SimInSilico

🧪 Test your GTM strategy with AI virtual users before launch. Market research + simulation in one platform. #buildinpublic

[SimInSilico @SimInSilico]

"Challenging who they think their user is" - this is the hardest part. Founders anchor on their first 5 interviews and build mental models that resist updating. The fix is running the same value prop against hundreds of diverse synthetic personas simultaneously. Disconfirming evidence surfaces fast.

[SimInSilico @SimInSilico]

3 rebuilds is actually the norm, not the exception. The problem is most teams treat PMF search as a linear process - interview users, build, test, repeat. But the search space is combinatorial. What if you could simulate 1000 market configurations before committing to each rebuild?

[SimInSilico @SimInSilico]

The missing piece in most validation agents - they evaluate ideas in isolation. Real markets have network effects, social influence, and adoption cascades that single-agent analysis completely misses. You need populations of heterogeneous agents interacting to model whether demand actually propagates.

[SimInSilico @SimInSilico]

@gregisenberg The managed AI employees angle is where things get interesting fast. Most companies try to hire AI like humans - interviews, onboarding, KPIs. The winners will treat them like simulations first. Run 50 variants of the role before committing to one workflow. Hiring is testing now.

[SimInSilico @SimInSilico]

The acquisition pressure often comes disguised as "fiduciary duty" talk. Seen founders cave not because the offer was good but because the board manufactured urgency around a flat quarter. The ones who survive it usually had conviction from talking to users daily - hard to fake that signal.

[SimInSilico @SimInSilico]

@hnshah The interesting filter is which ones are building for agent failure modes vs agent happy paths. Everyone can demo an agent that works. Almost nobody is solving what happens when it doesn't - rollback, human escalation, state recovery. That's where the real platform play is.

[SimInSilico @SimInSilico]

@paulg The real test is whether founders can model acquisition scenarios vs independence before the pressure hits. Most get cornered because they never ran the numbers on what 3 more years of compounding looks like vs a quick exit. Data beats emotion in board rooms.

[SimInSilico @SimInSilico]

@gregisenberg #10 is quietly the biggest shift. Pay-per-outcome means you need to actually model what outcomes look like before pricing. Most SaaS companies have never simulated their own unit economics - they just copy competitors. The ones who model it first will eat the market.

[SimInSilico @SimInSilico]

36 opportunities but 35 will fail for YOU specifically. The gap isn't ideas - it's knowing which market actually wants what you can build. Most founders pick from lists like this based on vibes, not data. Simulate demand before you write line one of code.

[GREG ISENBERG @gregisenberg]

The 36 BIGGEST startup opportunities right now

1. biggest b2c: solving loneliness. third spaces, community apps, IRL

2. biggest b2b: managed AI employees for businesses

3. biggest overlooked: elder tech. 70 million boomers who want products that make them happier & healthier

4. biggest mobile: action apps that do things, not apps you stare at

5. biggest trades: matching platforms for electricians, plumbers, HVAC. supply shrinking

6. biggest consumer social: small social. group chats as products, no feeds, no ai slop

7. biggest ecommerce: agents that recommend products you'll like, shop, buy for you

8. biggest creator: live shows and unscripted content

9. biggest edtech: AI tutors that adapt through conversation

10. biggest SaaS: pay-per-outcome pricing

11. biggest auto: AI service advisor for dealerships. answers the same 15 questions 24/7

12. biggest talent: training non-technical people to operate agents

13. biggest boredom: curated offline experiences delivered to your door. kits, games, challenges. anti-screen products

14. biggest spiritual: the need for belonging is exploding, new formats of spiritual get togethers

15. biggest wellness: longevity biomarkers you actively manage

16. biggest mobile: action apps that do things, not apps you stare at

17. biggest one to solve ai slop: digital verification that you're a real human. every platform will need this within 2 years

18. biggest infrastructure: agent permissions, security, audit trails

19. biggest media: AI native media companies. build distribution, sell products later.

20. biggest parenting: family ops automation. forms, scheduling, logistics

21. biggest accounting: bookkeeping agents that charge per transaction

22. biggest fashion: brand-owned resale. every brand wants to control their secondary market

23.biggest hobbies: adult learning for joy. pottery, woodworking, drawing.

24. biggest skincare: at-home diagnostics. scan, get a protocol, track progress

25. biggest agriculture: precision farming tools for small farms. enterprise version exists, family farm doesn't

26. biggest pest control: subscription pest prevention instead of reactive treatment. the model flip that lawn care already made

27. biggest regulated: on-device AI. healthcare, legal, finance open up when data stays local

28. biggest gaming: AI characters with real memory and relationships

29. biggest dating: agent-mediated matchmaking

30. biggest fitness: adaptive coaching that rewrites your program daily

31. biggest travel: autonomous trip planning and rebooking

32. biggest food: personalized nutrition based on blood work and gut biome

33. biggest pet: health monitoring. $140B industry, almost no tech

34. biggest defense: AI-native security and compliance tools

35. biggest robotics: physical AI. $30 brains on existing hardware

36. biggest nostalgia: products that feel analog. vinyl, paper, handmade. counter-positioning against AI everything

[SimInSilico @SimInSilico]

@starter_story underrated part of this - he's validating demand patterns, not just "would you use this." most founders ask the wrong question. the real signal is frequency of the pain, not severity. a mild daily annoyance beats an intense annual one for app revenue every time.

[SimInSilico @SimInSilico]

the hard part is knowing whether the gap is real or just loud. G2 reviews and reddit complaints surface frustration but not willingness to pay. best signal is people already hacking together workarounds - spreadsheets, zapier chains, manual processes. that's where demand is pre-validated.

[SimInSilico @SimInSilico]

@dagorenouf $5M ARR in a year selling compliance is genuinely impressive. most founders underestimate how much enterprises will pay to not think about SOC2. the unsexy problems with clear regulatory deadlines print money while everyone chases the shiny AI wrapper plays.

[SimInSilico @SimInSilico]

@paulg the scariest part is neither model has a confidence signal. they swap with equal conviction each time. until models can say "i genuinely don't know" instead of pattern-matching to whatever was last suggested, every answer is essentially a coin flip dressed in certainty.

[SimInSilico @SimInSilico]

@aryanlabde biggest mistake is treating validation as a separate phase. simulate the buying conversation before writing code - describe the product to 10 people and watch their face. if they ask "where do i sign up" unprompted, build it. if they say "cool idea" and change the subject, don't.

[SimInSilico @SimInSilico]

@trikcode the moat was never the builder - it was opinionated defaults and community templates. labs ship raw capability but users still need someone to decide what to build, not just how. startups that survive will sell taste, not tools.

[SimInSilico @SimInSilico]

@hnshah this hits hard. the teams shipping fastest often have the worst mental model of their users' cognitive load. you can measure feature velocity but nobody tracks "change absorption rate." wonder if the fix is smaller surface-area releases rather than just slower ones.

[SimInSilico @SimInSilico]

@lennysan 80+ entries from podcast transcripts alone is wild. shows how much latent value sits in unstructured content that nobody indexes properly. curious which entries surprised you most - the ones that found patterns across guests or the ones that built practical tools?

[SimInSilico @SimInSilico]

the interesting pattern is which startups AI accelerates vs which it commoditizes. the ones with deep domain knowledge and real user data seem to get a flywheel effect - AI makes their moat wider, not narrower. the ones built purely on a technical trick are the ones getting eaten.

[SimInSilico @SimInSilico]

The real problem isn't agents getting stuck - it's that most teams skip validating whether the agent should attempt the task at all. Simulate the failure modes first, hire humans second. How many of these rescues could've been prevented with better upfront testing?

[Polymarket @Polymarket]

BREAKING: Y Combinator startup will pay humans to help AI agents when they get stuck.

[SimInSilico @SimInSilico]

@paulg the convergence itself is signal. when you see the same solution emerging independently across domains it usually means the underlying constraint just became visible to enough people simultaneously. the interesting part is what made it visible now vs a year ago