Runwago wants to make running pay—without ponzinomics. Founder Martin explains how their SportsFi app lets everyday runners monetize realistic goals using a transparent “challenge pool” model: everyone joins a challenge, finishers get 100% of their stake back plus rewards funded by those who don’t finish. No shoe NFTs, no inflationary reward token.

Timestamps

• [00:00] Opening & why SportsFi still matters

• [00:03] Martin’s crypto OG journey (since 2012)

• [00:05] The problem: 300M daily runners, almost zero monetization

• [00:08] The model: pooled challenges; finishers earn from non-finishers

• [00:11] Devices & anti-cheat: Garmin/Apple/WearOS integrations

• [00:16] App Store realities: compliance, custody, reviews

• [00:19] Tokenomics done differently (RUNWAGO ≠ reward token)

• [00:22] Fiat challenges, company revenue, future revenue-share to holders

• [00:25] GTM: Puma tie-ins, offline activations, club strategy

• [00:29] Community > celebrities; micro-KOLs and run clubs

• [00:36] What they’re hiring/raising/partnering for next

• [00:39] How to try the app + meet at Token2049
  

Connect

https://www.runwago.com/

https://x.com/runwago

https://www.linkedin.com/in/martin-lepka-12b51120b/

Disclaimer

Nothing mentioned in this podcast is investment advice and please do your own research. Finally, it would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend.
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Alex (ex-BMO investment banker turned founder) shares how Futr.network is flipping the ad/data model with AI agents that pay you for training them. We dig into why the Web2 barter economy (free services for your data) is collapsing, how zero-party data and a data supply chain can route value back to consumers and data contributors (think: mortgage brokers, lenders), and why agentic advertising will disrupt Google/Meta style targeting.

Timestamps

[00:00] “Time for a new data model” — agents that pay you for training data

[00:01] Alex’s path: BMO (M&A → FIG → Digital Assets), fund, stables, now AI agents

[00:03] What’s broken: Web2 barter system, cookies/pixels, hidden training sets

[00:05] Futr.network: self-sovereign AI agent + incentives for data contributors

[00:06] Mortgage example: underwriting pack → agent training → recurring royalties

[00:09] Why prior “get paid for your data” failed; why now works (AI agents = $$)

[00:15] KPIs: users, dollars on rails, brand demand for zero-party data

[00:16] Beachhead: auto loans (optimize payments, save interest, set-and-forget)

[00:18] Rails: bank links, credit/debit, embedded wallet, stablecoin payments

[00:19] Next verticals: mortgages, student loans; beyond finance → life data

[00:21] What most miss about AI: it ran Web2 behind the scenes for years

[00:24] High-fidelity AI: no hallucinations when money moves; enterprise-grade stacks

[00:28] AI × crypto: machine-speed decisions need machine-speed value settlement

[00:30] Data supply chain (like music royalties) via blockchain micropayments

[00:32] The disruption: agentic advertising eats digital ads in 3–5 years

[00:33] The ask: brands & enterprises; network effects; just raised $5.1M; more to come

[00:37] Beta access coming; train your agent, earn tokens

Connect

https://futrnetwork.com/

https://www.linkedin.com/company/thefutrcorporation/

https://www.linkedin.com/in/alex-mcdougall-290b0b39/

Disclaimer

Nothing mentioned in this podcast is investment advice and please do your own research. Finally, it would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend.
Be a guest on the podcast or contact us - https://www.web3pod.xyz/

Billy, founder of Intuition Systems (ex–VC/early crypto), digs into the biggest risk in AI: models trained on garbage, regurgitated data and algorithm-shaped behavior. We unpack how attestations, signed data, and web-of-trust reputation can give both humans and AI better intuition at decision time — from picking a chair to routing agentic AI across platforms.

Timestamps

[00:00] We’re being shaped by algorithms; slop-in → slop-out AI

[00:02] Billy’s path: distributed systems, fintech, game bots, early Bitcoin

[00:05] The problem: fragmented info (web + people’s heads) & costly context-switching

[00:06] What Intuition is: structured expression + rediscovery + rewards

[00:08] Attestations & incentives (economic + reputational) without spam

[00:12] Can bots game it? Bonding curves, loss for bad signal, web-of-trust filters

[00:18] Best content should win: TikTok-style merit + trust primitives

[00:19] Use case #1: AI agents—personalization, agent reputation, platform reputation

[00:22] Why these reputations must be decentralized (avoid platform capture)

[00:23] GTM: unopinionated dev platform + opinionated apps to show PMF

[00:24] Biggest challenges: focus, hitting PMF, AI acceleration outpacing products

[00:27] Mission: give AI “intuition” via signed, attributed, reputational data

[00:28] Reducing AI’s recursive slop problem; verifiable attribution with keys

[00:30] Humans act differently per platform; algorithms distort expression

[00:33] Call to action: build at the AI × Web3 trust layer; join the community

[00:35] Layer Zero Ventures origins (people = Layer 0); why Intuition exists

[00:37] Events: Korea Blockchain Week & Token 2049; Intuition side events

Connect

https://www.intuition.systems/

https://www.linkedin.com/company/0xintuition/

https://x.com/0xintuition

https://www.linkedin.com/in/william-luedtke-b0a3bb5a/

https://x.com/0xbilly
Disclaimer

Nothing mentioned in this podcast is investment advice and please do your own research. Finally, it would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend.
Be a guest on the podcast or contact us - https://www.web3pod.xyz/