『Matt Robillard from SmartAC』のカバーアート

Matt Robillard from SmartAC

Matt Robillard from SmartAC

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今ならプレミアムプランが3カ月 月額99円

2026年5月12日まで。4か月目以降は月額1,500円で自動更新します。

概要

Matt Robillard from SmartAC joins Alex on AI After Dark to explain why he walked into a company with product-market fit but antiquated tech stack and immediately started rewriting everything with AI at its core. He breaks down the difference between horizontal process automation that Service Titan can eventually replicate versus vertical intelligence modeling the actual thermodynamics of HVAC systems that nobody else can touch, why clean energy is magical thinking constrained by the Shockley-Queisser limit and Betz limit, and how hiring the best AI talent in HVAC meant recruiting from SpaceX, Databricks and Perplexity by selling real-world impact over another CRM with AI slapped on. The conversation reveals why OEMs surprisingly embrace their solution instead of fearing disruption, his controversial take that LLMs are stochastic parrots not true intelligence, and how the marginal cost of code going to zero is pure hype when production systems at scale still require senior engineers who deeply understand customer pain points.

Click here to watch a video of this episode.

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Click here to view the episode transcript.

00:00 CES takeaways and hardware semiconductor relationships
01:53 Joining SmartAC post product-market fit with legacy tech
07:15 Data platform challenges and ML model development
12:14 Vertical AI defensibility versus horizontal automation
18:26 Alignment with Josh and commercial-product pairing
24:17 Replatforming effort and organizational design overhaul
29:27 Recruiting top AI talent from SpaceX and Databricks
36:03 Development velocity changes and junior engineer growth
41:01 AGI possibility and stochastic parrots debate
48:21 Controversial clean energy physics constraints opinion
56:42 Smart home promises and Tesla-style behavioral nudges
01;01;10 VC organizational design mistakes over two years
01;05;13 Who else should be on AI After Dark

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