"Should we add AI?" Here is how to decide
A clear decision framework for the exact moment every founder faces: bolt on AI, ignore it, or rethink the product.
Open founderprompts.com →
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Should my startup add AI features, and which ones actually matter to users?
The short answer
Users do not care that a feature uses AI; they care whether it removes a slow, painful, judgment-heavy step from their day. Start from a specific friction point in your existing workflow, not from the technology, and be honest about whether AI is a checkbox for investors or a real 10x on time saved. The strongest AI features sell the finished work (a resolved ticket, a drafted document), not a fancier tool.
A quick orientation. The real value is below: resources worth your time, from people who've actually done it.
20 resources worth your time
AI Features Must Solve Real User Problems
The UX research heavyweights on why novelty chatbots harm products and what user value actually looks like.
Open nngroup.com →The State of AI 2025
VC-grade market data on which AI product categories are actually winning, so you bet where demand is proven.
Open bvp.com →State of Consumer AI 2025: Product Hits, Misses, and What's Next
A blunt scorecard of which AI product ideas changed user behaviour and which quietly flopped.
Open a16z.com →Where Does Consumer AI Stand at the End of 2025?
Four consumer investors on why subtle product choices now matter more than raw model quality.
Open a16z.com →AI startups: Sell work, not software
The canonical reframe: sell the finished work, not a 15% productivity tool, and your market gets 10-50x bigger.
Open sarahtavel.com →A few "Sell Work, Not Software" updated thoughts
Tavel pressure-tests her own thesis with what she has since seen in real AI startups.
Open sarahtavel.com →How AI is reshaping the product role | Oji and Ezinne Udezue
Veteran product leaders on AI at the core versus AI at the edge, the distinction most founders miss.
Open lennysnewsletter.com →The Truth About Building AI Startups Today (Lightcone Ep. 1)
YC group partners distil patterns from hundreds of AI startups they funded, including what users reward.
Open ycombinator.com →
A Cheeky Pint with Intercom Cofounder Des Traynor
Traynor on growing Fin and why selling AI products is genuinely hard, from a founder who bet the company on it.
Watch on YouTube youtube.com →Des Traynor on reinventing Intercom and avoiding "foie gras" features
The full transcript, including the memorable rule against force-feeding users AI they never asked for.
Open cheekypint.substack.com →Bitter lessons building AI products
A rare honest post-mortem from a product team on which AI bets worked in production and which had to be killed.
Open hex.tech →The 30 Best Pieces of Company Building Advice We Heard in 2025
Includes Carta's hard-won lesson on separating model behaviour from UX when shipping AI features.
Open review.firstround.com →Superhuman CEO on the 40% PMF Rule and AI-Native Productivity
Vohra applies his famous product-market-fit engine to deciding which AI features earn their place.
Open productschool.com →
Rahul Vohra on How AI Will Change the Way We Work (Upfront Summit)
How Superhuman designed Summarize and Instant Reply so users send 72% more email per hour.
Watch on YouTube youtube.com →"Does my new startup idea need to have AI in it?"
A top consumer investor answers the exact question, drawn from hundreds of a16z Speedrun applications.
Open x.com →What We Learned Building AI Products in 2025
A data company's candid recap of which of its own AI features users adopted and why.
Open amplitude.com →How Intercom navigated the AI paradigm shift
A case study of an incumbent rebuilding around one AI feature that customers demonstrably pay for.
Open bvp.com →The Force-Feeding of AI on an Unwilling Public
The sharpest counterargument you will read; know the backlash before you ship an unwanted AI feature.
Open honest-broker.com →Building My First AI Product: 6 Lessons from My 90-Day Deep Dive
A respected discovery coach documents what surprised her when she actually built an AI product herself.
Open producttalk.org →More in AI in Your Product
- How do founders ship their first AI feature (APIs, wrappers, RAG in plain terms)? 19 →
- How much does it cost to run AI features, and how do I keep costs sane? 19 →
- Which AI model should I build on (GPT, Claude, Gemini, open-source), and how do I avoid locking myself in? 17 →
- How do I test whether my AI feature actually works before launch (evals, in plain terms)? 18 →
- How do I stop my AI feature from making things up in front of customers? 17 →
- Prompt engineering, RAG, or fine-tuning: which one does my product actually need? 18 →
- How do I design AI features people trust (chat vs buttons, streaming, showing the work)? 18 →
- What is my moat if I'm building on someone else's model? 19 →
- Should I put an AI agent in my product, or is a simple workflow enough? 18 →
- Is it safe to send customer data to AI APIs, and what about privacy laws like India's DPDP Act? 18 →
- How should I price AI features: per seat, per use, or per outcome? 17 →
- My AI feature is live. How do I monitor it and make it better with real usage? 18 →