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AI Leadership When Nothing Is Certain

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Manage episode 489524318 series 2833920
Content provided by Elevano. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Elevano 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://ppacc.player.fm/legal.

In this episode, Amir speaks with Anna Patterson, founder of Ceramic AI, about what it truly means to lead an AI-first company. They unpack the differences between engineering and AI leadership, the chaos and creativity of early-stage research, how Ceramic AI is betting on emerging talent, and why managing AI roadmaps is an exercise in uncertainty and invention. Anna also shares perspectives from her experience at Google and how search engine wars inform today’s AI landscape.

💡 Key Takeaways:

AI Leadership = Research Leadership

Managing AI projects is less like traditional engineering and more like guiding research — with unknowns, pivots, and breakthroughs.

Invention and Market Fit Are Separate Risks

Startups must solve both: the technical challenge and the business case. Success in one doesn't guarantee the other.

Competing with Giants Means Betting on Talent

Ceramic AI doesn’t try to match OpenAI or Anthropic on salaries. Instead, they hire promising but overlooked researchers and invest in their growth.

Motivation is Self-Driven

People with deep academic or research backgrounds bring strong self-motivation — a must-have trait in early-stage, high-risk AI environments.

Vertical AI and Pointed Models Are the Future

Rather than aiming to compete broadly, building specialized models for specific workflows could be the path for emerging players.

⏱️ Timestamped Highlights:

00:38 – Ceramic AI’s efficient training stack for long-context models

01:29 – Why AI leadership mirrors research more than engineering

03:37 – Managing a roadmap when invention and success are uncertain

05:34 – Staying competitive when Big Tech might absorb your feature

07:10 – Prepping new hires for startup chaos

08:49 – How Ceramic AI hires promising talent that others overlook

10:37 – Breakdown of the AI infrastructure pipeline: from pretraining to inference

12:57 – Lessons from search engine wars and how they might reflect AI’sn evolutio

14:42 – The messy near-future of models: distillation, specialization, and competition

16:08 – Keeping morale and creativity high with flexibility, fun, and sleep

18:22 – Balancing coding and leadership as a technical founder

19:31 – How Anna envisions her evolving role at Ceramic AI

🛠️ Mentioned Resources:

Contact Anna: [email protected]

🎯 Career Tips (discussed):

Bet on Early Talent: If you're early in your career and not yet established, smaller companies might be more willing to take a chance on your potential than large labs.

Be Startup-Ready: Know what you’re getting into. Embrace ambiguity, multiple directions, and creative chaos — especially in AI startups.

Stay Curious and Motivated: A research mindset — driven by deep curiosity and self-direction — is essential in a domain where there’s no guaranteed outcome.

💬 Quote:

“One thing about researchers… there's a deep self-motivation. Nobody is dying for you to graduate. You have to want it — deeply.” – Anna Patterson

  continue reading

478 episodes

Artwork
iconShare
 
Manage episode 489524318 series 2833920
Content provided by Elevano. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Elevano 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://ppacc.player.fm/legal.

In this episode, Amir speaks with Anna Patterson, founder of Ceramic AI, about what it truly means to lead an AI-first company. They unpack the differences between engineering and AI leadership, the chaos and creativity of early-stage research, how Ceramic AI is betting on emerging talent, and why managing AI roadmaps is an exercise in uncertainty and invention. Anna also shares perspectives from her experience at Google and how search engine wars inform today’s AI landscape.

💡 Key Takeaways:

AI Leadership = Research Leadership

Managing AI projects is less like traditional engineering and more like guiding research — with unknowns, pivots, and breakthroughs.

Invention and Market Fit Are Separate Risks

Startups must solve both: the technical challenge and the business case. Success in one doesn't guarantee the other.

Competing with Giants Means Betting on Talent

Ceramic AI doesn’t try to match OpenAI or Anthropic on salaries. Instead, they hire promising but overlooked researchers and invest in their growth.

Motivation is Self-Driven

People with deep academic or research backgrounds bring strong self-motivation — a must-have trait in early-stage, high-risk AI environments.

Vertical AI and Pointed Models Are the Future

Rather than aiming to compete broadly, building specialized models for specific workflows could be the path for emerging players.

⏱️ Timestamped Highlights:

00:38 – Ceramic AI’s efficient training stack for long-context models

01:29 – Why AI leadership mirrors research more than engineering

03:37 – Managing a roadmap when invention and success are uncertain

05:34 – Staying competitive when Big Tech might absorb your feature

07:10 – Prepping new hires for startup chaos

08:49 – How Ceramic AI hires promising talent that others overlook

10:37 – Breakdown of the AI infrastructure pipeline: from pretraining to inference

12:57 – Lessons from search engine wars and how they might reflect AI’sn evolutio

14:42 – The messy near-future of models: distillation, specialization, and competition

16:08 – Keeping morale and creativity high with flexibility, fun, and sleep

18:22 – Balancing coding and leadership as a technical founder

19:31 – How Anna envisions her evolving role at Ceramic AI

🛠️ Mentioned Resources:

Contact Anna: [email protected]

🎯 Career Tips (discussed):

Bet on Early Talent: If you're early in your career and not yet established, smaller companies might be more willing to take a chance on your potential than large labs.

Be Startup-Ready: Know what you’re getting into. Embrace ambiguity, multiple directions, and creative chaos — especially in AI startups.

Stay Curious and Motivated: A research mindset — driven by deep curiosity and self-direction — is essential in a domain where there’s no guaranteed outcome.

💬 Quote:

“One thing about researchers… there's a deep self-motivation. Nobody is dying for you to graduate. You have to want it — deeply.” – Anna Patterson

  continue reading

478 episodes

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