Even if Y Combinator never existed, Matthew Sutton would still be backing the top builders. Most investors wait for traction. Matthew Sutton backs builders before there’s proof, sometimes before there’s even a category. He is the first check writer you want to have on your side.Operating at the intersection of Harvard Ventures and the YC ecosystems, Matthew has backed AI, quantum, and defense tech founders at the moment where conviction matters more than metrics.In this episode of Deeptech Decoded, we talk about what it really takes to back deep tech when spreadsheets are useless, categories don’t exist yet, and most ideas look wrong at the beginning. Most importantly, his investment thesis and founder-centric approach.He breaks down:◽️ How he evaluates AI, quantum, and defense founders without being technical yourself◽️ The CURSOR LESSON: why he passed on a $100M+ company and what founder evolution teaches.◽️ The 3 CRITERIA for backing pre-revenue deeptech:◽️ Why the best deep tech companies often look irrational early on◽️ The difference between hype, narrative, and real conviction◽️ AI bubble reality check: using communities and open source to validate vs. hype◽️ What Harvard and YC teach — and don’t teach — about failure Timestamps:00:00 – Introduction: Judgment before proof04:13 – From Wall Street to backing deeptech builders07:08 – First entrepreneurial ventures at age 12-1309:27 – Cambridge vs Silicon Valley: The per capita talent thesis11:37 – Why California wins at commercialization15:16 – Where real startups are built: Dorm rooms and iteration24:24 – Evaluating AI and quantum founders without being technical28:03 – SimpleBet: AI sports betting meets regulatory change31:37 – The Cursor miss: Passing on a $100M+ AI company and the lesson38:27 – Filtering deal flow: Spotting technical founders with conviction41:14 – Quantum investing before traction: The Segaldry story45:02 – Finding founders outside the Bay Area echo chamber51:21 – Defense tech's golden age: Golden Dome to rapid innovation55:59 – Moving fast in defense: Small bets in sensitive sectors58:25 – Leadership styles: Future creators vs past learners1:02:35 – AI bubble navigation: Discord communities as validation1:05:34 – Avoiding echo chambers: Stress testing investment thesis1:11:16 – Operational discipline without killing innovation1:16:10 – Founder suffering: Why resilience matters in deeptech1:25:18 – Teaching failure at Harvard: The straight-A paradox1:29:04 – What doesn't scare him about AI's future1:32:30 – Desert island question: Three startup essentials1:34:16 – Closing thoughtsFollow Matthew:...Follow us on:About Deeptech DecodedDeeptech Decoded is a podcast and newsletter for builders and backers working at the frontier of technology—from AI and quantum to defense, space, and infrastructure. We focus on product judgment, conviction, and what it really takes to build what doesn't exist yet.

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.

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

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