Sep 17, 2019 · 30m · mad

Fireside Chat: Thomas Reardon, Founder & CEO, CTRL-Labs (FirstMark's Data Driven NYC)

Thomas Reardon · 22m spoken Matt Turck · 3m spoken
0:00 / 0:00
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In this Data Driven NYC fireside chat, Matt Turck interviews CTRL-Labs CEO Thomas Reardon about groundbreaking non-invasive neural interface technology that translates arm motor neuron activity into direct machine control. Reardon demonstrates working prototypes, explains the computational neuroscience and machine learning models behind the platform, and highlights future applications in computing, robotics, and medical care.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 13.7% of the talking time here. How this is scored →

Matt as informed peer 2.5 Guest teaching 3.5 Guest disagreement 2.1 Matt pushing back 0.5
05100:0010:0020:0030:000:04–2:09 · Matt as informed peer 2/10 Data Driven NYC Event Title Card Matt introduces Thomas Reardon and CTRL-Labs, framing their work as an API for the brain. Reardon gently corrects the framing, emphasizing 'neural interface technology' over 'brain-machine interface' because the nervous system extends down the spinal cord. Matt accepts the clarification and moves to video demos.2:09–6:15 · Matt as informed peer 3/10 Video Demo: VR Force-Like Object Control Reardon displays significant combativeness when dismissing a redundant demo video as a poorer version and calling the iPhone a 'Trump level disaster for humanity'. Matt engages constructively by framing the tech as capturing intent and citing Reardon's previous comments on mobile devices.6:15–8:43 · Matt as informed peer 1/10 Technical Mechanism: Surface Electromyography and Motor Neurons Reardon delivers a technical explanation of surface electromyography, motor neuron deconvolution, and action potentials. Matt steps back completely to let the guest explain the neuroscience and signal processing mechanics without interruption.8:43–11:55 · Matt as informed peer 4/10 The Human Output Mechanism and Natural Motor Control Matt demonstrates high understanding by offering a crisp layman summary that the brain's sole output is controlling muscles. Reardon excitedly agrees with Matt's summary and builds on it using the complexity of drinking a beer to contrast natural motor control with phone interaction.11:55–14:16 · Matt as informed peer 2/10 Target Applications: VR/AR, Pervasive Computing, and Robotics Matt prompts Reardon to discuss application areas. Reardon details their go-to-market focus on VR/AR immersive environments, pervasive computing like smartwatch interaction, and unexpected inbound demand from robotics.14:16–17:53 · Matt as informed peer 3/10 Developer Program Vetting and Platform Strategy Matt asks about platform strategy, developer selection criteria, and why machine learning is strictly necessary. Reardon candidly calls 'bullshit' on himself for lack of proof videos, then educates the room on why decoding the nervous system is the mother of all ML problems.17:53–21:58 · Matt as informed peer 5/10 Thomas Reardon's Career: Internet Explorer to Neuroscience When Matt asks about Reardon's past leading Internet Explorer, Reardon attempts to deflect ('Not really'). Matt pushes back directly by narrating Reardon's background himself, listing his work on IE, W3C standards, Columbia PhD, and large family background.21:58–24:30 · Matt as informed peer 0/10 Q&A: Prosthetics and Clinical Applications An audience member asks why prosthetics and amputee care were not mentioned. Reardon explains that while they do advanced work with Johns Hopkins, pursuing a broad consumer market yields data scale much faster than an FDA clinical route, ultimately helping clinical patients faster.24:30–28:17 · Matt as informed peer 1/10 Q&A: Business Model and Personal Neural Models Reardon answers audience questions regarding revenue models and personal neural calibration. When asked about nanotech applications for nerve damage, Reardon bluntly rejects the premise before explaining motor neuron diseases like ALS.28:17–30:20 · Matt as informed peer 4/10 Q&A: Learning Curve, Neural Signatures, and Conclusion Matt asks a final composite question about learning curves, potential advantages for children, and societal consequences. Reardon explains how stochastic neural signatures make each user's model unique and details typical training times.0:04–2:09 · Guest teaching 4/10 Data Driven NYC Event Title Card Matt introduces Thomas Reardon and CTRL-Labs, framing their work as an API for the brain. Reardon gently corrects the framing, emphasizing 'neural interface technology' over 'brain-machine interface' because the nervous system extends down the spinal cord. Matt accepts the clarification and moves to video demos.2:09–6:15 · Guest teaching 3/10 Video Demo: VR Force-Like Object Control Reardon displays significant combativeness when dismissing a redundant demo video as a poorer version and calling the iPhone a 'Trump level disaster for humanity'. Matt engages constructively by framing the tech as capturing intent and citing Reardon's previous comments on mobile devices.6:15–8:43 · Guest teaching 6/10 Technical Mechanism: Surface Electromyography and Motor Neurons Reardon delivers a technical explanation of surface electromyography, motor neuron deconvolution, and action potentials. Matt steps back completely to let the guest explain the neuroscience and signal processing mechanics without interruption.8:43–11:55 · Guest teaching 2/10 The Human Output Mechanism and Natural Motor Control Matt demonstrates high understanding by offering a crisp layman summary that the brain's sole output is controlling muscles. Reardon excitedly agrees with Matt's summary and builds on it using the complexity of drinking a beer to contrast natural motor control with phone interaction.11:55–14:16 · Guest teaching 2/10 Target Applications: VR/AR, Pervasive Computing, and Robotics Matt prompts Reardon to discuss application areas. Reardon details their go-to-market focus on VR/AR immersive environments, pervasive computing like smartwatch interaction, and unexpected inbound demand from robotics.14:16–17:53 · Guest teaching 4/10 Developer Program Vetting and Platform Strategy Matt asks about platform strategy, developer selection criteria, and why machine learning is strictly necessary. Reardon candidly calls 'bullshit' on himself for lack of proof videos, then educates the room on why decoding the nervous system is the mother of all ML problems.17:53–21:58 · Guest teaching 2/10 Thomas Reardon's Career: Internet Explorer to Neuroscience When Matt asks about Reardon's past leading Internet Explorer, Reardon attempts to deflect ('Not really'). Matt pushes back directly by narrating Reardon's background himself, listing his work on IE, W3C standards, Columbia PhD, and large family background.21:58–24:30 · Guest teaching 5/10 Q&A: Prosthetics and Clinical Applications An audience member asks why prosthetics and amputee care were not mentioned. Reardon explains that while they do advanced work with Johns Hopkins, pursuing a broad consumer market yields data scale much faster than an FDA clinical route, ultimately helping clinical patients faster.24:30–28:17 · Guest teaching 4/10 Q&A: Business Model and Personal Neural Models Reardon answers audience questions regarding revenue models and personal neural calibration. When asked about nanotech applications for nerve damage, Reardon bluntly rejects the premise before explaining motor neuron diseases like ALS.28:17–30:20 · Guest teaching 3/10 Q&A: Learning Curve, Neural Signatures, and Conclusion Matt asks a final composite question about learning curves, potential advantages for children, and societal consequences. Reardon explains how stochastic neural signatures make each user's model unique and details typical training times.0:04–2:09 · Guest disagreement 2/10 Data Driven NYC Event Title Card Matt introduces Thomas Reardon and CTRL-Labs, framing their work as an API for the brain. Reardon gently corrects the framing, emphasizing 'neural interface technology' over 'brain-machine interface' because the nervous system extends down the spinal cord. Matt accepts the clarification and moves to video demos.2:09–6:15 · Guest disagreement 5/10 Video Demo: VR Force-Like Object Control Reardon displays significant combativeness when dismissing a redundant demo video as a poorer version and calling the iPhone a 'Trump level disaster for humanity'. Matt engages constructively by framing the tech as capturing intent and citing Reardon's previous comments on mobile devices.6:15–8:43 · Guest disagreement 1/10 Technical Mechanism: Surface Electromyography and Motor Neurons Reardon delivers a technical explanation of surface electromyography, motor neuron deconvolution, and action potentials. Matt steps back completely to let the guest explain the neuroscience and signal processing mechanics without interruption.8:43–11:55 · Guest disagreement 1/10 The Human Output Mechanism and Natural Motor Control Matt demonstrates high understanding by offering a crisp layman summary that the brain's sole output is controlling muscles. Reardon excitedly agrees with Matt's summary and builds on it using the complexity of drinking a beer to contrast natural motor control with phone interaction.11:55–14:16 · Guest disagreement 1/10 Target Applications: VR/AR, Pervasive Computing, and Robotics Matt prompts Reardon to discuss application areas. Reardon details their go-to-market focus on VR/AR immersive environments, pervasive computing like smartwatch interaction, and unexpected inbound demand from robotics.14:16–17:53 · Guest disagreement 2/10 Developer Program Vetting and Platform Strategy Matt asks about platform strategy, developer selection criteria, and why machine learning is strictly necessary. Reardon candidly calls 'bullshit' on himself for lack of proof videos, then educates the room on why decoding the nervous system is the mother of all ML problems.17:53–21:58 · Guest disagreement 3/10 Thomas Reardon's Career: Internet Explorer to Neuroscience When Matt asks about Reardon's past leading Internet Explorer, Reardon attempts to deflect ('Not really'). Matt pushes back directly by narrating Reardon's background himself, listing his work on IE, W3C standards, Columbia PhD, and large family background.21:58–24:30 · Guest disagreement 2/10 Q&A: Prosthetics and Clinical Applications An audience member asks why prosthetics and amputee care were not mentioned. Reardon explains that while they do advanced work with Johns Hopkins, pursuing a broad consumer market yields data scale much faster than an FDA clinical route, ultimately helping clinical patients faster.24:30–28:17 · Guest disagreement 3/10 Q&A: Business Model and Personal Neural Models Reardon answers audience questions regarding revenue models and personal neural calibration. When asked about nanotech applications for nerve damage, Reardon bluntly rejects the premise before explaining motor neuron diseases like ALS.28:17–30:20 · Guest disagreement 1/10 Q&A: Learning Curve, Neural Signatures, and Conclusion Matt asks a final composite question about learning curves, potential advantages for children, and societal consequences. Reardon explains how stochastic neural signatures make each user's model unique and details typical training times.0:04–2:09 · Matt pushing back 0/10 Data Driven NYC Event Title Card Matt introduces Thomas Reardon and CTRL-Labs, framing their work as an API for the brain. Reardon gently corrects the framing, emphasizing 'neural interface technology' over 'brain-machine interface' because the nervous system extends down the spinal cord. Matt accepts the clarification and moves to video demos.2:09–6:15 · Matt pushing back 1/10 Video Demo: VR Force-Like Object Control Reardon displays significant combativeness when dismissing a redundant demo video as a poorer version and calling the iPhone a 'Trump level disaster for humanity'. Matt engages constructively by framing the tech as capturing intent and citing Reardon's previous comments on mobile devices.6:15–8:43 · Matt pushing back 0/10 Technical Mechanism: Surface Electromyography and Motor Neurons Reardon delivers a technical explanation of surface electromyography, motor neuron deconvolution, and action potentials. Matt steps back completely to let the guest explain the neuroscience and signal processing mechanics without interruption.8:43–11:55 · Matt pushing back 0/10 The Human Output Mechanism and Natural Motor Control Matt demonstrates high understanding by offering a crisp layman summary that the brain's sole output is controlling muscles. Reardon excitedly agrees with Matt's summary and builds on it using the complexity of drinking a beer to contrast natural motor control with phone interaction.11:55–14:16 · Matt pushing back 0/10 Target Applications: VR/AR, Pervasive Computing, and Robotics Matt prompts Reardon to discuss application areas. Reardon details their go-to-market focus on VR/AR immersive environments, pervasive computing like smartwatch interaction, and unexpected inbound demand from robotics.14:16–17:53 · Matt pushing back 1/10 Developer Program Vetting and Platform Strategy Matt asks about platform strategy, developer selection criteria, and why machine learning is strictly necessary. Reardon candidly calls 'bullshit' on himself for lack of proof videos, then educates the room on why decoding the nervous system is the mother of all ML problems.17:53–21:58 · Matt pushing back 3/10 Thomas Reardon's Career: Internet Explorer to Neuroscience When Matt asks about Reardon's past leading Internet Explorer, Reardon attempts to deflect ('Not really'). Matt pushes back directly by narrating Reardon's background himself, listing his work on IE, W3C standards, Columbia PhD, and large family background.21:58–24:30 · Matt pushing back 0/10 Q&A: Prosthetics and Clinical Applications An audience member asks why prosthetics and amputee care were not mentioned. Reardon explains that while they do advanced work with Johns Hopkins, pursuing a broad consumer market yields data scale much faster than an FDA clinical route, ultimately helping clinical patients faster.24:30–28:17 · Matt pushing back 0/10 Q&A: Business Model and Personal Neural Models Reardon answers audience questions regarding revenue models and personal neural calibration. When asked about nanotech applications for nerve damage, Reardon bluntly rejects the premise before explaining motor neuron diseases like ALS.28:17–30:20 · Matt pushing back 0/10 Q&A: Learning Curve, Neural Signatures, and Conclusion Matt asks a final composite question about learning curves, potential advantages for children, and societal consequences. Reardon explains how stochastic neural signatures make each user's model unique and details typical training times.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 32.8% · guest 67.2%0:00 · Matt 32.8% · guest 67.2%3:00 · Matt 16.4% · guest 83.6%3:00 · Matt 16.4% · guest 83.6%6:00 · Matt 10.5% · guest 89.5%6:00 · Matt 10.5% · guest 89.5%9:00 · Matt 4.8% · guest 95.2%9:00 · Matt 4.8% · guest 95.2%12:00 · Matt 10.4% · guest 89.6%12:00 · Matt 10.4% · guest 89.6%15:00 · Matt 23.1% · guest 76.9%15:00 · Matt 23.1% · guest 76.9%18:00 · Matt 25.5% · guest 74.5%18:00 · Matt 25.5% · guest 74.5%21:00 · Matt 3.4% · guest 96.6%21:00 · Matt 3.4% · guest 96.6%24:00 · Matt 0.5% · guest 99.5%24:00 · Matt 0.5% · guest 99.5%27:00 · Matt 10.7% · guest 89.3%27:00 · Matt 10.7% · guest 89.3%30:00 · Matt 21.7% · guest 78.3%30:00 · Matt 21.7% · guest 78.3%
Sharpest disagreement ▶ 5:00 iPhone is a Trump-level disaster for humanity

Reardon uses extreme hyperbole, calling the iPhone a disaster for humanity and an instrument of human enslavement that forces users into cognitive battles with autocorrect.

Hardest push from Matt ▶ 18:15 Matt overrides Reardon's deflection on his IE background

When Reardon declines to talk about leading Internet Explorer, Matt refuses the deflection, declaring 'I'll do it for you' and listing Reardon's achievements.

Biggest teaching moment ▶ 23:15 Reardon reframes consumer targeting vs clinical FDA focus

Reardon educates the audience member on startup strategy, demonstrating that avoiding slow FDA clinical paths and targeting consumer scale generates the ML data necessary to advance prosthetic technology.

Matt holds his own ▶ 8:42 Matt summarizes core neuroscience concept in layman terms

Matt demonstrates high technical mastery by synthesizing complex material into a concise summary that the brain's sole output mechanism is muscle control.

the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Data Driven NYC Event Title Card 2420 Matt introduces Thomas Reardon and CTRL-Labs, framing their work as an API for the brain. Reardon gently corrects the framing, emphasizing 'neural interface technology' over 'brain-machine interface' because the nervous system extends down the spinal cord. Matt accepts the clarification and moves to video demos.
Video Demo: VR Force-Like Object Control 3351 Reardon displays significant combativeness when dismissing a redundant demo video as a poorer version and calling the iPhone a 'Trump level disaster for humanity'. Matt engages constructively by framing the tech as capturing intent and citing Reardon's previous comments on mobile devices.
Technical Mechanism: Surface Electromyography and Motor Neurons 1610 Reardon delivers a technical explanation of surface electromyography, motor neuron deconvolution, and action potentials. Matt steps back completely to let the guest explain the neuroscience and signal processing mechanics without interruption.
The Human Output Mechanism and Natural Motor Control 4210 Matt demonstrates high understanding by offering a crisp layman summary that the brain's sole output is controlling muscles. Reardon excitedly agrees with Matt's summary and builds on it using the complexity of drinking a beer to contrast natural motor control with phone interaction.
Target Applications: VR/AR, Pervasive Computing, and Robotics 2210 Matt prompts Reardon to discuss application areas. Reardon details their go-to-market focus on VR/AR immersive environments, pervasive computing like smartwatch interaction, and unexpected inbound demand from robotics.
Developer Program Vetting and Platform Strategy 3421 Matt asks about platform strategy, developer selection criteria, and why machine learning is strictly necessary. Reardon candidly calls 'bullshit' on himself for lack of proof videos, then educates the room on why decoding the nervous system is the mother of all ML problems.
Thomas Reardon's Career: Internet Explorer to Neuroscience 5233 When Matt asks about Reardon's past leading Internet Explorer, Reardon attempts to deflect ('Not really'). Matt pushes back directly by narrating Reardon's background himself, listing his work on IE, W3C standards, Columbia PhD, and large family background.
Q&A: Prosthetics and Clinical Applications 0520 An audience member asks why prosthetics and amputee care were not mentioned. Reardon explains that while they do advanced work with Johns Hopkins, pursuing a broad consumer market yields data scale much faster than an FDA clinical route, ultimately helping clinical patients faster.
Q&A: Business Model and Personal Neural Models 1430 Reardon answers audience questions regarding revenue models and personal neural calibration. When asked about nanotech applications for nerve damage, Reardon bluntly rejects the premise before explaining motor neuron diseases like ALS.
Q&A: Learning Curve, Neural Signatures, and Conclusion 4310 Matt asks a final composite question about learning curves, potential advantages for children, and societal consequences. Reardon explains how stochastic neural signatures make each user's model unique and details typical training times.

Statements from this episode (22)

Disclosure
Reardon: CTRL-Labs is primarily a neuroscience and machine learning company
“We are only very thinly a hardware company. We are overwhelmingly a neurosciences and machine learning company.”
Thomas Reardon Sep 17, 2019 ▶ 0:59
Assertion Supported
CTRL-Labs band decodes motor neuron activity through the forearm
“Okay, so this is somebody wearing our band that's decoding the activity of the neurons that drive the hand, so that happens through the forearm.”
Thomas Reardon Sep 17, 2019 ▶ 2:09
Assertion Not checkable as stated
No living neuroscientist can explain what a thought is, says Thomas Reardon
“I'll tell you there's not a neuroscientist alive who can tell you what a thought is.”
Thomas Reardon Sep 17, 2019 ▶ 4:33
Opinion
Thomas Reardon calls the iPhone a Trump-level disaster for humanity
“I think the iPhone is a Trump level disaster for humanity.”
Thomas Reardon Sep 17, 2019 ▶ 5:00
Disclosure
Thomas Reardon wants to replace keyboards with thought-speed neural text input
“What I want to do is get rid of the 19th century keyboard entirely and allow you to effectively sort of text, if you will, at the level of your cognitive capacity.”
Thomas Reardon Sep 17, 2019 ▶ 6:01
Assertion Partly supported
CTRL-Labs reconstructs motor neuron activity before physical muscle movement occurs
“We figured out how to actually reconstruct the activity of the motor neurons that drove that electrical activity in the muscles themselves. This is activity that actually happens long before The muscles actually move, you know, long in the computational sense …”
Thomas Reardon Sep 17, 2019 ▶ 6:31
Assertion Not checkable as stated
Reardon: The human brain's only physical output is controlling muscles
“Ah, the only thing your brain does is turn muscles on and off. There's nothing else that it does.”
Thomas Reardon Sep 17, 2019 ▶ 9:13
Disclosure
CTRL-Labs began shipping its neural developer kits in September 2019
“We started shipping our developer kits last Friday after, boy, over three years of work.”
Thomas Reardon Sep 17, 2019 ▶ 11:39
Disclosure
Inbound robotics demand drove unexpected investments at CTRL-Labs
“We didn't start the company thinking about robotics at all, and those opportunities have all kind of inbound to us and actually led to some of our investments.”
Thomas Reardon Sep 17, 2019 ▶ 13:46
Assertion Not checkable as stated
CTRL-Labs received up to 100,000 developer applications for its platform
“More than 10,000, less than a 100,000.”
Thomas Reardon Sep 17, 2019 ▶ 15:20
Disclosure
Reardon: CTRL-Labs conducts video interviews with every developer applicant
“We interview, we do video conferences and interview every single applicant.”
Thomas Reardon Sep 17, 2019 ▶ 15:31
Opinion
Decoding the human nervous system is harder than predicting the weather
“I mean, decoding the human nervous system is the mother of all machine learning problems. I think it's significantly more complicated than predicting the weather, say.”
Thomas Reardon Sep 17, 2019 ▶ 16:19
Assertion Supported
Reardon: CTRL-Labs captures single neuron signals without penetrating the skull
“This is a, Pretty significant breakthrough that we're able to get to single neurons without basically penetrating into your skull.”
Thomas Reardon Sep 17, 2019 ▶ 17:30
Assertion Supported
Thomas Reardon created Internet Explorer in 1994 and led it through IE4
“I started up Internet Explorer in 94 and then worked on IE all the way through IE four, when we finally sort of won the browser war.”
Thomas Reardon Sep 17, 2019 ▶ 18:39
Assertion Partly supported
Thomas Reardon co-founded W3C with Tim Berners-Lee and helped develop CSS
“I helped found the W-three-C with Tim Berners-Lee and kind of set up those initial web standards I, you know, I'm very proud of my work on CSS, if people know what that is.”
Thomas Reardon Sep 17, 2019 ▶ 19:00
Opinion
Neuralink's clinical-first strategy leads to grindingly slow progress, says Thomas Reardon
“Whereas if you go just to the clinical population first, which is sort of where Neuralink is going with their work, it's just grindingly slow progress.”
Thomas Reardon Sep 17, 2019 ▶ 23:55
Disclosure
CTRL-Labs plans subscriptions for continuous neural modeling with free developer hardware
“Yeah, so our revenue model is based on us charging effectively a subscription for modeling work, but in fact, what we are pushing right now is one in which it's a free license to the hardware and a free license to the initial models, and we want to get paid Fo…”
Thomas Reardon Sep 17, 2019 ▶ 24:42
Assertion Contradicted
A trained CTRL-Labs band will not work at all on another person
“This is a very personal model that is custom to just you. When you put on the band after you've trained it, if you put it on somebody else, it doesn't work at all. It is, and it's not like design. That's just the way neural interfaces are required to work. The…”
Thomas Reardon Sep 17, 2019 ▶ 25:13
Assertion Not checkable as stated
CTRL-Labs needs only a dozen neurons to decode hand movement intent
“You need, like I said, 30,000 neurons to drive these hand movements, but it's mostly because you need those neurons to sort of gain up the signal. But we only need, like, a dozen to be able to actually understand what the actual movement intent was, rather tha…”
Thomas Reardon Sep 17, 2019 ▶ 27:15
Assertion Not checkable as stated
CTRL-Labs uniquely identifies users among 7.4 billion in 800 milliseconds
“I can put a band on you and after 10 minutes I will know enough from those signals that I can pull you out of a population of 7.4 billion people after 10 minutes of training data and do that in about 800 milliseconds.”
Thomas Reardon Sep 17, 2019 ▶ 28:51
Assertion Partly supported
Reardon: Genetic clones have entirely different motor neuron maps
“Your clone has an entirely different motor neuron map than you have.”
Thomas Reardon Sep 17, 2019 ▶ 29:25
Assertion Not checkable as stated
Training the CTRL-Labs neural device for full text input takes one hour
“Today it takes you know, an order an hour or so to do text training.”
Thomas Reardon Sep 17, 2019 ▶ 29:57
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