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NVIDIA AI Podcast

Alembic and the Future of AI in Marketing - Ep. 263

39 min episode · 2 min read
·
Thomas Puig

Episode

39 min

Read time

2 min

Topics

Startups, Fundraising & VC, Design & UX

AI-Generated Summary

Key Takeaways

  • Spiking Neural Networks for Signal Processing: Alembic runs spiking neural networks on NVIDIA GPUs using custom wetware simulators to compare different marketing modalities like Nielsen ratings versus store visits, enabling outlier detection with zero time history for short campaigns like Olympics sponsorships.
  • Private Data as Competitive Advantage: Corporate profit follows information flow, with all future alpha coming from private datasets rather than public ones. AI models trained on similar public data converge to 90% similarity, making proprietary customer data the key differentiator like BP versus Shell gasoline.
  • Causal Chain Reaction Modeling: Alembic connects marketing touchpoints across time using causal inference mathematics to track sequences like watching Olympics coverage, searching Google flights, clicking ads, then purchasing tickets, calculating optimal lag times between each conversion step to prove campaign effectiveness mathematically.
  • LLMs for User Experience Only: Use large language models like Mad Libs templates where AI writes connecting language but causal deep learning models fill in actual data points, eliminating hallucination risks while maintaining readable intelligence briefings instead of traditional dashboards that require human interpretation.

What It Covers

Thomas Puig, founder of Alembic, explains how his company uses spiking neural networks and causal AI mathematics to transform marketing intelligence, processing billions of data rows to connect customer touchpoints and prove campaign ROI.

Key Questions Answered

  • Spiking Neural Networks for Signal Processing: Alembic runs spiking neural networks on NVIDIA GPUs using custom wetware simulators to compare different marketing modalities like Nielsen ratings versus store visits, enabling outlier detection with zero time history for short campaigns like Olympics sponsorships.
  • Private Data as Competitive Advantage: Corporate profit follows information flow, with all future alpha coming from private datasets rather than public ones. AI models trained on similar public data converge to 90% similarity, making proprietary customer data the key differentiator like BP versus Shell gasoline.
  • Causal Chain Reaction Modeling: Alembic connects marketing touchpoints across time using causal inference mathematics to track sequences like watching Olympics coverage, searching Google flights, clicking ads, then purchasing tickets, calculating optimal lag times between each conversion step to prove campaign effectiveness mathematically.
  • LLMs for User Experience Only: Use large language models like Mad Libs templates where AI writes connecting language but causal deep learning models fill in actual data points, eliminating hallucination risks while maintaining readable intelligence briefings instead of traditional dashboards that require human interpretation.

Notable Moment

Puig reveals Delta's Olympic medal presentation ceremonies with the Eiffel Tower backdrop drove more ticket sales to Paris than traditional thirty-second ad spots, demonstrating how emotional brand moments outperform direct advertising when mathematically measured through causal modeling.

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Episode Transcript

Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. A quick note before we welcome today's guest. The AI podcast has a new home on the web at ai-podcast.nvidia.com. You can find all of our episodes there as well as links to listen to the show on your choice of podcast platforms. If you like what you hear, please take a moment to follow, subscribe, or even leave us a review. And if we're missing your favorite platform on that page or you just wanna tell us something, drop a line at aipodcast@nvidia.com. Thanks for listening, and let's get right to it. My guest today is working at the leading edge of marketing intelligence. He's got a fascinating backstory, his company does. And, today, they're using data backed strategy and AI to help brands transform their marketing. Thomas Puig, founder and CEO of Alembic, is here to discuss it all. And, I've got just enough of a background in marketing myself that hopefully I can, you know, carry my end of the conversation. We'll see. Thomas, welcome, and thank you so much for joining the NVIDIA AI podcast. Thank you for having me. Pleasure to be here. So I kind of hinted at it, but it's always better coming from the guest than me. We try to do these intros. But, interesting story behind Alembic, and I'm sure I only know the the tip of the iceberg there. So can you tell us, what Alembic is and the story behind founding the company? Yeah. So we've been around a little while. We're really an applied science company, and so it took us many years to build technology. It began with, three people originally and, yeah, still with us today. Myself and my background started at Ames Research Center originally, like, when I was a kid, basically. Winding the quantitative economics and then decided, actually, I preferred music, the arts, and marketing. I want that route for quite a while till I ended up backwards. Right. The other, founder was a guy named John Adams. John Adams, very storied infrastructure engineer in the valley. He was the thirteenth employee of Twitter Wow. And took the company from the time it was a Mac mini with a bad Ethernet cable under his desk all the way through the IPO. Right. I think for many years, he was the longest serving person not on the board of directors. Wow. Okay. And then Seth Little, who, is a world renowned, creative director and designer who has rebuilt, you know, brands for Lego and even done work for Apple, stuff like that over the period of time. The three of us got together, and there were a number of reasons why we really chose this field. But the most important is that we have felt that marketing and anything around the creative and even the arts in trying to promote it had been and no one had been able to be …

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