T-Minus Space Daily – "Teaching Computers to Survive Space"

Podcast: T-Minus Space Daily by N2K Networks
Episode Date: December 20, 2025
Host: Maria Varmazis
Guest: Ralph Grundler, AI Tech

Episode Overview

This episode dives into the cutting-edge challenge of deploying artificial intelligence (AI) systems in space. Host Maria Varmazis interviews Ralph Grundler, a veteran in building space-rated computers at AI Tech, to uncover what it takes for AI and high-performance computing hardware to survive—and perform—amidst the intense radiation, thermal, and environmental hazards of space. The conversation explores technical innovations, lessons from flight heritage, the practical realities and risks of space-borne AI, and the transformative potential these technologies hold for Earth and beyond.

Key Discussion Points & Insights

1. The Roots and Evolution of AI Tech

• AI Tech’s Origin: Founded 40 years ago, during another surge of AI interest, AI Tech’s initial focus included both military and commercial applications.
  (04:15) Ralph Grundler: “To think that when they started a company 40 years ago and called it AI tech, they must have been geniuses.”
• Space Heritage: For 30+ years, AI Tech has been flying robust computers in space and accrued an impressive bill of “2 trillion miles without any failure,” including systems running on NASA spacecraft and the OSIRIS-REx mission to Bennu.

2. Radiation: Space’s Invisible Threat

• Radiation Analogy: Grundler compares radiation damage in silicon chips to bullet traces through water — invisible but potentially disastrous.
  • (08:17) Ralph Grundler: “You can think of radiation as being random bullets shooting through silicon. So it's kind of a war zone out there... you're being bombarded by radiation from the sun and... black holes...”
• Testing and Design: Surviving space means rigorous selection and screening of commercial parts, intensive testing, and building robust hardware and error correction around vulnerable components.

3. Approaches to Space-Rated AI Systems

• Low Earth Orbit (LEO) vs. Deep Space:
  • LEO benefits from Earth’s magnetospheric shielding—letting systems use more commercial parts (like Nvidia GPUs).
  • Deep space exposure demands even greater resilience; power and radiation protection are paramount.
  • (10:09) Grundler explains that shielding allows more flexibility in LEO, while deep space needs stricter design.
• Edge Computing in Orbit:
  • Satellites now perform substantial onboard data processing—filtering, compressing, and even analyzing imagery—before sending only valuable info back to Earth, reducing transmission bottlenecks.
  • (12:08) “The real advantage is edge computing... so for example, I would have a satellite go by this data center in space, transmit my data to it, it would do the processing for me, and then transmit the result back to Earth.”
• Key Technologies: GPUs (notably Nvidia’s), FPGAs for reconfigurability, and specialized error-correcting memory (ECC) protect against faults.

4. Lessons from Earth: Radiation at High Altitudes

• Server Farm Failures: Historical failures in Denver and Mexico City highlighted that cosmic radiation also impacts terrestrial computing—leading to industry-wide adoption of error-correction even on Earth.
  • (15:29) “We started putting... server farms in Denver and Mexico City... they had much higher error rates... radiation, actually, that was the cause.”

5. Space-Borne AI Applications

• Image Processing: A prime use-case is identifying features in satellite images (ships, whales, cloud cover) onboard and only downlinking relevant data.
• Autonomous Deep Space Missions: Mars rovers like Perseverance and Curiosity use on-board AI for navigation, sample analysis, and more, as latency to Earth makes autonomy essential.
  • (18:32) "The easy application is image processing... The other easy answer is the deep space applications... you want to do a lot of things autonomously."
• Space Collision Avoidance: AI can help predict and avoid collisions (and minimize fuel use), a critical need as orbital congestion grows.
  • (28:40) “...AI can help not only predict when you're going to have a collision, but also predict a movement such that you won't have a collision for a long time in the future... fuel is a premium..."

6. Engineering Challenges and “Battle-Testing”

• Testing in Extreme Environments: Before reaching orbit, hardware is run in harsh conditions on Earth—like inside tanks in the desert—for ruggedization.
  • (22:16) “It’s in a tank right now driving around in the desert... providing image processing for tank operators, which is a very helpful thing as well…”
• Launch and Materials Science: Surviving launch vibrations, vacuum, venting, thermal extremes, and avoiding contamination by off-gassing materials is essential.
  • (25:18) "Satellites are mounted in... a Christmas tree structure inside the rocket. And so this Christmas tree is like shaking back and forth... you have to make sure that you have that shock and vibration testing all done...”

7. Frontiers: Hardware and Power Innovation

• Latest Tech: The Nvidia Orin AGX processor (248 TOPS, 12 ARM cores) is heading for space in upcoming AI Tech missions.
• Power and Cooling: Power efficiency and thermal management are still limiting factors. New hardware accelerators promise lower power for intensive in-orbit workloads.

8. Cost, Risk, and The “Elon Factor”

• Unlike SpaceX, most organizations can’t absorb hardware failures easily; repeated, rigorous testing and reliability are non-negotiable for every launch.
  • (26:50) “We can’t all be like Elon Musk... he can really just shoot anything into space because he’s the richest person in the world... any kind of failure in space is negatively looked on by the company.”

Notable Quotes and Memorable Moments

• On Radiation:
  (08:17) Ralph Grundler:
  “You can think of radiation as being random bullets shooting through silicon. So it's kind of a war zone out there... you're being bombarded by radiation from the sun and... black holes...”

• On AI's Practical Applications:
  (18:32) Ralph Grundler:
  “The easy application is image processing, right? ... one of the very first ones in space was cloud detection. Because if you take a picture and it's covered with cloud ... you don't want to transmit that back down to Earth... ”

• On Experience and Reliability:
  (06:54) Ralph Grundler:
  “We’ve been flying something like 2 trillion miles without any failure. Which is kind of a cool thing to say.”

• On Edge Computing in Orbit:
  (12:08) Ralph Grundler:
  “The real advantage is edge computing... so for example, I would have a satellite go by this data center in space, transmit my data to it, it would do the processing for me, and then transmit the result back to Earth.”

• On Doing It Right:
  (14:01) Ralph Grundler:
  “What you're really trying to do is keep your commercial devices alive. So you have to build a hardware infrastructure around these systems in order to keep them alive.”

• On Testing and Space Launch:
  (25:18) Ralph Grundler:
  “...the whole thing is like shaking. And that's really what's happening... being able to make sure that you have that shock and vibration testing all done is really important.”

Timeline & Timestamps for Key Segments

| Timestamp | Segment & Topic | |-----------|--------------------------------------------------------------------------------------------------| | 01:28 | Opening Theme: Why AI in space is hard. | | 02:40 | Ralph Grundler introduces himself and AI Tech’s origin story. | | 06:54 | Reliability stats: 2 trillion miles, 200 flying objects, and mission heritage. | | 08:17 | Radiation as random “bullets”—the war zone metaphor. | | 10:09 | Differences in LEO vs. deep space environments for computing. | | 12:08 | Edge computing vs. “server farms”—what orbital data centers really mean. | | 15:29 | Lessons from high-altitude server farm failures (Earth-side radiation). | | 18:32 | Practical AI in orbit: From image processing to Mars rovers’ autonomy. | | 21:25 | The new Nvidia Orin processor—processing power and flight plans. | | 22:16 | Testing AI systems in harsh Earth conditions (tanks in the desert). | | 25:18 | Launch challenges, shock/vibration, venting, off-gassing, and PCB reliability. | | 26:50 | Comparing mainstream (risk-averse) and SpaceX (iterate-and-launch) philosophies. | | 28:15 | The critical future role of energy-efficient, autonomous AI for deep space & traffic management. | | 30:56 | Host wraps up—enthusiasm for the future of space-based AI. |

Closing Thoughts

Ralph Grundler’s insights give a vivid sense of both the astounding technical progress and daunting hazards involved in moving AI off-planet. Space is unforgiving—to AI, to hardware, to engineering shortcuts. Careful preparation, robust error correction, and continual innovation are the guiding principles. The potential for autonomous space missions, near-real-time orbital data processing, and new forms of in-space infrastructure is vast—and, as AI Tech’s long flight record makes clear, very much within reach.

Listener Takeaway:
If you wonder how space hardware can possibly keep up with the breakneck pace of AI, this episode will leave you with newfound appreciation for the unsung, resilient machinery—and the clever humans—that make those orbits possible.

End of Summary