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#118 - Decentralizing AI with Idle CPUs: Heisenberg Network & Data Agents | Mehdi Kargar

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Manage episode 474487642 series 3377311
Content provided by Samir Azizi. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Samir Azizi 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://podcastplayer.com/legal.

In this episode of the Azizi Podcast, Samir Azizi sits down with Dr. Mehdi Kargar, co-founder of Heisenberg Network, to dive deep into the most overlooked problem in AI today: AI-ready data.

You’ll learn:

Why 80% of AI projects are failing—and what Heisenberg is doing about it

How Data Agents transform raw, messy data into structured, contextual intelligence

Why CPUs, not GPUs, are the real heroes in AI data processing

How you can earn crypto just by running a lightweight node on your laptop

The future of decentralized compute and how it empowers a more open, censorship-resistant AI ecosystem

How Heisenberg cuts AI data costs by up to 70%

Dr. Kargar explains the origins of Heisenberg, how they went from Web3 predictive analytics to building the missing layer of AI, and why the network's infrastructure is powered by a decentralized network of idle CPUs from everyday users—just like you.

📡 This conversation is a must-watch if you’re into:

Decentralized AI

Building AI agents

Data infrastructure

Crypto & compute rewards

The next evolution of AI beyond LLMs

Join Heisenberg Network Node Waitlist:
https://www.heisenberg.so/heisenberg-node

Connect with Mehdi Kargar:
LinkedIn: https://www.linkedin.com/in/mehdi-kargar/
X: https://x.com/mehdi_kargar

Follow Heisenberg Network:
LinkedIn: https://www.linkedin.com/company/heisenbergnet
X: https://x.com/HeisenbergNet

Connect with Samir Azizi:
LinkedIn: https://www.linkedin.com/in/samirazizi/
X: https://twitter.com/AdoubleZI

🎧 Subscribe to the Azizi Podcast on Spotify:
https://open.spotify.com/show/3FDd0dQi8vvTnQz5h5QwAz

  continue reading

122 episodes

Artwork
iconShare
 
Manage episode 474487642 series 3377311
Content provided by Samir Azizi. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Samir Azizi 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://podcastplayer.com/legal.

In this episode of the Azizi Podcast, Samir Azizi sits down with Dr. Mehdi Kargar, co-founder of Heisenberg Network, to dive deep into the most overlooked problem in AI today: AI-ready data.

You’ll learn:

Why 80% of AI projects are failing—and what Heisenberg is doing about it

How Data Agents transform raw, messy data into structured, contextual intelligence

Why CPUs, not GPUs, are the real heroes in AI data processing

How you can earn crypto just by running a lightweight node on your laptop

The future of decentralized compute and how it empowers a more open, censorship-resistant AI ecosystem

How Heisenberg cuts AI data costs by up to 70%

Dr. Kargar explains the origins of Heisenberg, how they went from Web3 predictive analytics to building the missing layer of AI, and why the network's infrastructure is powered by a decentralized network of idle CPUs from everyday users—just like you.

📡 This conversation is a must-watch if you’re into:

Decentralized AI

Building AI agents

Data infrastructure

Crypto & compute rewards

The next evolution of AI beyond LLMs

Join Heisenberg Network Node Waitlist:
https://www.heisenberg.so/heisenberg-node

Connect with Mehdi Kargar:
LinkedIn: https://www.linkedin.com/in/mehdi-kargar/
X: https://x.com/mehdi_kargar

Follow Heisenberg Network:
LinkedIn: https://www.linkedin.com/company/heisenbergnet
X: https://x.com/HeisenbergNet

Connect with Samir Azizi:
LinkedIn: https://www.linkedin.com/in/samirazizi/
X: https://twitter.com/AdoubleZI

🎧 Subscribe to the Azizi Podcast on Spotify:
https://open.spotify.com/show/3FDd0dQi8vvTnQz5h5QwAz

  continue reading

122 episodes

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