Andreas Welsch 0:00

Welcome back for another episode of "What's the BUZZ?", where leaders share how they have turned hype into outcome. Today we'll talk about what happens when different technologies converge, right? We've been talking so much about AI that we are almost neglecting things like cybersecurity and quantum and how they fit in. So I'm super excited to welcome Joseph Ng on the show to talk more about that. Hey, Joseph. Thank you so much for joining.

Joseph X Ng 0:20

Thank you for the invite. I'm happy to be here. It's very exciting times indeed.

Andreas Welsch 0:24

Oh yeah, for sure. Joseph I was wondering if you can introduce yourself real quick to the audience for those who might not know you yet.

Joseph X Ng 0:32

Oh, hello. My name is Joseph Ng, and I am the chief strategy officer for Gene Genius, and I recently published a book named The Hybrid Mind: The Human-AI Convergence.

Andreas Welsch 0:46

So- That's exciting. Yeah. So- Yeah I know you're right at the center of where all of these things come together. We've obviously been connected over LinkedIn for a while. We got to meet at the AI summit at the end of last year and said we, we should definitely spend some more time talking about what happens when all of these things converge. Now obviously there is so much focus on the AI race in- I'm wondering what are you seeing? What's your take? Is this really where companies should focus all of their efforts these days?

Joseph X Ng 1:16

That's a great question. When people talk about AI race, they usually frame it as a race for bigger models, more GPUs, faster inference, or lower costs. That matters, but I do not think that should be where all the resources should be concentrating. These things will continue to evolve. The more important question is whether an organization actually knows how to operationalize intelligence inside the business. AI is moving from being a tool that analyzes information to becoming a system that participates in those c- decisions. As that happens, the real challenge is no longer access to AI, the challenge becomes architecture. How do you govern the system? How do you manage risk oversight and human authority when intelligence is embedded directly into the workflows?

Andreas Welsch 2:12

Yeah.

Joseph X Ng 2:12

So if I was advising companies, do not spend all your time chasing the AI race as if winning means having the biggest models or most compute. Spend your time in the architecture. And then allows AI to operate responsibly and productively inside your institution.

Andreas Welsch 2:31

I think that's a very sensible call, es- especially these days when, one headline chases next There's a new model that comes out almost every other week it seems, or at least a new feature. So making sure you, you actually get your foundation right. And it's great because I think a lot of times we talk so much about data as being a foundation, but I think if you go one level above that it is indeed the architecture, the orchestration, the guardrails, the guidelines the evaluation and so on. So good to hear that for sure. But what does it mean from your perspective when AI participates in these discussions like you said, or it participates in decisions? What does it mean? How does it change the dynamic?

Joseph X Ng 3:12

Historically, AI sat outside the decision process. It generates insights, dashboards, predictions. The human was still clearly in control of the final decision. What's changing now is that the AI systems are actually starting to act with- inside their workflow. They are approving transactions, generating responses, even triggering downstream actions across systems. At that point, AI is no longer just supporting decisions, it is part of the decision-making process itself. You can no longer rely on traditional assumptions around accountability. You need to be able to answer very basic but critical questions. Why did a system make that decision? What data did it use? What rules did it apply? And most importantly, if something goes wrong, who is responsible? I address this problem in my book The Hybrid Mind. The idea is that when humans and AI systems are operating together, they ne- need a structured way to manage that interaction. That is where concepts like OODA loop, observe, orient, decide, act, and most importantly, feedback- is very key.

Andreas Welsch 4:27

Yeah.

Joseph X Ng 4:28

Yep.

Andreas Welsch 4:30

I think es- especially this part about, observing and feedback, that's, that sounds like a huge opportunity. I was just talking to a former colleague of mine and we were working on machine learning projects like almost 10 years ago. And one of the big challenges was whenever we had a substantial amount of new data, say six months after a model training, we would have to go back and retrain the model on, on, on that new larger set of data. And you would have to test, and you would have to see does it still decide and act in, in ways that you had already previously approved, or do you need to make changes again? So to me this feedback loop, this a- almost constant learning that's a real big benefit of looking at things like agents that they can pick up information on the go or for the next go around, basically. So a lot of promise in, in my mind, a lot of opportunity to cut down on this batch type learning when it's more interactive and more on the go. Yeah. I see a lot of companies de- deploying these AI tools whether it's, "Hey, we've given everybody Copilot and we're done. That's our AI strategy," or, "We, we, we have this little feature, I don't know in, in our travel and expense app and you can scan your receipts and now we're all AI enabled." But I feel a lot of times this type of mindset falls short of expectations of delivering something bigger, more, more meaningful that captures a lot more value than just giving somebody a tool and expect them to work on that. What are you seeing? How do you feel organizations can evolve? What are some of your... the recommendations that you have there?

Joseph X Ng 6:08

Definitely. This is a very key question. What most companies are missing at this point is that they're still treating AI as a tool deployment problem- When it's really a decision systems redesign problem. Right now, a lot of pro- organizations are adding AI in very narrow ways. They introduce a chatbot, a co-pilot, like you're saying- An automation fe- feature, and they call it this, their AI strategy. In reality, what they have done is a layer of intelligence on a legacy operating model- without changing the architecture underneath.

Andreas Welsch 6:44

Yeah.

Joseph X Ng 6:44

That works for small productivity gains, but it does not hold once AI starts influencing judgment, routing actions, or participating in decisions that have business and regulatory and human consequences. The gap is that most companies have not mapped these decisions or action made independent of humans at this point.

Andreas Welsch 7:11

So-

Joseph X Ng 7:11

Which is the logic behind cognitive AI a- and native architecture or CANA. CANA is not about adding AI to the stack, it is about restructuring the stack so intelligence, oversight, and human authority are built into how the system operates from the start.

Andreas Welsch 7:30

So question there, I know you've previously worked in financial services for a large and well-known financial services company. I'm assuming there's already a muscle built o- over the years from predictive analytics to machine learning, to generative AI, to agentic AI. And I see this a- across many different industries, that the larger the organization, the longer they've been at this problem and have been working with this kind of technology, the more it becomes second nature and the next evolution of technology changes a few things, but not fundamentally what the organization is. But I also see smaller organizations struggling with this, right? When you talk about architecture it's not that easy for a mid-sized bank, for example or mid-sized in- insurance broker to do that heavy lifting of thinking about what does our architecture look like. What do you recommend there? What can companies and leaders do that's practical when they don't have a large centers of excellence or dozens or hundreds of people working on this, but two or three?

Joseph X Ng 8:29

So what I would do or recommend for these organizations that are small in scale is to first do a audit of their current systems and their s- systems in general, and then see where those breaking points would be. Yeah. And then how we can integrate AI into the mix. It's not a full redesign. It's more modular in essence.

Andreas Welsch 8:55

So that, that also means reducing your risk a little and being able to show something more quickly and make progress and continue making progress as you go.

Joseph X Ng 9:04

Exactly. And this is what I call phased approach. Whenever we're doing large projects, we break it down into certain phases. Yeah. And es- especially creating some objectives and within there- To accomplish s- goals, right? So yeah. I think this is the best approach to a small organization.

Andreas Welsch 9:27

Thank you. Thanks for sharing. That's great. Maybe you're seeing this next thing too, right? A lot of times I feel that I see this kind of low quality output or work slop in my inbox. "Hey, can you take a look at this? I created a first draft. What do you think?" And you start reading and you're like, "Yeah, I don't know. It's not bad, but it's also not good. It's not very authentic. It misses details. It misses depth. It misses teeth." That's one of the big challenges I see and that I hear from former colleagues as, as well in, in corporate who say, "Hey, I get all of this stuff and it reads like somebody just pulled this out of Copilot or ChatGPT." And to me it feels where seven years ago, five years ago, even before ChatGPT we're talking so much about business processes about business functions and their productivity, changing processes with AI. Now we're in some ways a step back talking about personal productivity, like you said. But what are some of the other mistakes maybe or other challenges that you see that companies are running into when they just roll out an AI tool, a gen AI tool, maybe now agentic AI tool? What else are they missing?

Joseph X Ng 10:36

One of probably the biggest mistake that organizations are making right now is they're scaling capability much faster than they are scaling control. There's a lot of pressure to adopt AI quickly, deploy models, launch features, automate workflows. A- and everything on the surface looks like it's progress. Systems become more capable, and organizations feel like they're moving forward. But underneath that, many of the systems are being deployed without a clear understanding of how they behave- how they are exposed, and how they can be influenced. So what you end up with is a huge gap that increases risk, and that risk compounds very quickly.

Andreas Welsch 11:21

I think that's an important point, right? A lot of times we think about this being additive, but it's actually more exponential. Thinking about agents or multiple agents in a system. It's not risk plus risk, but plus risk, but times, right? When each part in the chain be- becomes a failure point or it becomes a breaking point.

Joseph X Ng 11:40

Exactly.

Andreas Welsch 11:41

Yeah. So there's specifically it's not just again, a- about rolling out tools. It's about understanding what does that risk mean. And it's funny you say we're rolling out capability faster than governance. I'm thinking in, political terms. If I look at the US, if I look at Europe if I look at Asia on a larger scale we're seeing how this can play out, right? Being more liberal with innovation in trying to be first to market or in, in that AI race on a on a global scale competing others in the EU taking a much more deliberate a- approach and starting with regulation first. Say, "How can we protect our citizens? How can we protect the data? How can we uphold our values?" And I'm seeing this, to your point on a smaller scale in businesses too. But certainly when AI labs push out innovation weekly some even daily it seems and you're still thinking in quarterly roadmaps or in, half-year planning cycles. I'm sure your executives are asking, "So what are you doing? How come we're not shipping as, as fast?" So it's definitely a challenge for sure. But a- along with that there, there are risks beyond the high level what if something fails if you introduce AI, if you introduce agentic AI. What are some of those that, that you typically see, and what do you advise companies and leaders on addressing them?

Joseph X Ng 13:06

Risks in AI is very interesting because there's a new technology that's coming around the corner, right? Quantum changes the conversation because it is no longer a incremental improvement in computing. It fundamentally expands what computationally is possible. With classical systems, there's entire categories of problems that are simply out of reach right now because of the how long it would take. Quantum computing changes that. It opens the doors to solving highly complex optimization problems, simulate mo- molecular interactions at a level of precision we have never, ever seen before.

Andreas Welsch 13:46

Yeah.

Joseph X Ng 13:46

And advancing probabilistic modeling in ways that could reshape industries like healthcare, science- And finance. The biggest concern for me is the cryptography. Today, most of our digital infrastructure relies on encryption methods that are secure, but because they are computationally difficult to break- and it takes many years in order for it to be possible. Quantum computing changes that assumption, and the issue is not just future risk, it's actually now. There's a concept called harvest now, decrypt later- where sensitive data is being collected today with the expectation that it could be decrypted once computing powers like quantum matures. The real question is whether we are building the governance, security, and infrastructure to handle what technology becomes possible when it does arrive. Yeah.

Andreas Welsch 14:44

So I'm part of a of a global network of chief architects and recently this topic has been moving more to the forefront as well to say, "Hey, what are we actually doing to a- address this and what can we do proactively?" Because we see the writing on the wall as, as soon as quantum is out there and it's commercially viable and usable, there is a high risk that our current cryptography methods are out the window there. They're useless or in- ineffective. And I think that's one of the key risks I'm not seeing enough people talk about yet because we're so focused on, "Hey AI productivity," and summarizing meeting minutes and all that good stuff. But when this quantum leap is really next, what are some of the, again, things organizations can do right now? How can you harden your security and what can you actually do to prevent some of the harm that is likely going to come in the future?

Joseph X Ng 15:37

And you're totally right, Andreas. Most organizations are still asking which model to use or which to deploy, right? Those are downstream questions. The real risk of, o- of the systems is what we're talking about quantum, right? The first step to map is to map out the decision systems that we have, not just the workflows. Leaders need to understand where decisions are made, where AI can advise or well advise or act. Yeah. Okay? The second a- is architecture. This future is not a single model layered onto a legacy system, but it is a actual orchestration environment of models, agents, data, and humans. If the architecture is not designed for that, governance will always lag behind capability. And finally, Third is the governance layer, right? It has to be embedded into the execution. It cannot live in a policy somewhere in your network that no one l- reviews for a few years, and then finally someone, something or some event happens, and then you have to pull it up in the middle of the night, right?

Andreas Welsch 16:51

Yeah.

Joseph X Ng 16:52

It needs to exist inside the system through chase ability, access control, and clear human authority over critical decisions. And the role of the leadership is not just to adopt AI, but is to build an institution that can operate both intelligence responsibly, visibly, and at scale.

Andreas Welsch 17:12

I think that's a key point. Thinking back roughly 25 years I remember in cybersecurity there, there were these rainbow tables. Basically, what are the passwords or common passwords and how can you guess them or through brute force get in. We've moved on to more complex passwords, right? Should be at least, I know, 16, 32 characters special characters, uppercase, lowercase, numbers and whatnot. Now we're talking about pass keys, but even that, like with this kind of technology doesn't stand a chance. So to your point it takes a multilayered approach. In, in, in some ways I feel people are still going to be the weakest link in your technology strategy, let alone now being technology or technology even being weaker than it used to be. But the other part that I think is more more hopeful, I would say, or more, more promising more, more positive in, in, in that sense is you say, hey, quantum computing you can find new molecules, you can do a lot of other things. So let's talk about that very briefly too. Aside from cracking cryptography, what are some of the good things that you can actually use the technology for?

Joseph X Ng 18:19

There's many possibilities within quantum that can be very positive and very impactful, right? I think in terms of quantum, there's a lot of opportunities in genomics. So within GeneGenius we will be excited to explore that capability once it's available. And how do we map genomes or the human genomes, right? And then provide capability of discovery on drugs, and then the n- next enhancement of drug capabilities to solve some of humans' or society's most rare diseases.

Andreas Welsch 19:03

That sounds that sounds very promising and like a like a great cause for sure, right? Now you've also talked a bit about your book The Hybrid Mind. In it you, you also talk about what that organization looks like that fuses machines or AI and humans to-together, or where they collaborate. When I say fuse I don't know, it doesn't sound quite right, but where they work effectively together. Let's maybe go with this one. What does that organization look like?

Joseph X Ng 19:30

W- what led me to write The Hybrid Mind was not just a single moment, but a pattern I kept seeing within the industry, academia, and also the real world, right? In enterprise environments, especially in regulated sectors like banking, I saw how decisions are actually made. As AI entered those environments, it became clear that the technology was advancing much faster than the systems designed to govern it. At the same time, in academia there was a gap. Conversations were focused on models and performance, but not on what happens when AI is embedded into real decision-making systems and who is accountable. Through the, a work in AI governance and platforms like GeneGenius that we just talked about, I saw the shift firsthand, where the challenge is no longer just building something that works, it is building something that can be trusted, audited, and aligned with human responsibility. The book is an attempt to formalize that shift and bringing together concepts like governance first design into something organizations can actually apply. At its core, it is about how institutions need to evolve when intelligence is no longer outside the system, but is enclosed and operating within.

Andreas Welsch 20:50

Yeah. Awesome. So folks, for you in, in, in the audience, definitely recommend pick up Joseph's book, The Hybrid Mind, to learn more about how to facilitate that change and prepare for what's next. Now Joseph, we're getting close to the end of the show, and I was wondering if you can summarize the key three takeaways for our audience today. We've covered a lot of ground but what are the three things that folks should take away from our conversation today?

Joseph X Ng 21:14

All right. AI is moving from a tool that analyzes information to a system that participates in decisions. Once that happens, the focus is no longer just capability, it becomes control, accountability, and design. The AI race is not about where differentiation will come from. It will come from who can build the systems that govern how intelligence operates inside organizations. On the security side, the attack surface has changed. Systems do not need to be broken into. In the traditional sense, they can be influenced through normal transactions and interactions, which means governance and security must be embedded directly into the architecture.

Andreas Welsch 21:59

Yeah.

Joseph X Ng 22:00

Quantum reinforces the same point. Every leap in compute increases both opportunity and risk. The more powerful these systems become, the less tolerance there is for weak infrastructure and delayed preparation. For leaders, the shift is clear. This is no longer about adopting AI tools. It is about redesigning how decisions are made, how systems are structured, and how responsibility is maintained when intelligence is shared between humans and machines. That is what the hybrid mind is all about. It is a blueprint for operating in a world where intelligence is distributed, automation is s- consequential, and governance must be built into the system from the start.

Andreas Welsch 22:47

Awesome. Thank you. Yeah. Thank you so much for sharing, Joseph. It was a pleasure having you on. Thanks for sharing your expertise with us. Always great talking to you. And yeah, thanks for joining.

Joseph X Ng 22:57

Oh, thank you so much. It was a fun, fun discussion, and very critical timing too.