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248: From Wall Street to Web3 Agents: Yang Tang on the Future of Autonomous AI

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Manage episode 477725431 series 3012250
Content provided by Sam Kamani. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sam Kamani or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

Yang Tang, co-founder of Memetica, joins Sam to dive deep into the world of AI agents — what they are, how they’re trained, and how they’re already generating value across Web2 and Web3. From his background in institutional finance and machine learning to launching BSD and Liam, Yang walks us through building intelligent, monetizable agents and why the future of AI is vertical-specific and application-first.

Key Timestamps

  • [00:00:00] Introduction: Sam welcomes Yang Tang and introduces the topic of AI agents.

  • [00:01:00] Yang’s Background: From Wall Street to machine learning to Web3.

  • [00:03:00] Evolution of Trading: How everything became algorithmic post-2008.

  • [00:05:00] Why AI Agents Now: LLMs aren't applications — agents are.

  • [00:06:00] Core Features: Memetica’s pillars — memory, RL, and utility.

  • [00:08:00] Competing with Giants: Why focus beats AGI and big capital.

  • [00:10:00] Data Strategy: Why private data is useless without context.

  • [00:12:00] Use Cases: Real-world agent examples like Liam and BSD.

  • [00:14:00] Reinforcement Learning: How Liam evolved to boost impressions.

  • [00:16:00] Tokens and Agents: The rise of BSD and market cap milestones.

  • [00:18:00] Pricing and Ownership: Who owns the agent’s IP and revenue?

  • [00:20:00] SME and Enterprise Use: From sports betting to social media ops.

  • [00:23:00] Institutional AI Demand: Why application matters more than research.

  • [00:25:00] Distribution Challenges: Why even strong products struggle to scale.

  • [00:28:00] Time vs. Decision Value: Where AI agents can win right now.

  • [00:30:00] Agent vs. Human: Running A/B tests with agents on social.

  • [00:34:00] AI Misuse: The Trump chart story and hallucination risks.

  • [00:36:00] Launching Tokens: What it takes to create tokenized agents.

  • [00:38:00] Utility vs. Distraction: The token paradox for founders.

  • [00:41:00] Building for SMEs: Future plans to support long-tail businesses.

  • [00:44:00] Hiring and Scaling: What Memetica needs to grow.

  • [00:46:00] Accuracy & Safeguards: How Memetica agents reach 95%+ accuracy.

  • [00:47:00] Final Ask: Yang is raising, hiring, and looking to onboard more creators and partners.

Connect

https://memetica.ai/

https://x.com/memeticaAI

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

https://x.com/yangtang

https://www.linkedin.com/in/yangtang/
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/

  continue reading

254 episodes

Artwork
iconShare
 
Manage episode 477725431 series 3012250
Content provided by Sam Kamani. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sam Kamani or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

Yang Tang, co-founder of Memetica, joins Sam to dive deep into the world of AI agents — what they are, how they’re trained, and how they’re already generating value across Web2 and Web3. From his background in institutional finance and machine learning to launching BSD and Liam, Yang walks us through building intelligent, monetizable agents and why the future of AI is vertical-specific and application-first.

Key Timestamps

  • [00:00:00] Introduction: Sam welcomes Yang Tang and introduces the topic of AI agents.

  • [00:01:00] Yang’s Background: From Wall Street to machine learning to Web3.

  • [00:03:00] Evolution of Trading: How everything became algorithmic post-2008.

  • [00:05:00] Why AI Agents Now: LLMs aren't applications — agents are.

  • [00:06:00] Core Features: Memetica’s pillars — memory, RL, and utility.

  • [00:08:00] Competing with Giants: Why focus beats AGI and big capital.

  • [00:10:00] Data Strategy: Why private data is useless without context.

  • [00:12:00] Use Cases: Real-world agent examples like Liam and BSD.

  • [00:14:00] Reinforcement Learning: How Liam evolved to boost impressions.

  • [00:16:00] Tokens and Agents: The rise of BSD and market cap milestones.

  • [00:18:00] Pricing and Ownership: Who owns the agent’s IP and revenue?

  • [00:20:00] SME and Enterprise Use: From sports betting to social media ops.

  • [00:23:00] Institutional AI Demand: Why application matters more than research.

  • [00:25:00] Distribution Challenges: Why even strong products struggle to scale.

  • [00:28:00] Time vs. Decision Value: Where AI agents can win right now.

  • [00:30:00] Agent vs. Human: Running A/B tests with agents on social.

  • [00:34:00] AI Misuse: The Trump chart story and hallucination risks.

  • [00:36:00] Launching Tokens: What it takes to create tokenized agents.

  • [00:38:00] Utility vs. Distraction: The token paradox for founders.

  • [00:41:00] Building for SMEs: Future plans to support long-tail businesses.

  • [00:44:00] Hiring and Scaling: What Memetica needs to grow.

  • [00:46:00] Accuracy & Safeguards: How Memetica agents reach 95%+ accuracy.

  • [00:47:00] Final Ask: Yang is raising, hiring, and looking to onboard more creators and partners.

Connect

https://memetica.ai/

https://x.com/memeticaAI

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

https://x.com/yangtang

https://www.linkedin.com/in/yangtang/
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/

  continue reading

254 episodes

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