The Future of Decision Science: Martin Lucas on Human-AI Alignment and Deterministic Intelligence | Ep 1136

Summary

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Episode Overview

Theme:
The Future of Decision Science: Martin Lucas on Human-AI Alignment and Deterministic Intelligence explores how Martin Lucas, inventor of deterministic AI and pioneer in decision science, envisions a future where machines and humans make better decisions together. The conversation demystifies "deterministic" vs. probabilistic AI, highlights the importance of human context in decision-making, and delves into the practical and ethical implications for organizations as AI becomes more reliable and human-aligned.

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Key Discussion Points & Insights

1. What is Deterministic AI? (01:14–03:25)

• Origin & Motivation:

  • Martin Lucas started exploring why "humans don't understand humans" and expanded to study how decision-making works in the brain and machines.
  • He developed deterministic AI to move beyond the limitations of stochastic, probabilistic models.

• The “30% Problem” in AI:

  • Probabilistic models are “inconsistent,” often giving different results for the same input, which Lucas calls a barrier to trust and reliability.
  • Example: AI can inconsistently reject or accept identical financial applicants, or give different results for the effectiveness of the same drug.

• Deterministic AI Solution:

  • It “takes away the probability,” offering continuity and consistency in results.
  • Rather than replacing existing AI, deterministic AI “overlays” to improve accuracy and reliability.

• Notable Quote:

  “Deterministic AI solves the biggest problem that sits inside AI. Today it's known… as the 30% problem. So every prompt that a human sends… has this 30% issue…. What we've done is created AI that allows you to be accurate, so it takes away the probability, so you get continuity and consistency of results.”
  — Martin Lucas @ 01:53

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2. Reconciling Rigorous AI with “Messy” Human Decision Making (04:11–08:59)

• The Human Angle:

  • Lucas insists intelligence—human or artificial—must be "understandable, reproducible and deeply human at its core."

• Thought Experiment—Brand Loyalty Test:

  • Lucas walks the host (and listeners) through a live example: reflect on a beloved brand and analyze emotions, trust, engagement, timing, and loyalty triggers.
  • Insights:
    • Decision science identifies the architecture of our decision-making, emotional context, and language.
    • Statistic: “The average human brain makes 36,000 decisions a day and only 2% of them are conscious.” (05:59)
    • Brand loyalty is driven by emotional resonance more than product or price, a point missed by many organizations.

• Notable Quote:

  “If it doesn't speak the way that resonates with you, then you reject it…. What we're doing is understanding each audience and what experiences they want and what experiences they don't.”
  — Martin Lucas @ 05:59 & 08:27

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3. Human-AI Augmentation vs. Replacement (08:59–10:30)

• Machine Alignment:

  • Lucas advocates for machines that augment—not replace—human abilities, especially for high-stakes decisions (healthcare, critical infrastructure).

• Role of Deterministic AI in Governance:

  • In regulated domains, stochastic AI is “too Wild West.” Deterministic AI can “reduce error rate to less than 1%,” making it suitable for governance and compliance.

• Future of Human Creativity:

  • Despite AI’s rise, “you’re never going to be able to replace that human creative component.”

• Notable Quote:

  “The next 10 years is going to be very much about alignment…. The rise of human creativity, of ideation, is going to manifest in slightly different ways, but you're never going to be able to replace that human creative component.”
  — Martin Lucas @ 09:28

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4. The Future: Symbolic Mathematics & Human-Machine Collaboration (11:19–12:54)

• Exclusive Announcement & Vision:

  • Lucas reveals a new patent combining deterministic AI, decision science, and decision physics—rooted in “symbolic mathematics.”
  • Shifts from instructing machines “what to do” toward teaching machines “how” to reason and understand context.

• Worlds to Enable:

  • Lucas envisions systems that focus on “quality and how you serve people better and understand humans.”
  • Predicts the mythology of “sci-fi” human-machine partnerships will become reality—a future of deeper collaboration.

• Notable Quote:

  “What we've built… is symbolic mathematics. And what that does is teach a machine how. And once a machine understands how, it means it can operate with reasoning and understanding…. You can combine that mythology of the sci-fi of humans and machines working together.”
  — Martin Lucas @ 11:19

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Memorable Moments & Quotes

| Timestamp | Speaker | Quote | |-----------|--------------|--------------------------------------------------------------------------| | 01:53 | Martin Lucas | “Deterministic AI solves the biggest problem… the 30% problem.” | | 05:59 | Martin Lucas | “The average human brain makes 36,000 decisions a day…” | | 08:27 | Martin Lucas | “We've performed 120 million different tests… 76% above the industry.” | | 09:28 | Martin Lucas | “The next 10 years is going to be very much about alignment.” | | 11:19 | Martin Lucas | “What we've built… is symbolic mathematics. And what that does is teach a machine how.” |

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Timeline of Important Segments

• 00:00–01:14 — Introduction & Guest Background
• 01:14–03:25 — Defining Deterministic AI and the 30% Problem
• 04:11–08:59 — Human Factors: Emotional & Behavioral Aspects in Decision Science
• 08:59–10:30 — Augmentation, Alignment & Human Agency in High-Stakes Domains
• 11:19–12:54 — Patent Announcement, Symbolic Mathematics & Future Vision

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Tone & Language

• Martin Lucas speaks with clarity and enthusiasm, blending technical explanation ("decision physics," "deterministic intelligence") with relatable human examples (brand loyalty test).
• The atmosphere is approachable, curious, and collaborative.
• Focus on demystifying AI while emphasizing the irreplaceable role of human creativity.

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Summary

In this conversation, Martin Lucas champions a new paradigm for AI—one deeply rooted in deterministic logic and decision science, focused on reliability, reproducibility, and emotional resonance with people. By bridging the worlds of technology, psychology, and philosophy, Lucas’ work argues for AI models that don’t just calculate but truly “understand” human context and thought. As patent-protected “symbolic mathematics” frameworks emerge, Lucas predicts a near future where machines and humans collaborate in new, high-quality ways, grounded in mutual understanding—fulfilling the long-promised vision of human-AI synergy.

Transcript

[A]

Foreign welcome to Coruscant Technologies, home of the Digital Executive podcast. Do you work in emerging tech? Working on something innovative? Maybe an entrepreneur? Apply to be a guest at www.corazont.com brand welcome to the Digital Executive. Today's guest is Martin Lucas. Martin Lucas is the inventor of deterministic AI and decision science proven across more than 100 global brands with results up to 76% above market performance. He has spent 10 and a half years designing and proving a new kind of intelligence, one that works with the laws of physics instead of probabilities. His work redefines how machines make decisions and how humans understand their own. Martin's career spans multiple businesses, four, five books and projects that bridge technology, psychology and philosophy. His research has influenced government thinking, including a paper written for the Prime Minister's office and a personal accommodation from the Home Secretary for Innovation in Behavioral Design. Well, good afternoon Martin. Welcome to the show.

[B]

Thank you, Brian. It's great to be on.

[A]

Absolutely, my friend. I appreciate it. And you're hailing out of the London area in England, the uk. I'm in Kansas City. So I appreciate you hanging out, making the time today, traversing these time zones. And Martin, jumping into your first question, you describe your work as going beyond probabilistic AI to what you call deterministic AI and introducing a framework known as decision physics. Could you walk us through what you mean by deterministic intelligence, how it differs, differs from current models, what drove you to develop it and why you believe it's necessary for the next generation of machine led decision making?

[B]

Awesome. Great question. So where I started in 2015, so I've been at this for 10 and a half years, was why don't humans understand humans? Right? And that accelerated through to like how does decision making work within the brain and how, what's all the variables that go into it and that. I'll come back to that and as we develop that forward. Right. But deterministic AI solves the biggest problem that sits inside AI. Today it's known in the AI community as the 30% problem. So every prompt that a human sends, every software that's run by code has this 30% issue. And what it means is that because it's a stochastic model and a probabilistic math model, it is inconsistent with what it does. So imagine for example, you're a finance company and you want to use AI for loan applications. It will reject some people and it will then accept other people that are exactly the same. In pharma, meds, research, it Means that you will get unreliable results. So it could say that this medication is good one time, that you run it, you could run it again and let's say it's bad. That's the 30% problem. So in market and personalization, you've got people looking at customer actions and it becomes all inconsistent. So what we've done with deterministic AI is created AI that allows you to be accurate, so it takes away the probability, so you get continuity and consistency of results. So it's not like a choice of deterministic AI or AI as it exists. This actually overlays onto existing AI and makes it accurate. So it solves a pretty big problem for reliability.

[A]

First of all, that's awesome. Thank you for sharing that. And yeah, over the last 10 years, we were talking earlier, you've just done a lot of work in this area, a lot of research, understanding, as you said, why humans don't understand humans. Deterministic AI allows for accuracy, reliability and more consistency. So I appreciate you breaking that down for us. And Martin, your career spans technology, psychology, philosophy, and you often say that intelligence, human or artificial, needs to be understandable, reproducible and deeply human at its core. How do you reconcile building rigorous deterministic systems with the messy, emotional content rich world of human decision making? How much of your work is engineering and how much is behavioral science?

[B]

So if it's okay with you, I'm going to run a fun test. I believe it's fun, but you can tell me at the end. Right? A fun test for you and for anyone listening to this. Right. Because what we've done is built intelligence that works with human thought, that understands it. You've got the deterministic AI that makes AI accurate. And then as you mentioned, for the messy stuff, like what goes on inside the brain is what we've built with the decision science and decision physics. If it's okay with you, we'll run a little test. Right, that cool, Brian?

[A]

Sounds great.

[B]

Right, So I want you and everyone listening to this, just take a breath and I want you to think about a brand that you genuinely love, one that feels right to you. What's the first thing that comes to mind when you think of them?

[A]

Trustworthy?

[B]

Okay. And when, what do you feel when you see or hear them? Right. You mentioned trust. Is it security? Is excitement? Is it belonging? Is it identity? Like what kind of feelings do you have towards that brand?

[A]

Just again, trust, A relaxed feeling that my experience, my next experience is going to be as great as the last one.

[B]

Awesome. Thank you. And Then what we're going to look at next. So this is great. So Brian's just exploring how he feels about a brand that he likes. So I want you to keep thinking about that for everyone that's listening to. Right. When you think about how the, the tone and the words they use towards you, like, do you, do you, do you want them to be calm, to be bold, to be more human, to be more clever, to be simple? Like, what is it about the brand that makes you engage with them?

[A]

They meet my needs. It seems like they're a step ahead. Or every again, every experience I have with this brand does not let me down.

[B]

Yeah, that's fantastic. It's really, really fantastic because one of the biggest things about decision science and unsung decision making that most brands fail at and the world doesn't understand is like most people are looking to understand. What problem do you solve for me?

[A]

Right.

[B]

So as a side note, the average human brain makes 36,000 decisions a day and only 2% of them are conscious. Right. So you could have a brand that like, has all the things that you want, but if it doesn't speak the way that resonates with you, then you reject it. Right. So just picking it up again. Right. Do you love everything that they sell or just certain things?

[A]

Most everything, yeah. And I'm probably would look at other things that I haven't purchased from them just because they've done such a great job with their products.

[B]

Yeah. Cool. Okay. And describe the time when they absolutely nailed the experience. Whether it was online, in store, general communication, what made it stand out for you?

[A]

Just the experience, using their products. Just phenomenal. Everything from setup, configuration, to enjoying the entertainment that they provide.

[B]

Love it. Okay. And when you think about when you're engaging with them, and I don't mean purchase. Right. Because the world is very much focused on, on purchase to the cost of like what people want from an experience. When you think about when you want to engage with them, is that like when you're scrolling at the weekend, is it late at night? Is it when you're at work, is it when you're on holiday? Like, what does the timing look like for your relationship with that brand?

[A]

The timing is. Or when I most think about it is probably when it's time to relax with the family or. Yeah, just some downtime. And that's probably when I think about them the most.

[B]

Nice. Thank you. So we're just exploring all these different conditions that go through your brain. Right. This is how you're. The architecture of Your decision making for this particular brand. Right. And it sounds so simple. But if we look at it conversely, right. If they ever done something that made you hesitate, like the tone fell off, the campaign was wrong. They're just sending you too many messages, too many discounts, like in the infant, that kind of broke that magic of your, your relationship with them.

[A]

Not that I can think of, but I'm sure it's happened, but I just can't think of a time or example.

[B]

All right, cool. Okay, so final, final question for me and appreciate you doing this. Right. So hopefully it's been fun to explore that kind of component, Right. When we think about your loyalty towards this brand. And I mean the feeling of loyalty inside the brain, right? Like, is it the product? Is it how they make you feel? Is it when they engage with you, is it new products, is it new experiences? Like, what drives you to think of that brand, to want to go back to them again and again?

[A]

They don't disappoint. They deliver high quality products and the experience has always been the best.

[B]

Love it. Thank you. So what we've been looking at there, right, is we've been examining Brian's relationship with that brand, his emotional context, his language, his timing, what resonates with him, what experience that he wants. That's what we've done with decision science. So that each brand, and we worked with over 100 different brands, 120 million different tests we performed, 76% above the industry. Right. Which is all wonderful because what we're doing is understanding each audience and what experiences they want and what experiences they don't. That's what decision science is. Harm.

[A]

Amazing. Thank you. And I appreciate stepping us through those examples. This will be great for the audience. So, Martin, you emphasize augmentation rather than replacement, designing machines that work with human decision making rather than supplement rather than supplanted. Right. In your view, how do organizations balance automation and human agency in decision science? And where do you draw the line? Especially in domains like health or mission critical infrastructure?

[B]

Yeah, I think that, I think the text the next 10 years is going to be very much about alignment. So machine logic and the acceleration of what machines can do and what AI can do is fantastic. But it must understand intent and context. So going back to what I mentioned about deterministic AI, when you've got a 30% drift problem, you can't use that in healthcare, you can't use that in finance, you can't use that in pharma. Deterministic AI reduces that to less than 1% error rate, which is perfect for any kind of governance market. So at the moment, it's a little bit too Wild west and too much of an AI bubble that's going on with not enough governance and security. And the funny thing is that governance and security means that you can actually understand humans better. And what meets in the middle for me over the next 10 years is that the rise of human creativity, of ideation, is going to manifest it in slightly different ways, but you're never going to be able to replace that human creative component. So I think it's a world of opportunity if we understand how to manage what technology brings while still using the best of what makes humans humans.

[A]

Thank you. I appreciate that. Love to get people's insights here on the podcast, but you talked about the future, next 10 years is that alignment again. The technology, it's got to understand intent, it's got to understand the context. But what I took away from it, though, is that rise of human creativity may look a little bit different than it does today. But I absolutely agree there's nothing that can replace a human being and its creativity. So I appreciate that. And Martin, the last question of the day I have for you, looking ahead even 5, 10 years, how do you imagine the landscape of AI, cognition and decision making evolving with your deterministic framework? What kind of world or systems do you hope to enable? And what legacy do you want your work to leave behind in the way humans and machines interact?

[B]

Yeah, so I'll give you an exclusive here. So we've just signed off the patent and the invention. The most code today works on the basis that you're telling a machine software what to do. Right. That's traditional computing code. What we've built, the deterministic AI combined with decision science, combined with decision physics, is symbolic mathematics. And what that does is teach a machine how. And once a machine understands how, it means it can operate with reasoning and understanding. If it's got reasoning and understanding, it's not just that it can do things quicker, but it can do things by you explaining and teaching it the context of the people that it's trying to serve. So rather than just being able to produce lots of stuff, which is what lots of businesses are doing right now, I believe. And what I'm going to prove is over the next 10 years, it becomes about quality and how you serve people better and how you understand humans. And you can combine that mythology of the sci fi of humans and machines working together. So I'm excited for what the future looks like. There's lots of opportunity.

[A]

Thank you. And you did tease that apart a little bit. Generally, the traditional code is written to instruct AI to perform certain algorithms or certain functions. But your decision science and physics is really about symbolic mathematics, I believe, is what you said. And you're definitely taking a different approach around this. And I really appreciate you sharing that with our audience today. And Martin, it was such a pleasure having you on today, and I look forward to speaking with you real soon.

[B]

Thank you, Brian. It's awesome.

[A]

Bye for now.