Manas Bihani
About

the questions

  1. What is a moat in an AI world?
  2. Why do AI products converge?
  3. What becomes scarce when intelligence becomes cheap?
  4. Does distribution matter more than technology?
  5. Why might human-made things become more valuable?
  6. What happens to expertise when everyone has the same models?
  7. Which parts of an AI startup are actually defensible?
  8. Where does value move when intelligence becomes commoditized?

everything on the desk

  1. The periodic table of the AI stackVisualization
  2. What is a moat when the model isn't yours?Note
  3. The problem-selection premiumNote
  4. Same model, different wiringNote
  5. Selection is the new bottleneckNote
  6. Get friendly with the AI raceEssay
  7. The convergence taxNote
  8. The luxury of realityNote
  9. The non-technical technical advantageNote
  10. The verification economyNote
  11. The bets against the wallVisualization
  12. You can't buy your way outVisualization
  13. How a chatbot writes one wordVisualization
  14. The grid is the last wallVisualization
  15. Who got paidVisualization
  16. Why this paper mattersExplainer
  17. Transformer: Why did transformers replace RNNs?Vaswani et al., NeurIPS 2017
  18. KV cache: Why does a long conversation get slower and cost more than a short one?Shazeer, 2019
  19. Mixture of experts: Why do some AI models have experts?Fedus, Zoph and Shazeer, 2021
  20. FlashAttention: Why is attention slow when the GPU is barely doing any arithmetic?Dao et al., NeurIPS 2022
  21. Mamba: Why does a model reread the whole conversation instead of just remembering it?Gu & Dao, 2023
  22. PagedAttention: Why does a GPU with free memory still refuse new requests?Kwon et al., SOSP 2023
  23. DeepSeek: How did DeepSeek train a frontier model so cheaply?DeepSeek-AI, 2024
  24. Jamba: Why does Jamba matter?Lieber et al., AI21 Labs, 2024
  25. BitNet: Why does BitNet matter?Ma et al., Microsoft Research, 2025
  26. DeepSeek-R1: Can a small AI model learn to reason like a huge one?DeepSeek-AI, 2025
  27. Kimi K2: Why does Kimi K2 matter?Kimi Team, Moonshot AI, 2025
  28. Sliding-window attention: How do models handle huge context windows without the memory bill exploding?Gemma Team, Google DeepMind, 2025
  29. How electricity becomes intelligenceVisualization
  30. This desk, as a datasetDataset
  31. The first version of this roomNote
  32. The aura dividendNote
  33. Distribution is rented attentionNote
  34. The Convergence TestNote
  35. A shelf for thinking about cheap intelligenceCollection
  36. Anatomy of an AI startupNote
  37. Six shocks to expertiseNote
  38. Nineteen Public KeysEssay
  39. The value migration machineModel
  40. AAA-Rated GPUsEssay
  41. Moats, before and afterVisualization
  42. The rhinoceros problemNote
  43. What becomes scarce when intelligence becomes cheap?Essay
  44. AI Has Passed Every Exam. It Has Never Had an Idea.Essay
  45. What Becomes Scarce After Intelligence?Essay
  46. India’s Carbon Markets : A New Test for Global Climate PolicyEssay
  47. Google Wants AI to Become BoringEssay
  48. The Wall That Wasn’t YoursEssay
  49. The Rate-Limiting StepEssay
  50. The Speed of Being WrongEssay
  51. Uber Burned a Year of AI Budget in Four Months. A Rat Catcher in 1902 Knew WhyEssay
  52. Finding a Flat in India Is Broken. We Have the Technology to Fix It. Nobody With Power Wants To.Essay
  53. Why We Can Never Have Good Social MediaEssay
  54. Gen Z Is Going OfflineEssay

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Essay · 15 Mar 2026

Gen Z Is Going Offline

What looks like a generational preference for offline socializing is a market signal about the failure of retention-based business models. Gen Z isn’t rejecting digital tools. They’re leaving platform

First published on Substack, 15 Mar 2026.

I’ve been tracking what looked like a collection of unrelated cultural shifts, run clubs surging, silent book clubs with wait lists, pottery studios as the new third space. It felt anecdotal until the retention data made it hard to ignore.

Dating apps hover at 3.3% retention after 30 days. Meanwhile, 77% of Gen Z finds partners through offline channels. This isn’t user failure. It’s product-market fit collapse for platforms that spent a decade optimizing for engagement.


The business model broke before anyone admitted it

Dating apps, social platforms, traditional nightlife, all optimized for the same metric: time spent. Keep users scrolling, swiping, coming back. Revenue scaled with retention, so retention became the goal rather than the means.

The problem: in social products, the desired outcome is usually an exit. You want to find your person and leave the app. You want to find your community and stop searching. At some point the platform’s financial interest and the user’s actual goal stopped pointing in the same direction.

Think of it as the casino problem. Casinos are designed to keep you inside. No clocks, no windows, bathroom routes that pass slot machines. Users tolerated it for a while because the alternative seemed harder. Then the calculation shifted. The ROI on digital social platforms collapsed, and Gen Z ran the numbers.


Physical presence is becoming a premium

What we’re seeing isn’t digital detox. It’s money moving toward spaces where trust hasn’t been used up by bad incentives.

When 62% of adults can’t tell human profiles from AI ones, and platforms are financially better off keeping you searching than helping you find someone, showing up in person becomes the only signal that can’t be faked.

The pricing already reflects this. Curated dinner parties charge $75+ per head. New Clubs ( Hiking Clubs, Run Clubs, etc) are up 300% on Strava. Members-only social clubs are oversubscribed. These aren’t amenity fees. They’re verification fees, payment for the assurance that everyone in the room is real, present, and filtered through the costly signal of physical attendance.

The market is pricing in what platforms destroyed: trust.


Three layers of the Verification Economy

This creates a HUGE opportunity across three layers. Each depends on the one before it.

Layer 1 — Coordination Infrastructure

Tools that help people gather offline without trying to keep them on the app. These businesses do well when users leave to attend something. Revenue comes from logistics, invitations, payments, reminders, waitlists, not from screen time.

Partiful raised $27M for this. Luma is processing millions in IRL event ticketing. Strava functions less as a fitness app now and more as the operating system for run clubs.

Layer 2 — Verification-as-a-Service

As digital identity gets harder to trust, businesses that verify human presence and make introductions will charge a premium. The value isn’t the space or the activity. It’s the guarantee that nobody in the room got in by gaming a bot filter.

Hinge’s founder left to build Overtone, a voice-first app that routes people offline immediately. Bumble paid $17.5M for Geneva to add a community verification layer. These moves share a thesis.

Layer 3 — Physical Social Infrastructure

Real estate that answers “where do I meet people” will get more valuable as third spaces disappear. Coffee shops converting to listening bars. Bookstores running author events and reading nights. Fitness studios that double as social sorting mechanisms.

Lululemon sees 4-5× higher revenue from customers who use stores as community hubs. The yoga class, the coffee, the book, they’re the entry fee for access to a vetted human network.


Why this won’t reverse

Three things make this durable.

1. The trust problem is permanent. AI has permanently lowered the cost of faking digital signals. Users know this now. Even if platforms found a technical fix, the knowledge wouldn’t go away. Physical presence is the only proof that costs something real to fake.

2. The incentive problem can’t be patched. Retention-based revenue models and successful social outcomes are in direct conflict. This isn’t a product flaw. It’s the business model. The company that genuinely tries to fix Tinder also destroys Tinder’s revenue.

3. Scale has a biological ceiling. Dunbar’s number, roughly 150 stable relationships, is a cognitive constraint, not a preference. Platforms that tried to scale past it were always going to hit a wall. The opportunity here is in working with that limit: more communities, not bigger ones.


Who’s winning, who’s losing

Winning

  • Partiful / Luma — coordination infrastructure that earns when users show up offline

  • Overtone / Wavelength — AI that routes people to rooms, not feeds

  • Activity-led retail — Lululemon, REI, bookstores repurposed as gathering spaces

  • Physical clubs with digital coordination — run clubs on Strava, interest groups on Discord

Losing

  • Incumbent dating apps — retention revenue requires users not to succeed

  • Traditional nightlife — the alcohol-margin model is eroding as Gen Z drinks less

  • Feed-based social — passive scrolling is losing ground to active coordination

Worth watching

  • Bumble — the Geneva acquisition points the right direction, but the core product still runs on retention

  • Discord for social — works as coordination infrastructure right now, but the pressure to monetize through engagement is real and growing


Investment checklist

Five questions that separate good bets from bad ones in this category:

✅ Does the business earn when users leave? Revenue tied to facilitated gatherings, not time on platform.

✅ Coordination, not discovery. Makes it easier to meet, not easier to browse more options.

✅ Verification through costly signals. Physical presence, time, money, things bots can’t manufacture cheaply.

✅ Grows sideways, not upward. More communities of 150 rather than one community of 15 million.

✅ Infrastructure, not content. Connects people. Doesn’t try to replace them.


The timeline

2025–2026 — The window. The data is there but most people read it as a vibe shift rather than a market transition. Coordination infrastructure is raising real venture rounds. This is the period before the category becomes a category.

2027–2028 — Inflection. Large platforms start announcing IRL pivots. Match Group starts reporting meaningful revenue from events and verification services. Leaders emerge.

2029–2030 — Settled. Physical social infrastructure carries real estate premiums. “Verified human network” is a recognized product type. Coordination tools become table stakes the way Calendly is for scheduling.


Bear case

Verification becomes access control. If physical presence and curated spaces become luxury goods, authentic community ends up gated by income and free time. The early internet promised to democratize belonging. This could do the opposite.

Coordination tools drift toward engagement. Network effects create monetization pressure. The same logic that corrupted the last wave of social platforms will come knocking again, sooner than founders expect.

The physical premium fades. If enough people move offline at once, the signal degrades. Once AI verification gets good enough to solve the trust problem from the other direction, the calculus changes.

None of these kill the thesis. But they’re why infrastructure bets beat platform bets here, the incentives sit better, and why it’s worth paying attention early when a portfolio company starts reporting engagement metrics instead of gathering metrics.


What this actually is

This isn’t only a market opportunity. It’s a correction.

We tried to scale human connection the way we scaled content distribution. Took out the friction, optimized for volume, assumed more was better. The platforms that won did it by engineering reasons to keep people searching. Gen Z grew up inside that system and was the first generation to see it clearly enough to leave.

What’s being built now, coordination tools, verification layers, physical spaces as filters, isn’t a return to the pre-internet world. It uses digital infrastructure while accepting that the actual value sits in being in a room with real people who chose to be there.

The companies that win won’t be the ones keeping people on screens the longest. They’ll be the ones getting people off screens fast and into rooms with the right people.

That’s better business. It’s also just better.


This thesis changes as the market moves. If you’re building in this space, or have data that pushes back on any of this, I want to hear it.

Comments open for humans who showed up.