What are the key takeaways from this Software Engineering Daily episode?

Key insights include: **Hardware data loss at ingestion:** One Nominal customer discovered only 10% of test data reached long-term storage due to misconfigured schema versioning when operators forgot to update config values after software downgrades. Automating version sync between aircraft software and ground control eliminated this silent data loss entirely — a fix requiring no operator action.; **Hot/cold pipeline architecture for hardware:** Supporting both real-time control room monitoring and deep post-test analysis requires maintaining synchronized hot and cold data paths. Nominal's architecture lets engineers write logic once and apply it across both use cases, abstracting away the fact that sub-second latency streaming and OLAP-style batch analysis are fundamentally different infrastructure problems.; **Edge agents with local buffering over dropping data:** In network-constrained test environments, Nominal deploys edge agents that prioritize safety-critical data for immediate transmission while buffering everything else locally for later upload. Since high-value tests like rocket fires produce dense data in short bursts — seconds of firing, hours of downtime — amortized upload catches up without losing a single data point.

What did Jason Hock and Kevin Ball discuss on Software Engineering Daily?

Jason Hock, CEO of Nominal, explains why hardware engineering lacks the observability and tooling software teams take for granted. Nominal builds a data platform managing high-frequency sensor data from physical assets, covering real-time control room monitoring, post-test analysis, and simulation correlation for aerospace, defense, and energy hardware programs. Key topics include: **Hardware data loss at ingestion:** One Nominal customer discovered only 10% of test data reached long-term storage due to misconfigured schema versioning when operators forgot to update config values after software downgrades. Automating version sync between aircraft software and ground control eliminated this silent data loss entirely — a fix requiring no operator action.; **Hot/cold pipeline architecture for hardware:** Supporting both real-time control room monitoring and deep post-test analysis requires maintaining synchronized hot and cold data paths. Nominal's architecture lets engineers write logic once and apply it across both use cases, abstracting away the fact that sub-second latency streaming and OLAP-style batch analysis are fundamentally different infrastructure problems..

How long is this episode of Software Engineering Daily?

This episode is 50 minutes long. SignalCast provides an AI-generated summary so you can get the key insights in about 3 minutes.

Software Engineering Daily

The Hardware Bottleneck AI Can’t Fix

June 2, 2026

50 min episode · 2 min read

Jason Hock,Kevin Ball

Episode

50 min

Read time

2 min

Topics

Leadership, Design & UX, Artificial Intelligence

AI-Generated Summary

Published Jun 2, 2026

Key Takeaways

✓Hardware data loss at ingestion: One Nominal customer discovered only 10% of test data reached long-term storage due to misconfigured schema versioning when operators forgot to update config values after software downgrades. Automating version sync between aircraft software and ground control eliminated this silent data loss entirely — a fix requiring no operator action.
✓Hot/cold pipeline architecture for hardware: Supporting both real-time control room monitoring and deep post-test analysis requires maintaining synchronized hot and cold data paths. Nominal's architecture lets engineers write logic once and apply it across both use cases, abstracting away the fact that sub-second latency streaming and OLAP-style batch analysis are fundamentally different infrastructure problems.
✓Edge agents with local buffering over dropping data: In network-constrained test environments, Nominal deploys edge agents that prioritize safety-critical data for immediate transmission while buffering everything else locally for later upload. Since high-value tests like rocket fires produce dense data in short bursts — seconds of firing, hours of downtime — amortized upload catches up without losing a single data point.
✓Asset hierarchy and tagging as foundational infrastructure: Correlating a test result against its corresponding simulation takes two clicks when data is properly tagged and cataloged, versus writing custom SQL queries when it is not. Hardware organizations should define asset hierarchies — aircraft, engines, subsystems — and tag data at ingestion, because component swaps, like moving an engine between aircraft, make lineage tracking operationally critical.
✓AI agents cannot yet close the hardware feedback loop: Software agents iterate because code execution provides near-instant feedback. Hardware lacks this — a single rocket fire test represents years of work and tens of millions of dollars. Closing this gap requires accumulating labeled datasets where domain engineers tag regions of interest, anomalies, and expected behavior, building the training foundation that could eventually support hardware design agents.

What It Covers

Jason Hock, CEO of Nominal, explains why hardware engineering lacks the observability and tooling software teams take for granted. Nominal builds a data platform managing high-frequency sensor data from physical assets, covering real-time control room monitoring, post-test analysis, and simulation correlation for aerospace, defense, and energy hardware programs.

Key Questions Answered

•Hardware data loss at ingestion: One Nominal customer discovered only 10% of test data reached long-term storage due to misconfigured schema versioning when operators forgot to update config values after software downgrades. Automating version sync between aircraft software and ground control eliminated this silent data loss entirely — a fix requiring no operator action.
•Hot/cold pipeline architecture for hardware: Supporting both real-time control room monitoring and deep post-test analysis requires maintaining synchronized hot and cold data paths. Nominal's architecture lets engineers write logic once and apply it across both use cases, abstracting away the fact that sub-second latency streaming and OLAP-style batch analysis are fundamentally different infrastructure problems.
•Edge agents with local buffering over dropping data: In network-constrained test environments, Nominal deploys edge agents that prioritize safety-critical data for immediate transmission while buffering everything else locally for later upload. Since high-value tests like rocket fires produce dense data in short bursts — seconds of firing, hours of downtime — amortized upload catches up without losing a single data point.
•Asset hierarchy and tagging as foundational infrastructure: Correlating a test result against its corresponding simulation takes two clicks when data is properly tagged and cataloged, versus writing custom SQL queries when it is not. Hardware organizations should define asset hierarchies — aircraft, engines, subsystems — and tag data at ingestion, because component swaps, like moving an engine between aircraft, make lineage tracking operationally critical.
•AI agents cannot yet close the hardware feedback loop: Software agents iterate because code execution provides near-instant feedback. Hardware lacks this — a single rocket fire test represents years of work and tens of millions of dollars. Closing this gap requires accumulating labeled datasets where domain engineers tag regions of interest, anomalies, and expected behavior, building the training foundation that could eventually support hardware design agents.

Notable Moment

Hock describes a customer building a large-scale machine where Nominal reduced problem resolution from two full lost days to thirty minutes by catching issues earlier in the data stream — framing this as an order-of-magnitude improvement that still falls far short of the microsecond iteration loops now possible in AI-assisted software development.

Know someone who'd find this useful?

Episode Transcript

Software engineering has developed powerful tools for observability, data management, and continuous testing, but hardware engineering has largely not kept pace. The feedback loops, tooling, and infrastructure that software engineers take for granted simply do not exist in most hardware programs. Nominal is a data platform built to help hardware organizations move at the same speed as software teams. It manages the hardware data supply chain end to end, from ingesting high frequency sensor data off physical assets to enabling real time control room monitoring, post test analysis, and simulation correlation. Jason Hock is the cofounder and CEO of Nominal, and he has a background spanning distributed data systems at Palantir and cloud infrastructure at Vercel. In this episode, Jason joins Kevin Ball to discuss why hardware engineering has lagged so far behind software in tooling and observability. The unique data challenges of working with high frequency time series sensor data, how nominal handles both real time control room workflows and post test analysis, why AI agents are transforming software development but have not yet made the same leap in hardware, and what it would take to close that gap. Kevin Ball or Kate Ball is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He cofounded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI in action discussion group through Latent Space. Check out the show notes to follow Kay Ball on Twitter or LinkedIn, or visit his website, kball.llc. Jason, welcome to the show. Thanks for having me, Kevin. Yeah. I'm excited. It's gonna be fun to to talk with you because the subject area of hardware and hardware testing is, like, a slant for me. It's, like, real close to what I do, but it's not what I do. So I'm excited to dig in. But let's start with you. Can you give me a little bit of your background and, like, how you got into what you're doing today with Nominal? Yeah. I'll give you kind of a longer story, which is that I started my life as someone who was more of a mathematician than an engineer. I think software was a way for me to, like, enter engineering. And I got like, I fell in love with distributed data systems. I built a lot of software at Palantir early in my career. Also fell in love with working for, like, really technical users, people who had expertise in something, but not necessarily software and data systems. And nominal for me is, like, the culmination of some things that I'm really passionate about. So it's growing software teams and products from scratch, but also with, like, big ambitions, meeting those really technical users. In our case, it's, like, hardware engineers, people who have obsessed over electrical systems, aerospace systems, and then, like, frankly, just solving, like, really hard but fun, like, software problems. So full stack up and down. Like, …

Get the full transcript (11,094 words) + summary by email — free

One-time email with the complete transcript and AI summary of this episode. No account needed.

One email, no spam. We’ll also show you what SignalCast does.

Browse all Software Engineering Daily transcripts →