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Deeptech Decoded: Frontier Builders | AI | Product Taste
Nihal Kurth
3 episodes
2 days ago
Kitchen-table conversations with builders at the deep-tech AI frontier. No script. No filters. The reasoning behind the moves: what's working, what's not and why conviction beats consensus. For those building what doesn’t have a manual. Deep-dives → https://deeptechdecoded.substack.com/
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Technology
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All content for Deeptech Decoded: Frontier Builders | AI | Product Taste is the property of Nihal Kurth and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
Kitchen-table conversations with builders at the deep-tech AI frontier. No script. No filters. The reasoning behind the moves: what's working, what's not and why conviction beats consensus. For those building what doesn’t have a manual. Deep-dives → https://deeptechdecoded.substack.com/
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Technology
Episodes (3/3)
Deeptech Decoded: Frontier Builders | AI | Product Taste
The AI Software Preventing Million-Dollar Failures | Shapol M., Entangl (YC S24)

65% of data-center outages are caused by human error, sometimes as simple as flipping the wrong switch.Shapol M., founder & CEO of Entangl (YC S24), went from building reusable rockets in the UK to preventing catastrophic failures in the infrastructure powering AGI.In this Deeptech Decoded conversation, we dive into:- Why data-center downtime causes mass chaos (remember when even Eight Sleep went dark?)- How Entangl helps engineers avoid million-dollar mistakes on-site- The pivot from aerospace to critical AI infrastructure- Why today’s AI build-out is bigger than the Manhattan Project- What it takes to earn customer trust so deep they bring you to their next companyIf AI is the future, this is the system that keeps that future online.

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2 weeks ago
1 hour 28 minutes 53 seconds

Deeptech Decoded: Frontier Builders | AI | Product Taste
AI × Quantum: YC’s Bet to Turn 27 Years of Work into 2 Minutes | Brandon Severin, Conductor Quantum

Quantum computing is about to leave the lab and land on your laptop.In this episode of Deeptech Decoded, Nihal Kurth sits down with Brandon Severin, CEO & Co-Founder of Conductor Quantum (Y Combinator) — the startup using AI to automate quantum chip design 1,000× faster, cutting setup time from 27 years to just 2 minutes.Together, they unpack how AI and automation are scaling qubits like semiconductors and why the next leap in quantum won’t come from colder labs but smarter code.Later, Cameron Farrar-Frank joins to lead a live AMA with the audience, diving deeper into the most thought-provoking questions from founders and researchers.They break down: • Why quantum computing’s PR problem is holding the field back • The shift from cold labs to software-defined systems • How AI is scaling quantum architectures 1,000× faster • Y Combinator’s influence on speed, focus, and iteration • Why Brandon calls this his life’s work — and what’s next for quantum hardware“If quantum is going to scale, it can’t depend on PhDs tuning each qubit. It has to be software-defined.”Big Idea:The startup bringing quantum computing to your desk — turning deep-tech research into real-world infrastructure.Read next:Story of Brandon Severin and Joel Pendleton → https://deeptechdecoded.substack.com/p/yc-funds-quantum-computing-you-canSubscribe for noise-canceling insights from the deep-tech frontier:https://deeptechdecoded.substack.comFollow Deeptech DecodedLinkedIn → linkedin.com/company/deeptechdecodedYouTube → youtube.com/@deeptechdecodedaiInstagram → instagram.com/deeptechdecodedTikTok → tiktok.com/@deeptechdecodedSpotify → https://podcast.sptfy.com/QbkB

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1 month ago
1 hour 27 minutes 35 seconds

Deeptech Decoded: Frontier Builders | AI | Product Taste
Data Centers in Space: The Future of AI Compute | Philip Johnston, Starcloud

Join us for an in-depth AMA and podcast conversation with Philip Johnston, Co-founder & CEO of Starcloud, the world’s first orbital data center company. 🚀

We cover:

1. Why orbital infrastructure is the next leap in AI compute

2. Starcloud’s plan to launch megawatt-scale compute into orbit by 2027

3. The challenges of energy, cooling, and scalability in space

4. Lessons from building at the frontier of deep tech, aerospace, and AIThis wide-ranging discussion blends technical insight, founder perspective, and long-term vision. Perfect for anyone curious about the future of AI infrastructure, space technology, and frontier startups.

📌 Topics include: orbital data centers, AI compute demand, space infrastructure, scaling deep-tech startups.


00:00 Intro

01:13 What Starcloud is building

02:19 5 GW vision; module approach

03:03 Architecture: central spine + modules

03:27 Solar arrays and radiators

03:58 Demo sat (H100s), Nov target

04:21 Roadmap to 40 MW modules

05:29 Modularity and self‑sufficiency

06:06 Cooling and racks

08:05 Top risks and objections

11:20 Mission life & end‑of‑life

13:16 Disposal options

14:09 Maintenance strategy

17:40 Backhaul plan

18:22 Iteration and capex

19:32 Launch cadence; Sat‑2 service

20:31 Costs/runway overview

22:12 Early customers (DoD/USG)

23:37 Differentiation (H100s)

25:01 EO data bottleneck

25:44 Space‑to‑space optical

26:08 On‑orbit inference example

26:49 Latency: hours → seconds

27:09 Contrarian view (waste heat)

29:01 Q&A

31:48 Debris strategy

35:16 LEO capacity; Lagrange points

38:23 Scale refs; mass & launches

41:20 Launch economics context

42:23 Misconceptions (cooling, latency)

47:19 CAPEX per module

50:57 Closing advice

51:39 Wrap

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2 months ago
51 minutes 41 seconds

Deeptech Decoded: Frontier Builders | AI | Product Taste
Kitchen-table conversations with builders at the deep-tech AI frontier. No script. No filters. The reasoning behind the moves: what's working, what's not and why conviction beats consensus. For those building what doesn’t have a manual. Deep-dives → https://deeptechdecoded.substack.com/