt-Replaced” startups
In the era of GPT-4o, Gemini Pro, Claude 3.5 and beyond, apps like meal planners, greeting generators, quote tools, and basic personal assistants can be replicated in a single prompt or by AI-native OS features.
YC, Product Faculty, and First Round all emphasize: Startups must solve hard, valuable problems, not build features masked as products.
Avoid the SISP trap (Solution in Search of Problem): just because you can build something cool with AI doesn’t mean it solves anything painful. Tech-first ideas must still be user-problem-first.
Just like in finance, where higher return demands higher risk, the most durable startup ideas embrace real risk (deep problem complexity, behavior change, long sales cycles) and thus create outsized value.
Trivial apps avoid risk and deliver trivial returns.
The Framework: "AI-Era Startup Fit"
Inspired by YC's Make something people want, First Round's Enduring Moats, Product Faculty’s Problem-Solution-Persona loop, and SISP warning signals and marketing (segmentation–differentiation) thinking.
Is this a Vitamin or a Painkiller?
(Y Combinator: People don’t buy vitamins in downturns, they buy painkillers.)
| Test | Action |
| Is there existing paid behavior without AI? | If no, it's likely novelty. |
| Will users feel pain daily without your product? | If yes, keep going. |
And if you’re starting with tech looking for a user, you are deep in SISP territory.
In startups, the “painkiller” ideas may look scary or complex, but that’s where risk-adjusted upside lives.
Problem–Solution–Persona Loop (Product Faculty)
A loop that ensures your idea is grounded in unmet needs, not AI-first enthusiasm.
Problem: Is this a deep, frequent, high-stakes problem?
Persona: Who exactly feels this pain? (Not “everyone”)
Solution: Does your solution go beyond what a generic LLM can do with a prompt?
Apply sharp product segmentation: define persona by vertical, job, geography, and context, not “general users”.
If the solution is just “a better UI on ChatGPT”, go back to the drawing board.
If your persona is “people who like productivity”, your segmentation is broken.
Generic products without crisp segments vanish in noise, especially in AI, where horizontal tools are now default.
Litmus Test: "Can LLMs replace this with 1 prompt?"
Inspired by YC's default dead vs default alive mental model.
Ask:
Could a reasonably smart person recreate this product with 1 good ChatGPT prompt and a Google Sheet?
If yes → you are in “prompt trap territory”
If no → explore what value your system provides that LLMs can’t or don’t want to
Moats & Defensibility (First Round + Lenny's Newsletter)
Use the "DEEP Moat" checklist:
Data moat (proprietary, structured, longitudinal)
Ecosystem moat (network effects, integrations)
Execution moat (fast iteration, founder–problem fit)
Process moat (complex workflows, compliance, trust)
Examples:
"Just another email writer" → no moat
"CRM Copilot trained on your dealflow, outcomes, and comms history" → data + workflow moat
AI as Infrastructure, not the Product
YC partners (like Paul Buchheit) suggest: use new tech to enable, not replace.
Don’t build “an AI travel agent”
Build a trip optimization platform that uses AI + APIs + human feedback + logistics orchestration
10x Outcome Rule (Product Faculty + First Principles PM Thinking)
Ask:
Does this make the user's job 10x faster, 10x cheaper, or 10x more insightful?
Is the output uniquely better than ChatGPT with context?
If no → you're adding noise, not value.
If yes → you’re creating leverage, not just features.
Idea Iteration Loop: YC’s Problem → Insight → Wedge
| Step | Action |
| Problem | Find a sharp, specific, underserved pain in a valuable domain |
| Insight | Identify why Gen AI hasn’t fully solved it yet (context? cost? UX?) |
| Wedge | What unique, sharp wedge can you enter with, that scales? |
Caution: What NOT to build in 2025
| Idea | Verdict | Why it fails |
| Meal planner app | ❌ | Solvable in 1 prompt (“Plan my week of meals, 2000 cal/day, 30 mins or less”) |
| Greeting generator | ❌ | AI excels at this with zero context |
| Daily affirmation app | ❌ | Commodity content, zero defensibility |
| Resumé/cover letter AI writer | ⚠️ | Needs proprietary insight to survive |
Mini-Case Comparison Table (Real Startups)
| Startup | Outcome | Insight |
| AI journaling tool | Dead | Prompt-replaceable, lacked context, no moat |
| Lex.page | Fragile | Beautiful editor, but threatened by GPT-4-level writing tools |
| Cresta | Scaling | Deep sales workflow + human-in-the-loop feedback |
| Posthog AI Insights | Strong | Embedded AI inside real product analytics stack |
What to build instead
High-Frustration, High-Stakes Problems:
Legal, medical, financial workflows
Multi-agent decisions (e.g., hiring, B2B sales)
Regulatory compliance, insurance underwriting
Workflow orchestration, not content generation
These are the startup equivalents of high-risk, high-return investment classes.
Consumer AI? Still viable, if you create strong looping behavior, contextual feedback, and unique personalization (e.g., WHOOP GPT, Runway ML, BeReal GenAI remix).
Self-Check questions before you build
| Question | Why it matters |
| Would I still build this if GPT-6 launched tomorrow with memory, multi-modal input, and real-time actions? | Tests if your idea depends on today’s AI limitations. Great startups survive platform shifts. |
| Would users still pay if ChatGPT or Gemini offered this in their free tier? | If your entire value is replicable by a general-purpose LLM, you’re building a feature, not a company. |
| Am I solving a persistent workflow problem, or just generating a one-off output? | Output problems are easy to replace with prompts. Workflow problems involve coordination, decisions, and edge cases — harder to commoditize. |
| Do I understand my user so well that I’m pulling from personal experience or underserved insight? | YC stresses “live in the future, build for yourself.” Startups born from lived pain see around corners and move faster. |
| What makes my solution 10x better, faster, or cheaper for a specific group — not everyone? | Good ideas are differentiated by who they serve, not just what they do. Strong segmentation protects early traction. |
| If this didn’t use AI at all, would it still be worth solving? | Prevents "SISP" trap. If the problem matters without tech, your solution is grounded. If not, it’s novelty-driven. |
| What specific wedge gets me into this market, and what long-term moat keeps me there? | Great companies enter through a sharp wedge and build defensibility over time. Weak ideas start broad and stay fragile. |
| Am I taking the right kind of risk to justify the return? | YC advises solving hard, valuable problems. Risk in TAM, urgency, or execution is fine — risk in relevance or pain is fatal. |
Most reputed ideation frameworks and mental models
| Framework / Model | Key Idea / Principle | Usage / Application | AI-Era Relevance |
| Jobs To Be Done (JTBD): Clayton Christensen (Harvard), Intercom, Reforge | People “hire” products to get a job done in their life not because they want the product, but because they want progress. | Ask: • What job is the user hiring this product to do? • What causes them to fire an existing solution? | Focus on actual progress (e.g., “make better eating choices as a diabetic”) not just tasks (e.g., “generate meal plan”). |
| Problem–Solution–Founder Fit: Y Combinator, Jason Cohen, First Round | Filters ideas through personal insight, real pain, and buildability. | Three filters: 1. Problem Worth Solving: frequent, urgent, costly 2. Solution You Can Build: asymmetric advantage (skills, access) 3. Founder–Problem Fit: authentic obsession or unique insight | Ask: “Am I solving a full problem or just offering a clever shortcut GPT could do better?” |
| 7 Powers Framework: Hamilton Helmer (used at Netflix, Stripe, OpenAI indirectly) | The most strategic framework to assess long-term defensibility. | 7 Moats: 1. Scale Economies 2. Network Effects 3. Counter Positioning 4. Switching Costs 5. Brand 6. Cornered Resource 7. Process Power | Helps you avoid idea areas that lack increasing returns or compounding defensibility. |
| Pain–Frequency–Willingness Model: Product Faculty, PM communities | A fast, simple triage tool for raw ideas. | Score each idea (1–5) on:• Pain level• Frequency• Willingness to pay [Rule of thumb: Don’t build anything that scores below 12/15 on average.] | Avoids building GenAI novelties that aren’t painful or monetizable. |
| "Hair on Fire" Problem Filter: YC & First Round | Filters ideas through urgency and depth of pain. | Ask: If your customer had a hair-on-fire moment, would they Google you like crazy right now? | If not, your problem may be: • Too soft (nice-to-have) • Easily solved with ChatGPT or Google • Not urgent enough to monetize |
| Problem Stack / Layered Friction Model: IDEO + Reforge adaptation | Helps uncover non-obvious problems beneath surface friction. | Levels: 1. Surface friction (visible pain) 2. Workflow friction (system inefficiencies) 3. Coordination friction (multi-agent issues) 4. Cognitive friction (decisions + judgment) 5. Emotional friction (fear, confusion, inertia) | Most AI tools solve only surface friction. Great ideas solve layers 3–5. |
| Barbell Ideation Model: Inspired by Nassim Taleb + a16z | Strategic mental model for AI-native founders. | • One side: Ultra-niche, deep workflow problems (e.g., underwriting for rural insurers in India) • Other side: Massive infra/platform plays (e.g., LangChain, LlamaIndex, AI agents orchestration) [Avoid the middle: generic apps, templates, wrappers, most prone to disruption.] | Highlights why basic apps (planners, generators) will get replaced by GPT, but workflow-specific infra won’t. |
| Wardley Mapping: Simon Wardley, used by AWS, UK Gov, strategy teams | Maps current landscape: what’s novel vs. commoditized? Predict what GenAI will eat next. | • Map current landscape • Predict what GenAI will commoditize next • Avoid building in rapidly commoditizing zones (like content generation) | Avoids building in spaces where GPT/Claude will dominate (e.g., content, summarization, writing). |
| DAOs Framework for Evaluating AI Startup Ideas: Inspired by Paul Graham + Product-Led Growth | Build AI-native businesses, not wrappers. | • Distribution – Non-paid growth loop? • AI-native Differentiation? • Outcome > Output? • Switching Cost? | Directly filters prompt-wrapper apps from true AI-native, defensible products. |
| Reforge Idea Scoring System: Reforge’s Growth Series | Practical for prioritizing ideas with limited time. | Score ideas on:• Impact potential• Time to launch• Effort required• Defensibility• Strategic alignment | Ideal for fast-moving AI founders exploring many ideas at once. |
| SISP (Solution In Search of Problem): Y Combinator | Trap Warning: Don’t start with a shiny tool looking for a user. Start with a user who has a problem. | Ask:• Is this a real problem people have today?• If AI didn’t exist, would it still be worth solving?• Who loses sleep over this problem? | Most prompt-era ideas fall into SISP trap: "I built it with GPT" ≠ real use case. |
| Product Segmentation Model: First Round, April Dunford | Products don't fail from bad tech — they fail from poor segmentation. | Segment clearly by: • Persona • Vertical • JTBD • Psychographics • Behavior | “AI for everyone” ≠ good idea. “AI for underwriters at midsize insurers in LATAM” might be. |
References and further reading:
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Written by
gyani
gyani
Here to learn and share with like-minded folks. All the content in this blog (including the underlying series and articles) are my personal views and reflections (mostly journaling for my own learning). Happy learning!