Artificial General Intelligence and the Future of Responsible Governance
20 Feb 2026 11:00h - 12:00h
Artificial General Intelligence and the Future of Responsible Governance
Summary
The panel opened by noting that rapid advances in AI since 2020, especially the surge of powerful models in 2023-24, have sparked renewed debate about the emergence of artificial general intelligence (AGI) and the risk of missing the opportunity to shape it responsibly [1-4][6]. Participants agreed that while many definitions exist, AGI is generally understood as an AI that can reason, learn, adapt, transfer knowledge and operate beyond narrow, task-specific domains [12-18]. Simonas Satunas offered a concrete, albeit simplified, definition: an AGI would perform any human professional task with comparable accuracy, and he estimated a 3-to-7-year horizon for reaching that milestone based on growing public trust in generative AI tools [21-24].
The discussion highlighted that massive compute investments are driving the current AI boom, but compute is only one element of a broader ecosystem that also requires energy-efficient hardware, data, and especially human capacities such as critical thinking [70-71][72-85][86-90]. Alexandra emphasized that achieving human-like situational awareness will demand access to large amounts of private data, raising privacy concerns and underscoring the need for robust regulatory frameworks [35-37][38-41]. Kenny warned that as AI becomes more capable, it can both generate sophisticated attacks and mimic human decision-making, making security threats more realistic and amplifying the importance of educating defenders [105-108][110-112].
Simonas Satunas outlined a four-tier risk hierarchy-from traditional privacy and cyber-fraud to mental-health impacts, social empathy erosion, and macro-level threats to democracy-calling for coordinated national and international strategies to mitigate these costs [131-138]. The panel concurred that critical thinking and public awareness are essential safeguards, with education needed to help people identify AI-generated misinformation and understand underlying threats [154-155][164-170]. Regarding governance, participants suggested technical measures such as model labeling and broader regulatory actions, noting Europe’s tendency toward over-regulation but recognizing the potential of reasonable standards [173-176].
Alexandra proposed building resilience through rollback mechanisms and contingency planning, likening the approach to preparing for electricity outages to reduce the impact of AI failures [187-190]. Kenny introduced the concept of an “AI Operating Procedure” (AOP) analogous to existing SOPs, which would institutionalize bias reviews, ethical training, and continuous validation of model outputs [191-199]. The discussion concluded that immediate actions should include investing in education, establishing robust risk-mitigation frameworks, and developing early-stage “anchor controls” to guide the safe evolution toward AGI [202-207][173-176].
Overall, the panel stressed that while AGI may be imminent, its safe deployment depends on balanced compute investment, privacy-respecting data practices, security preparedness, and proactive governance structures [70-71][105-108][173-176].
Keypoints
Major discussion points
– Defining AGI and estimating its arrival – The panel agreed that AGI means an AI that can reason, learn, adapt, transfer knowledge and operate beyond narrow tasks [12-18]. Simonas Satunas offered a concrete, if simplistic, definition – an AI that can perform any human task with professional-level accuracy – and projected a 3- to 7-year horizon for reaching that milestone [21]. Vinayak opened the session by noting the rapid acceleration of AI since 2020 and the growing debate around AGI’s feasibility [1-2].
– Compute, data, and the human factor as essential ingredients – While compute power is often highlighted, Simonas Satunas emphasized that it is only one link in a chain that also includes energy, data, implementation, language and, critically, human education and critical-thinking skills [72-89]. Alexandra added that achieving human-like situational awareness will require low-latency, energy-efficient hardware and massive private data, raising privacy limits [31-36]. Vinayak later asked why massive compute investments are needed for attention, context, reasoning and low latency [65-69].
– Security, privacy and ethical risks of increasingly powerful AI – Kenny Kesar warned that as AI accuracy improves (moving from 90 % toward “five-nines”), the technology will become capable of both sophisticated attacks and autonomous decision-making, creating new security threats [41-48][105-108]. Simonas Satunas outlined four risk layers-from classic privacy and cyber-fraud to mental-health, social cohesion and macro-level threats to democracy-calling for coordinated national and international strategies [131-138]. Alexandra highlighted the need for human oversight, pointing out how algorithmic bias can be exposed and corrected (e.g., the NBA video-surveillance example) [96-102].
– Governance, “anchor-control” mechanisms and early-stage regulation – The moderator asked for concrete control concepts to guide the transition to AGI [172-176]. Simonas Cerniauskas suggested technical safeguards such as model labeling and broader regulatory measures, noting Europe’s tendency to over-regulate but also its potential for viable standards [173]. Simonas Satunas argued that small nations must collaborate globally to embed moral and egalitarian values into AI development, citing the Myanmar-Meta case as an illustration of ethical failure [174-180]. Alexandra proposed building resilience and rollback mechanisms to mitigate the impact of failures, emphasizing a risk-reduction mindset [187-190].
– Impact on cognition, critical thinking and societal dependence on AI – Several speakers expressed concern that pervasive AI use could erode individuals’ critical-thinking abilities, creating a feedback loop where AI-generated content dominates training data and stifles human intellectual growth [150-170][164-170]. The panel stressed the need for widespread education and awareness to help people identify manipulation, disinformation, and “cognitive warfare” [154-155][155].
Overall purpose / goal of the discussion
The session was convened to clarify what “Artificial General Intelligence” (AGI) actually means, to assess how close we are to achieving it, and to explore the security, privacy, ethical, and governance challenges that AGI will introduce. Participants aimed to identify early-stage “anchor controls” and practical steps-technical, regulatory, and educational-that societies can adopt now to prepare for the transformative impact of AGI.
Overall tone and its evolution
– The conversation began with an optimistic, exploratory tone, highlighting rapid AI progress and the excitement of defining AGI [1-2].
– It then shifted to a cautious, risk-focused tone, as speakers detailed technical limitations, compute demands, and the widening gap between current narrow AI and true general intelligence [21][31-36].
– Mid-discussion the tone became protective and solution-oriented, emphasizing security threats, ethical pitfalls, and the need for robust governance and resilience [41-48][131-138][187-190].
– Toward the end, the tone turned reflective and advisory, urging education, critical-thinking cultivation, and coordinated global action to mitigate societal dependence on AI [150-170][154-155].
Overall, the panel moved from enthusiasm about AI’s potential to a sober assessment of the safeguards required before AGI can be responsibly deployed.
Speakers
– Mr. Vinayak Godse – Moderator/host of the panel discussion on AGI; leads the conversation and poses questions to panelists. [S2]
– Mr. Simonas Satunas – Panelist; provides a simplified definition of AGI and discusses timelines and societal impact. [S1]
– Simonas Cerniauskas – Panelist; contributes perspectives on AGI definitions, investment cycles, and the broader AI ecosystem. [S5]
– Mr. Kenny Kesar – Panelist; consultant advising AI clients, focuses on accuracy, compute, market disruption, and ethical/operational procedures for AI. [S6]
– Ms. Alexandra Bech Gjørv – Panelist; head of SINTEF, Norway’s largest research institute; discusses hardware, neuromorphic computing, privacy, governance, and societal implications of AGI. [S7]
Additional speakers:
– None. All speakers appearing in the transcript are accounted for in the list above.
The panel opened with Vinayak Godse framing the rapid expansion of artificial-intelligence research since 2020 and the surge of powerful models launched from early 2023 as a catalyst for renewed debate over artificial general intelligence (AGI) and the risk of missing the chance to shape it responsibly [1-4][6]. He warned that societies that do not begin to understand what AGI could mean for the next three to ten years will fall behind in governance and policy [5-7].
A broad consensus emerged that AGI must transcend today’s narrow, task-specific systems. Speakers agreed that a true AGI should be able to reason, learn, adapt, transfer knowledge and operate across domains rather than being confined to a single function [12-18]. Simonas Satunas offered a concrete, if simplistic, definition: an AGI would perform any professional human task with comparable accuracy and professionalism [21-23]. Citing a poll in which roughly 50 % of Israelis said they trust generative-AI tools more than friends, he projected a three-to-seven-year horizon for reaching that milestone [24-25][21].
Technical foundations and compute – Kenny noted that moving model accuracy from the current 90 % toward “five-nines” (99.999 %) historically required five to ten years for the first extra nine, and each subsequent nine adds roughly one to two years [41-48]. Cerniauskas warned that the industry may be heading toward an “over-capacity” situation for a couple of years, quoting Zuckerberg’s comment about excess compute resources [80-82]. Satunas used a 19th-century transport-infrastructure metaphor to argue that compute is only one link in a chain that also includes energy-efficient hardware, vast data, implementation expertise, language resources and, crucially, human critical-thinking capacity [72-90]. Alexandra added that achieving human-like situational awareness will require low-latency, neuromorphic or edge-computing architectures and access to large amounts of private data, which in turn raises serious privacy constraints [31-36][35-37].
System 1 / System 2 thinking and latency – Vinayak highlighted the distinction between intuitive “system 1” and logical “system 2” thinking, noting that the latency of purely language-based models limits system 2 performance. The panel agreed that reducing system 2 latency is essential for AGI, and that AI is helping to close this gap [65-69].
Security, privacy and risk taxonomy – Kenny emphasized that more accurate models will be able to launch sophisticated cyber-attacks and could emulate a CEO to make decisions, making AI-driven deception a concrete threat [105-108]. Satunas presented a four-level risk taxonomy: (1) classical privacy, security and fraud risks; (2) human health and mental-health impacts; (3) social effects such as erosion of empathy, bullying and addiction; and (4) macro-level threats to democracy and foreign manipulation [131-138]. He stressed that mitigation at each level will require costly, coordinated national and international strategies [131-138]. Alexandra reiterated that privacy limits on personal data impede the development of the deep situational awareness required for AGI, underscoring the tension between data needs and privacy protection [35-37].
Ethics, governance and “anchor-control” proposals –
* Technical labeling and European-style regulation were advocated by Cerniauskas as an immediate lever [173-176].
* Satunas called for a global, multi-stakeholder regulatory framework that embeds egalitarian values into AI design, citing the Meta algorithm that amplified violent content in Myanmar as a cautionary example [174-180].
* Alexandra proposed resilience and rollback mechanisms-analogous to planning for electricity outages-to limit the impact of AI failures [187-190].
* Kenny introduced AI Operating Procedures (AOP), formal SOP-like processes that embed bias reviews, ethical training and continuous validation into organisational practice [191-199].
Critical-thinking concerns – Vinayak warned that AI’s ability to provide rapid, multi-dimensional attention may erode human critical thinking, which he defined as “the ability to give attention to various dimensions” [156-163]. Kenny quantified the problem, noting that roughly 30 % of online content is already AI-generated, creating a feedback loop that could stall the evolution of human intellect if people stop exercising their “brain muscles” [164-170]. Satunas echoed this, urging investment in education that cultivates critical-thinking skills to prepare society for AGI [87-90][154-155].
Commercial viability – Kenny observed that AI is not commercially viable today because the costs outweigh the ROI [200].
Closing remarks and concrete outcome – After summarising the discussion, Vinayak thanked the participants, announced the launch of the “AI Cyber Security Terminal”, and noted the upcoming photo-shoot [210].
In conclusion, the panel agreed that AGI is likely to arrive within a near-term horizon, but its safe realisation depends on balanced investment in compute, energy-efficient hardware, high-quality data, and, crucially, human critical-thinking capacities. Immediate actions include developing AI Operating Procedures, establishing technical safeguards such as model labeling, investing in education to preserve critical thinking, pursuing tiered model strategies to manage compute costs, and creating resilience and rollback plans for AI failures. Unresolved issues remain around the exact timeline for AGI, reconciling privacy with the data needs of situational awareness, defining globally acceptable governance structures, and preventing the erosion of human cognition through AI-generated feedback loops. Addressing these challenges will require coordinated effort across industry, academia and governments, both nationally and internationally, to embed ethical, transparent and robust controls before AGI becomes a pervasive reality.
Pet Summit and the basic idea and intent behind setting up this session is while all the things were happening in AI in the period of 2020, a lot of development happening and somehow all that is now leading to kind of acceleration that we are seeing in last three years of time and especially this year, since January, all the new launches that we see, we are getting the first sign of a powerful AI, right? And now because of that, there is a discussion about AGI seems to be gaining quite a significant ground, right? And although people still have a lot of doubt and skepticism about whether it is really reality or possibility in coming future or what that means, many people are still skeptical.
They are struggling to define what that means for a cigarette. So as an overall society. and I can tell about India so probably we didn’t pay much attention when AI was coming. If you don’t pay attention now what is coming in next 2, 3, 5 years of time or 10 years of time that is probably the timeline for AGI, then probably we will miss on again thinking, talking, discussing, governing it better basically. So this discussion is about what is to help understand for us and for the audience here basically what do we mean by AGI can we really think about that right now what are different conference that we need to thank you for getting welcome to the panel and try to then find the meaning possible meaning for security, privacy and ethics basically.
So I would like to talk with someone with you, so how do you see this concept of AGI and formulationally how that will be different that we would see what is your understanding about the concept of artificial intelligence and artificial intelligence
So, yeah, thank you very much for having us here. And, yeah, like you said, it’s a really nice topic to wrap up the conference. So, well, so, you know, of course, there are kind of different definitions of AGI. And on the same time, most of them agree that it’s, you know, it’s about smarter AI than we have right now. We were joking a bit that, you know, on the way, the traffic is really, you know, exceptional. And, yeah, that’s a sign that maybe we are still not here today. So, but, yeah, but basically kind of among those common agreements that, let’s say, the smarter AI should reason. It should learn. It should adapt. And also it should transfer knowledge.
And also it shouldn’t be, you know, very. narrow, like, you know, of course, right now we have great, let’s say, areas where AI is really helping a lot, like co -development, customer service, and et cetera, but, you know, it should be much broader. So, and, you know, don’t think that any of us, maybe the colleagues will be able to answer when we will have, you know, and what timing, but definitely, you know, that’s one of the big topics right now.
Let me come to you and you look at the digital initiative and artificial intelligence as one of the important research areas, so we are grappling with understanding what is right now, but can we think about what would happen in the next three, five years of time, and that seems to be the timeline for each area.
So I’m the one with the date I’ll do my best So first of all my definition of AGI is very simplistic and I think that we need some simple explanation in this field and my very simple explanation is AGI will be something that can perform every human task at the level of accuracy and professionality of a human professional Now this is not an optimal definition because people can ask every task if a baby is crying will the AGI help him stop crying and people can ask what is the level of professionality but I think that this is something that we can digest and I think that for me I understood that we are getting closer there not from a technology perspective but from the perspective of talking with real Israelis about their problems and five years ago when I was telling this definition of AGI people were like oh it’ll never happen not in our lifetime and right now when I’m speaking with Israelis and I’m telling them this is AGI they’re saying oh aren’t we there yet oh because I thought that Chachi Biddy can help me like a lawyer isn’t it true now I think that we are not there yet okay there is a very sharp line between the AI that we are experiencing today and true AGI but the fact that the audience is already confusing the fact that people give trust to Gen AI tools 50 % of Israelis trust them more than they trust their friends many trust them more than they trust human professionals this puts us closer to AGI so I would say that it’s a matter of 3 years to 7 years until we reach that milestone
so coming to you Alexandra how do you see this as a concept what is leading to this AGI what would we do that will impact the future of the AI bring this age of Asia in three or seven years of time?
Well, I’m not necessarily subscribing to the time frame. I think that depends on how much money we throw at it. And then there are other things to throw money at as well. Some of this, for example, we had a discussion with my team, you know, are machines able to make complex decisions as fast as humans? And in some areas, like, you know, many operations demand millisecond response and reflex level. You know, you can see that machines are quite good at detecting fire or doing various instinctive things as fast as we are, but the ability to interpret context, emotions, ambiguity, surroundings, body language, etc., that’s still quite far away. They take too long. And in a dynamic environment, you know, a wrong decision or a late decision is really a wrong decision.
So in order to get there, I, you know, there’s both low latency, energy efficient hardware, neuromorphic and edge computing and architectures beyond auto regression. But I think, you know, the researchers in Sintef, I head up the largest research institute in Norway. They, you know, they point to promising like hierarchical reflex reasoning systems, embodied multimodal learning, et cetera, et cetera. And there’s really no real doubt that you will get there. But there’s, in order to have the situational awareness like a human, you have to study a lot of data that would be considered private, personal. So there’s really limits on privacy. And then it triggers a lot of other questions that I’m sure we’ll get into.
Yeah, we’ll come to that. So, Mr. Kenney, you must be serving many of the clients right now on AI, right? And every of us are getting stunned by… the progress and acceleration of the capability that is happening week by week basically right and that also scares us what is coming next right and when it comes to that level where there is a there is a two words uh somebody defines agi right so one is the consistency across the domain uh that it will be so general in a way that it will be consistently performing across the domain and second part is uh it will be reliable as well so currently probably sometimes it doesn’t have anything and it throws output and that’s why hallucination happens basically so consistency and reliability that’s what the agi will bring to the table basically so it will solve a lot of problems that we see uh uh right now we have been also getting stunned by the things that it can do basically so so there are routes to achieve the agi which will lead us to agi basically so how do you think uh uh your perspective the the journey that probably take us there
So, you know, I agree with the panel that a couple of things we talked about in terms of where we’re getting to models evolving. But you bring up another component of accuracy. I’ll talk about accuracy first, and then I’ll come back to the disruption which is happening in the market. Now, the epitome of accuracy is five nines. So for AI to get from 90 % to 99%, it took five to ten years. Now, every nine that you add is another year or two years to the point where you get to 99 .99 and nines. So every nine that you’re adding has a time frame to it. And the number of nines that you add, you get closer to general intelligence because that’s what is going to look at the human brain.
I’ll take the topic of photographic regression that you talked about. Any regression, AI is right now built on regression. It’s built on learnings of the neural network. The neural network maturing on information that it sees. but the human brain is also inventing. It’s researching. So when AI really gets to the point of being able to research and bring new ideas to life that a human brain does, you’re getting closer to intelligence. Now, the disruption in the market that you’ve seen with announcements across the different players which dominate the AI market is creating a disruption in the industry and I think it’s the right disruption. It’s the disruption that word processor did to typewriter, what computers did to word processor, and what cloud did to data center.
This is another thing, but it’s much faster because it’s more pervasive and it impacts everybody in life. So the fact is people are talking about how does it translate to me. When I say it translates to me, it’s about how do we structure processes. Everybody and I agree accuracy is work in process. And since accuracy is work in process, we have to be really mature about… the use cases that we put onto it. We have to look at the human pyramid, what components of the pyramid that you’re going to look at. So the way we are advising our clients and what we’re doing ours is maker jobs, which is basically repetitive jobs with little context.
AI does very well, but create a controller for these autonomous. So combination of probabilistic and deterministic is what’s going to be the near future as we get to more and more deterministic when we get to general intelligence, because from a human perspective, it’s mostly deterministic.
Right. Yeah. So these are and thank you all for putting some level of clarity in terms of what this means. And so at the end of the day, Asia is like so they say attention, right? Ability to give attention to all possible thing that. People, millions and billions of people asking questions. but as you rightly say the context matters so it’s not only attention the it should be contextual to your requirement and your things that you do right and third important part of which they are doing and last six months had been a great months for reasoning that bring to the table basically so my question is and anybody of you can answer this you then for achieving all of these things so why compute becomes very important so why you need this much of compute why there are trillions of dollars that is invested to make sure that it it use attention to each and every problem better and it is contextual and you reasoning and at the same time latency as I talk about so the role of compete what is the role of competitive this any of you
yeah so you know so of course if I may start and of course please accompany so currently we are at super high cycle let’s say of those investments and most of us are also wondering is it a bubble or when it will blow a bit etc is it really in some cases sustainable everyone of us most likely has our own opinion but still this race to be number let’s say one this belief that if you are number one you will remain number one and this momentum I think plus huge appetite all this hype definitely brings much much more money to the table than we could ever imagine and you know on the same time it depends a lot of course on the algorithms how efficient they will be all of us remember most likely last year this deep sea moment and there are also other models which are much more efficient but so So, you know, at some point we might understand that it’s overestimated, overinvested.
At the same time, I remember in Zuckerberg’s quotes that, you know, said, okay, in the worst case scenario, I will, you know, have overcapacity for a couple more years and then I will use it.
So my humble opinion is that compute is one element in a chain of elements and that sometimes we treat this element as the only one. Let’s explore a metaphor. Let’s imagine that we are in the 19th century and a prophet arrives and he tells us, okay, in five years, a new technology will emerge that will enable you to arrive from Delhi to Bangkok in less than an hour. But I don’t know what the technology is. Maybe it’s a ship, maybe it’s a car, maybe it’s a train, maybe it’s an airplane, but we must be prepared. So everyone is trying to be prepared and to build the right infrastructure. So let’s look at the structure. The problem is everyone thinks about it as something else.
So one will build an airport and the other one will build rails and the other one will build boats. I think that we are in this moment. We know that AGI will arrive. We know that it is soon and we know that we must be prepared. Compute is one of the elements that is necessary, but energy is also important and heating and cold is also important. Data is extremely important. Implementation is important. Language is important in India as well. I think that one of the elements that we are not investing enough is the human element. Think about critical thinking, for example. I don’t know what AGI will arrive, but I know that already now for us it is very important to raise critical thinking among the public.
When you hear something in the news, when you see something, was it made by AI? What is the manipulation that is being forced upon me? So I think that investing in education is not less critical than investing in computing.
And then another element I want to come to you on this that you talked about. there is very interesting discussion about this system one and system two thinking human is more intuitive in terms of response and system two is more logical and AI is probably helping with that basically but there is a latency that is an important area and that’s why they are putting a lot of effort and improving the competence such that the latency of system two thinking is also less so that your intuitive thinking can improve with that basically but it’s not only the competence the perception, the ambient, the senses, the emotions so all that also matters a lot and that’s where the limitation of language based models are getting exposed basically and you did talk about that in your initial remark can you just throw light on that?
On the language? On the different type of the models right? Ambient, compute for that matter, world model that people talk about so…
Well I just wanted to first agree with the… Mir, sorry that you know if you are a government and this is democratic access to compute is a big topic I think you can really get lost in just investing in compute power so investing in skills and leading edge technology understanding in your own country and participating in the regulatory approach because some of the things that I care about is that everybody says that they should be human oversight but you know that once you get into these dilemma situations like what should happen in a car accident, humans are not very good at understanding risks and humans are not very good at really making ethical discussions they tend to go as far, you know, do your best and then let moral luck decide who gets lost but you have to in machine driven systems you actually have to make decisions about those things so I think becoming, you know, educating also our politicians to be able to to know that you have to make the hard choices because otherwise the machines will make them for you and they will continue our biases and they will, you know, it will not end well.
But then I just wanted to share a little story that I heard. You know, Michael Lewis, the guy with the money ball and everything, he has this anecdote that in the Basketball Association in the States, they started video surveillance and the coaches were all making racist decisions and home team decisions. And by showing the videos and by showing the statistics, next season they couldn’t find any bias at all. So I think that’s a good example of how the machines make people better, whereas we’re not able to better ourselves over time. So I think I just thought this was a nice anecdote for this
Thank you. And I’ll come to Kenny. So… As we are… trying to solve problems of security, privacy in current big capability of AI and we are struggling to understand what it means for security, what it means for privacy and suddenly there is a significant acceleration that is happening so what we are doing right now for security privacy which could help us to graduate to more and more powerful model comes in or any other things basically so can you just help us
yeah I think security as we evolve and we talked about compute compute gets bigger, context get bigger, we get smarter in terms of what AI can do and definitely the same AI that can generate, can pose more sophisticated attacks and when we get to AGI right, the biggest thing is I could be emulating a human Let’s say in a company, I could emulate a CEO and make a decision because I’m getting so close to being natural. The threat is real. Now, even today, let’s say without AI, you need to be just a step ahead of the bad actors or the persons who are into cybercrime. You just have to be a step ahead. And similarly, we talked about, you know, we’re mentioning about the human portion, right?
That the human portion needs to get more educated where there are going to be set of humans that are going to use the same AI to build better agents to fight them. So now it’s a question of the tooling that you have at hand. Even today, it’s the tools. It’s a human who’s building tools to fight your cyber threats. Imagine, in the next era, the only thing is… It’ll become nearly close to science fiction when agents try locking humans out. But that’s, I would say, still science fiction. But the fact is as we evolve, we need to right -size the solution and that’s how we will manage compute too. You don’t use I7 computer or to do a simple calculator task of adding two numbers, right?
You use a calculator. So in the context of the world, we’re going to have SLMs which is small language models that will do smaller things so that we can manage compute. You have the bigger models that will solve world hunger in terms of how we do with different levels of machines and processing that we do. I think there will be tiering. Right now, we were talking about it’s a fight to who’s first. So with the fight to first, bigger, better, more elaborate. But now as it evolves, you’ll get the right size fitting to them. Then only it will be commercially viable. AI is not commercially viable today. The costs outweigh the RO.
Yeah, current cost is quite significantly higher. You can do POC but… once you put into production environment the token cost is too much high to the ROI so so near want to come to you there is a established understanding of security privacy safety or ethics right and that’s what the paradigm that we at least try to understand right now but would the Asia altogether different paradigm and the concepts of security privacy will be foundationally very different than what we discussed right now
so as I see it when we try to deal with the risks that AI pose we distinguish between four different levels the first level is the classical risks like privacy security cyber fraud every technology that we have since the 90s we need to explain how does it meet the current risk in that matter and AI is much more powerful and it poses a lot of more risks but these are the kinds of risks that we when we design products we know how to deal with them. Above it there is a level of human health and mental health and we find out that AI solutions can be quite problematic for mental health, can cause a lot of damage in some cases and this is something that is not yet well understood and investigated above that there is a social level.
What does it does to the empathy between people? What does it does normally people say oh I see that it’s bad for my kids. They are experiencing bullying or addiction usually what’s bad for your kids is also bad for you and we understand that these are complications that we didn’t think about when we code and the higher level is a macro level what does it do to society? What does it do to democracy? I think that several countries are now experiencing foreign manipulation and it is very easy to run campaigns that are built of fake news and we see that manipulation can become very problematic. So I think that a strategy, a national strategy and an international strategy should access, should address all these levels and all these levels have mitigations but they are costly and they need collaboration.
So we need to be in close collaboration in order to mitigate these risks.
It’s good that the way you put the structure, right? Things it would do to us, our brain and the thing that will impact us as individually and we discussed that in one of the sessions that we hosted on neuroscience and AI. So what this means to the brain development process if we are using AI for every small thing that we want to do, what that means to society, brain development process plateaus for that matter, what will be in society and then what is the macro kind of impact it. Do you want to add something on that?
Yeah, I just, sorry. I just want to build on that. How it’s not just targeted manipulation or the things that we see in our kids and somebody walking around with a button called friend and that’s your only friend that you need but also the well -structured in the geopolitical context the ability to create completely different information universes you don’t need to be neurologically strange you just see a completely different view we just published a paper in science on these agent swarms and just reading a book about the Ukraine and Russia war going on now and how large populations are overpowered by totally different images of the world from what we are and at least obviously your defense systems need to be hardened against those kinds of manipulations but it’s also you know actually an offensive strategy to find good bots that enter those universes.
It’s an actual battleground in and of itself, and it’s very strange to think about the world in that way, but I think you’re very naive if you don’t start systematically working on how you make your conviction of what the world is like also part of the people that you need to somehow, hopefully not defeat, but relate to and convince that things can be better. So it’s not just a technological challenge. I would say it’s a huge mental leap for most of us.
So Siman, the question is like the more we use, the more we become dependent on AI system, right? And the more acceleration of the people’s ability to think critically, that will go down basically, right? The speed will increase the more dependence, and then more… More AI become powerful for that matter, right? so what we see in terms of this misinformation, disinformation and defake, so probably there will be different kind of cognitive warfare that may happen so how do you see such kind of challenges in the society, you talked about society or individual for that matter, so what kind of implication it will have for individual society and overall the way the world is organized
yeah so absolutely so basically all those layers and all the dependencies like you rightly stated they also critical thinking of course is one but also awareness, education and you know the skills, abilities for people to understand the things here I think this audience is you know for us it’s more or less everything self obvious but you know when you start talking to people in the streets or different backgrounds then you you know realize that what is self -obvious for you for another person might be completely different. To find those ways I would say to educate to basically help them identify the threats, that’s one of the key priorities and also obligation I would say from our side.
one of the important challenge of this critical thinking which I come across is critical thinking is nothing but your ability to give attention to various different dimensions nuances, different perspective, different views basically right. Where it is tremendous amount of effort that I would have to become a critical thinker. And AI saws that quite easily for me. It can make me to bring all the attention, all the dimensions, all the nuances, all the viewpoints, you can quickly get access to me, right? So, even for critical thinking, Kenny, for you, this question is, you will be depending too much on AI as well, right? So, we need to know distinction between what do you critical thinking? Critical thinking is not just getting information, giving attention, but critical thinking is what?
So, that question probably is very important question to ask.
critical thinking that is very necessary for us to innovate further. So the biggest issue that the AI world is facing, 30 % of the content is consuming is AI generated already. So basically you’re feeding back and it’s learning on the same model. When originally it was learning on artifacts that were built through different thinking processes. So I would say one of the, it’s a risk, it’s a boon because it gets work done. But in overtime it’s a risk that we will stop evolving because if we don’t exercise the brain as a muscle, if we don’t exercise it and don’t build those neurons which really influence critical thinking, it will be actually a very big loss to society.
So I would say general intelligence, everybody is asking for it. Now how do we make sure as AI. computers get general intelligence we’re not losing our intelligence to create that general intelligence again so it’s a it’s a it’s a vicious cycle it’s a question which we’re debating we’re trying to answer in ourselves everybody has perspectives but it’s a it’s something that I think about do I have an answer to it no but I feel that critical thinking on both sides is something that we really need to critically think about
yeah so that’s what may every thing that you think as a solution and kind of thing so there is always this challenge of what it means right in this new paradigm is an important so now a little bit concluding part of this discussion is can we when this is question to each of you briefly we can discuss about it can we still think about I know we know we have been doing security privacy and particular safety privacy particular way right but as this paradigm is new can we think about some anchor control right now that we should be mindful of right that when it comes it happened right when AI was getting built after 3 years we are talking about AI governance and all these things so is there a way for us to think about some kind of anchor control some idea some concept basically that could help us to browse through challenges the AGI could throw I can start with you briefly and each of you can comment on this
yeah so well of course you know there are some technical things like you know the same what are marks or something you know labeling and other technical features that could help us a bit to identify at least some threats … then also we can talk about regulator measures but you know that’s a broader topic for the further discussion but especially here we in Europe we tend to regulate and overregulate everything so but in a way I think also at least some measures here also can be really viable and really reasonable
well I come from a very small country Israel is so small that you can put it it’s like a pin on the map and therefore our regulative approach is that we are unable to determine the global regulation and in this AI race I think that what is more important is the global regulation so since we are a very tiny country we must work with positive tools say, okay, we cannot affect the regulation, but how can we work together with the AI developers in order to make the personality of the AI more moral, more ethic? How can we put egalitarian and equality into the consideration? How can we avoid bias? And I think that it makes us work together with the industry and together with the academia in order to find out about new consequences.
I think that in many cases the giants, the big tech doesn’t point towards unethical conclusions, but they work towards financial incentives that make AI behave in a very immoral way. If I’ll take, for example, the conflict in Myanmar, in Burma, we saw that Meta was not actively promoting violence in Myanmar, but the algorithm of Meta was designed to attract attention in a way that make the AI the more violent post much more viral and make violence flourish. So if we’ll be able to promote a dialogue and if we’ll be able to be together with the industry in development of new AI, sometimes we’ll be able to make AI more ethical.
So Alexandra, your view. So one is the anchor control idea concept, but second part is how do you get into early? How do you get into? Early in the game, right? So when AI happened, now we are discussing in 25, 26 about the responsibility and alignment and adoption and governance basically, right? So in Asia discussion is the anchor control ways, ideas and ways for us to get into early discussion of it.
Well, I think at least you need to work on resilience and robust rollback mechanisms. A little bit like what we’re experiencing now in Europe, where we all have to practice on living without electricity. You know that it’s a realistic option that somebody. sabotages your electricity and then looking at well how dependent are we really and what are the alternative you know and and planning from a point of view where you not only work to reduce risk but you really work to reduce consequences of those risks occurring so if you work on the traditional risk matrix it’s always you know avoiding bad outcomes but then making the bad outcomes less bad that’s something that at least we think is well the new realities are propelling that kind of thinking and I think that’s important
Kenny your voice on this
sure actually the way we look at it in terms of AI from ethical AI to biases to data privacy it’s very similar akin to what a human would do even today what today we have a standard operating procedure that we review for biases, we review for content. You know, in our organizations, we have organizations that manage this. Now, and the other thing is we train people on ethical practices, on non -bias and things like that. So ultimately, AI is very similar to that, where we will have, you know, in today’s world, for the lack of a better word, I call it AOP instead of SOP, agent operating procedure or AI operating procedure, where we have to train AI in terms not to be biased.
So I feel that there is a big industry which is in the offing, which is going to manage and create models, LLMs, to manage or to validate that the responses from, you know, your common models are ethically right, non -biased. Because today, as organizations, we invite experts from outside to come and see our practices, whether we are following ethical, we are transparent, a number of those things. Very similarly as we mature towards more general intelligence and the more ways of working, I feel that these control structures will come in cyber security, will come in ethical use of AI, unbiased use of AI. So ultimately it will be a checks and balances system and we will see innovation in these areas.
That is how we feel it. It’s an evolving area. Let’s see how it happens.
Thank you all of you to really help us understand the meaning of this concept of AGI and how that will pan out from now and what kind of challenges it will throw to us. There are definitely opportunities that we don’t have time to discuss about what it will bring to us. But then what could we start doing right now? And this was definitely one of the important conversations. Help this would help you understand what we are talking about the AGI today. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Join me to give big hand to my co -panelists for helping us understand. Thank you. Thank you, Simon. Thank you, Nir.
Thank you. We have some photo shoot. Alexandra, we need to come here for photo shoot. I also request the fireside panels, Hendrikus sir and Narendra sir to please join us for the photo shoot. Thank you. Thank you. Before we commence the session for the Fireside I would like to announce the launch I would like to announce the launch of AI Cyber Security Terminal This is published today Thank you. Thank you. you you Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you.
“Roughly 50 % of Israelis said they trust generative‑AI tools more than friends.”
A poll cited in the knowledge base shows that 50 % of respondents consider trust foundational for long-term success of transformative technologies, matching the reported figure [S70].
“A three‑to‑seven‑year horizon is projected for AGI to perform any professional human task with comparable accuracy.”
Other sources note that many experts expect AGI development to take about five years, and some leaders explicitly favor slower timelines, providing nuance to the 3-7 year estimate [S24] and [S52].
“The industry may be heading toward an “over‑capacity” situation for a couple of years, with excess compute resources.”
Discussion about a global compute divide highlights that regions lacking compute fall further behind, underscoring concerns about mismatched capacity and potential over-supply in well-resourced areas [S77].
The panel shows strong convergence on four core themes: (1) the necessity of early, multi‑layered governance and anchor controls; (2) a shared risk taxonomy spanning privacy, security, health, social and macro dimensions; (3) acknowledgement that compute is vital but must be complemented by energy, data, hardware, and human skills; and (4) concern that AI over‑reliance could erode human critical thinking. These agreements cut across AI technical development, security, human rights, and broader socio‑economic impacts.
High consensus – most speakers articulate overlapping viewpoints on governance, risk management, and the broader ecosystem needed for safe AGI development. The alignment suggests that future policy and research agendas can build on these common foundations, though divergence remains on precise timelines and the scale of investment.
The panel shows broad consensus that AGI will pose significant societal, security, and ethical challenges and that proactive governance is essential. However, there are clear disagreements on the expected timeline for AGI, the primacy of compute versus a multi‑resource approach, the specific form of early anchor controls, and how to balance privacy with data needs. These divergences reflect differing strategic priorities (short‑term optimism vs. cautious uncertainty) and disciplinary lenses (technical, regulatory, societal resilience).
Moderate to high. While all participants agree on the need for action, the lack of alignment on timelines, resource prioritisation, and concrete governance mechanisms could hinder coordinated policy responses and lead to fragmented national strategies.
The discussion evolved from a broad, introductory framing of AGI to a nuanced, multi‑dimensional analysis thanks to several pivotal remarks. Definitions anchored in public trust, quantitative accuracy metrics, and a layered risk taxonomy gave the conversation concrete footing. Counterbalancing perspectives—such as the hardware‑centric view versus the human‑capital emphasis, and the optimistic bias‑reduction anecdote versus the stark security‑emulation scenario—created a dynamic tension that pushed participants to explore both opportunities and threats. Economic considerations about investment cycles added realism, while the final focus on resilience and rollback mechanisms translated the debate into actionable governance concepts. Collectively, these thought‑provoking comments shaped a rich dialogue that moved from abstract speculation to concrete policy and societal implications for the impending era of AGI.
Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.
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