Fireside Conversation: 02
19 Feb 2026 12:00h - 12:30h
Fireside Conversation: 02
Summary
The panel, moderated by Maria Shakil, featured Yann LeCun discussing AI’s future role as an amplifier of human intelligence rather than a replacement, noting that AI will likely create tools that boost progress without necessarily surpassing human intellect in all domains [10-13][14-16]. He emphasized that the most interesting outcome will be an “amplifier for human intelligence,” enabling faster advancement while keeping humans at the center of decision-making [16-17].
LeCun clarified that large language models (LLMs) are powerful information-retrieval systems that compress existing knowledge but function mainly as advanced search tools, comparable to a modern printing press [27-34][35-38]. While they excel at tasks such as code generation, they lack true world models that allow flexible, anticipatory interaction with physical environments-a gap evident in the current inability of AI to learn driving after minimal practice, unlike humans or animals that build mental models through observation and interaction [39-42].
Economists estimate AI will raise productivity by about 0.6 % per year, a modest yet significant boost that could accelerate scientific and medical advances, though the distribution of benefits remains a political question and should not be conflated with immediate economic transformation [45-51][52-56]. LeCun warned that the promise of radical abundance must be managed through policy to ensure inclusive gains [52-56].
Looking ahead, LeCun argued that the next wave of AI talent will come from youthful regions such as India and Africa, and that higher education-especially PhD-level training-will become even more essential to meet industry demand [90-98][99-105]. Making AI accessible to a nation of 1.4 billion people requires a dramatic reduction in inference costs, which are currently dominated by energy expenses [108-112]. He illustrated practical AI applications, such as smart-glass assistants for Indian farmers that diagnose crop diseases and advise on harvesting, showing how AI can improve agriculture and education once costs fall [118-120][114-117].
LeCun described the human-AI relationship as analogous to a manager-staff dynamic, where AI acts as a highly capable assistant that may be smarter than its user yet serves human goals [75-81][82-84][85-86]. He cautioned that past hype has repeatedly overestimated the speed of achieving human-level AI, noting that predictions of a breakthrough within a decade have been wrong for decades and that progress will be incremental rather than a single event [149-157][158-162].
Consequently, defining intelligence will remain a human-driven task, with humans setting agendas and avoiding the illusion that language ability alone signals true intelligence; the real challenge is building systems that handle the messy, continuous real world, a problem LeCun’s current research aims to solve [168-176][177-179]. He concluded that, while the societal impact of AI is hard to predict, it is comparable to the transformative effect of the printing press, and he remains optimistic that societies will harness the technology for broad benefit [142-146][181-182].
Keypoints
Major discussion points
– AI as an intelligence-amplifier rather than a replacement – LeCun stresses that the most valuable AI we will build is a tool that augments human thinking, not necessarily a fully autonomous super-intelligence. [14-17]
– Current limits of large language models and the need for world-models – LLMs excel at compressing and retrieving factual knowledge but lack the embodied, predictive “world models” that allow animals or humans to act in novel situations. [27-39][41-42]
– Economic impact and the question of shared abundance – Economists estimate AI will raise productivity only modestly (≈0.6 % / yr), and whether the gains translate into broad prosperity depends on political choices, not technology alone. [45-56]
– Education, talent development, and democratizing AI for the Global South – LeCun argues AI will become a “staff” for humans, requiring massive up-skilling, more PhD-level scientists, and lower inference costs to make the technology accessible in populous regions like India and Africa. [75-85][90-105][108-115]
– Gradual progress, over-hyped timelines, and the real-world challenge (Moravec paradox) – He rejects the notion of a single breakthrough event, warns that past hype cycles have repeatedly over-promised, and highlights the difficulty of building systems that handle high-dimensional, noisy real-world data. [58-66][149-165][168-178]
Overall purpose / goal of the discussion
The conversation, introduced by Speaker 1 and framed by the moderator’s opening question about creating “the smartest mind” [1-8][10-13], aims to clarify how AI is expected to evolve, what its realistic capabilities and limitations are, and how societies-particularly emerging economies-should prepare through education, policy, and inclusive innovation.
Tone of the discussion
– Opening – upbeat and celebratory, highlighting LeCun’s stature and the excitement around AI [1-5].
– Middle – becomes more analytical and measured as LeCun explains technical constraints of current models and the modest economic gains [14-22][27-39].
– Later – shifts to an optimistic yet pragmatic stance on education, talent pipelines, and global participation [75-85][90-105].
– Closing – adopts a cautious, realistic tone, warning against hype and emphasizing the long-term, incremental nature of progress and the need to tackle real-world complexity [149-165][168-178].
Overall, the dialogue moves from enthusiasm to nuanced reflection, ending on a hopeful but grounded outlook.
Speakers
– Yann LeCun – Executive Chairman, Advanced Machine Intelligence Labs; pioneer of deep learning, convolutional neural networks, and world-model AI research. [S3][S1]
– Maria Shakil – Managing Editor, India Today; served as moderator for the conversation. [S4]
– Speaker 1 – Event host/moderator who introduced the session and the guests; specific title not provided. [S6]
Additional speakers:
– None identified (no other individuals spoke in the transcript beyond the three listed above).
Speaker 1 opened the session by thanking Mr Brad Smith for his energising address and noting that his remarks had given a constructive direction to the AI discourse. He then introduced the next guest – Professor Yann LeCun, described as “the godfather of deep learning” whose work on convolutional neural networks underpins virtually every modern image-recognition system and who is now a provocative, independent voice at the frontier of next-generation AI architectures. The moderator, Ms Maria Shakil, was announced to lead the conversation [1-9].
Ms Shakil began by asking whether humanity is on a path to create “the smartest mind that humanity has ever known” and whether such a breakthrough might occur within our lifetimes [10-13]. Professor LeCun replied that while a few participants might live to see it, it is unlikely to happen in his own lifetime and that the more interesting outcome will be an “amplifier for human intelligence” that accelerates progress without necessarily producing an entity that surpasses human intelligence in every domain [14-17].
When pressed about the evolving notion of genius, LeCun traced the concept back several millennia, observing that earlier societies regarded practical innovators – such as those who domesticated crops or animals – as geniuses, whereas today genius is more often linked to theoretical creation and invention [20-24]. This historical shift underlines his view that AI should augment, rather than replace, human creative capacity.
Addressing the distinction between AI’s power and its intelligence, LeCun warned against anthropomorphising systems that mimic human functions. He described large language models (LLMs) as “incredibly useful” but essentially sophisticated information-retrieval tools that compress previously produced factual knowledge and provide rapid access, likening them to a modern evolution of the printing press, libraries, the Internet and search engines [27-34]. Although LLMs can exceed simple retrieval in domains such as code generation and mathematics, they remain largely symbolic systems that lack the ability to reason about the physical world in the way humans do; LLMs don’t do this, really [35-38][39-42] [39-42].
LeCun highlighted the gap by contrasting the ease with which a teenager can learn to drive after only a few dozen hours of practice with the current inability of AI-driven robots or self-driving cars to acquire comparable skills despite massive datasets. He explained that babies and animals learn through observation and interaction, building mental “world models” that enable them to handle novel situations; this capacity is missing from today’s AI, which does not yet possess robust world-model reasoning [39-42] [41-42]. He noted that this limitation is encapsulated in the Moravec paradox – “It’s called the Moravec paradox after roboticist Hans Moravec” [41-42].
On the economic front, LeCun cited economists such as Philippe Ackermann and Erik Brynjolfsson who estimate that AI will raise productivity by roughly ≈ 0.6 % per year – a modest but non-trivial boost that can accelerate scientific and medical progress. He stressed that there will be no single “boom” moment of abundance; instead, the benefits will accrue gradually, and the crucial question of whether those gains are shared equitably is a political, not a technical, issue [45-51][52-56][S1].
The moderator asked whether openness in AI development could survive as the economy expands. LeCun responded that AI progress will be continuous rather than a sudden breakthrough, rejecting the notion of a single “secret” to human-level intelligence and dismissing the term “AGI” as misleading because human intelligence is highly specialised. He argued that intelligence should be measured by the ability to learn new skills rapidly and to perform unseen tasks, not by a static suite of benchmark tests [58-66][S4].
LeCun then turned to the implications for talent and education, describing a future in which every individual becomes a manager of intelligent AI “staff”. Yann LeCun explained that “AI is going to be our staff… Every one of us is going to be a manager of a staff of intelligent machines” [75-84]. He noted that AI systems may be smarter than their users, just as academics rely on exceptionally bright students and politicians on savvy advisors. Consequently, massive up-skilling and reskilling will be required, with a growing demand for PhD-level scientists to drive scientific progress – a demand already evident in industry across India, Europe and the United States over the past fifteen years [85-86][90-105]. He added that we are over-estimating short-term impacts and under-estimating long-term ones, a pattern that has repeated throughout AI history [85-86].
Regarding the feasibility of deploying AI at the scale of India’s 1.4 billion population, LeCun pointed out that the current cost of inference – dominated by energy consumption – is prohibitive. He argued that only a dramatic reduction in inference costs will make AI practical for the vast majority of users, after which it can improve education, agriculture and healthcare. He illustrated this with a pilot in which smart glasses equipped Indian farmers with an AI assistant capable of diagnosing crop diseases, advising on harvest timing and providing weather forecasts [108-112][114-117][118-120].
When asked whether AI would make students more literate or merely dependent, LeCun acknowledged that humans have always depended on technology, but asserted that AI will act as a tool that expands access to knowledge, much like the printing press and the Internet did in earlier eras. He suggested that, if deployed responsibly, AI could raise overall literacy and enable more rational decision-making [125-132][133-136].
The discussion concluded with LeCun likening the present AI revolution to the invention of the printing press rather than to electricity, noting that while the societal impact will be transformative, its exact shape is difficult to predict. He expressed optimism that societies will eventually figure out how best to harness the technology for the benefit of their populations, adding that the biggest difficulty is not to be fooled by language [142-146][181-182] [181-182].
In sum, the discussion highlighted AI as an intelligence-amplifying tool, the current limits of LLMs, modest economic gains contingent on policy, the need for massive up-skilling (especially in the Global South), and the importance of reducing inference costs to realise AI’s societal benefits.
Thank you so much, Mr. Brad Smith, for that very energizing address, ladies and gentlemen. I think he really deserves an energetic applause from you all. His address has actually given a very constructive direction to the discourse on artificial intelligence. And well, now we are moving to the next conversation for which our guest is the person who’s often called the godfather of deep learning. Our guest is Mr. Yann LeCun, Executive Chairman, Advanced Machine Intelligence Labs. And his foundational work on convolutional neural networks underpins virtually every image recognition system in use today. Now at the frontier of next generation AI architectures, he’s one of the field’s most provocative and independent voices. Please welcome our next speaker, Mr.
Yann LeCun, and this conversation will be moderated by Ms. Maria Shakil, Managing Editor, India Today. Please welcome our guest and our moderator.
Mr. Yann LeCun. Welcome. Good afternoon, everyone. So let’s begin with a big idea here, Professor LeCun. Are we on a path to creating the smartest mind that humanity has ever known? And will that happen in our lifetime?
Maybe in the lifetime of some people here, possibly not in mine. We’ll see. It will take a while. But I think the more interesting… thing that we’re going to build is an amplifier for human intelligence. So maybe not an entity that surpasses human intelligence in all domain, although that will happen at some point, but it is something that will amplify human intelligence in ways that will accelerate progress.
So then will we end up defining and redefining genius? What will a genius be?
Well, you know, I think several thousand years ago, or even a few centuries ago, what people identified as genius is very different from what we currently identify as genius. And I think there will be more evolution of that concept of genius. You know, in the past, perhaps, you know, genius was, you know, some act of creation or invention, but maybe not at theoretical level like we are. We tend to think of it today. It was, you know, more practical, certainly in the very ancient past, people who figured out how to cultivate crops or domesticate animals probably were seen as genius.
So, you know, we have often seen, and this is a thought that you have all, you know, pretty openly shared, that AI is powerful but not intelligent. When we make that distinction and there are conversations around LLM, where do you see intelligence and AI -driven power?
Yeah, I think there’s a lot of confusion, really, because we tend to anthropomorphize systems that can reproduce certain human functions. So what’s, I mean, LLMs are incredibly useful. There’s no question about that. And they do amplify human intelligence, like computer technology going back to the 1940s. But LLMs, to some extent, except for a few domains, are mostly information retrieval systems. They can compress a lot of factual knowledge that has been previously produced by humans and can give easy access to it. In a way, it’s kind of a natural evolution of the printing press, the libraries, the Internet, and search engines, right? It’s just a more efficient way to access information. And there are a few domains where the intelligent capabilities of those systems actually is more than that.
It’s more than just retrieval. So for generating code, maybe doing some type of mathematics, we’re getting the impression that it’s beyond this. But it’s still, to a large extent, domains where reasoning has to do with manipulating symbols. The problem is that… you know why do we have systems that can pass the bar exam and win uh mathematics olympiads but we don’t have domestic robots we don’t even have self -driving cars and we certainly do not have self -driving cars that can teach themselves to drive in 20 hours of practice like any 17 year old so we’re missing something big still
so what are we teaching a 17 year old then
well so the you know the question is how does how does a baby learn uh or even an animal right animals have a much better understanding of the physical world than any ai systems that we have today which is why uh you know we don’t have smart robots um and and so you know we learn we learned about about the world how the world works mostly by observation when we are babies a few months old and then we learn by interaction and we learn mental models of the world that allows us to apprehend any new situation even if we haven’t been uh you know exposed to it beforehand we can still handle it so a big buzzword in ai today is world models and this is really this idea that we we We develop mental models of the world that allow us to think ahead, to apprehend new situations, plan sequence of actions, reason, and predict the consequences of our actions, which is absolutely critical.
And LLMs don’t do this, really.
There is the sense, Professor, that perhaps AI will unlock an era of radical abundance. Will this abundance benefit us?
Well, if you talk to economists, they tell you, if we can measure the improvement that AI will bring to productivity, which is the amount of wealth produced per hour worked, it’s going to add up to maybe 0 .6 % per year. This is from economists that actually have studied the effect of technological revolutions on the labor market and the economy. People like Philippe Ackermann. Like Jung or Eric Brynjolfsson. And so that seems small. It’s actually quite big. And, you know, it’s certainly going to accelerate scientific progress, progress in medicine. I do not believe there’s going to be a singular identifiable point where the economy is going to take off and there’s going to be abundance. And there’s also the question of the policies surrounding this.
Are those benefits going to be shared across humanity or different categories of people in various countries? That’s a political question. It has nothing to do with technology.
So if economists see this as boom, will then openness survive?
It’s not going to be an event. It’s going to be progressive. There is this false idea that somehow at some point we’re going to discover the secret of human level intelligence. I don’t like the phrase AGI because human intelligence is specialized. So I don’t like the artificial general intelligence phrase. But it’s not going to be an event. We’re not going to discover one secret. We’re going to make… continuous progress. And we’re not going to be able to measure that progress by just having a series of tests that are going to test, you know, whether a machine is more intelligent than humans, because machines are already more intelligent than humans on a large number, a growing number of narrow tasks.
And so, you know, it’s not like a uniform, you know, scalar measurement of quality. It’s a collection of quality. But what’s more important is that intelligence is not just a collection of skills. It’s an ability to learn new skills extremely quickly, and even to accomplish new tasks without being trained to do it the first time we apprehend it. That’s really what, you know, intelligence should be measured at. So we’re not going to be able to just design a test that is going to figure out, you know, are machines more intelligent than humans.
So if it’s about upskilling and ensuring that you’re relevant, then only perhaps you’re intelligent. Will that then mean that the countries that adopt AI and the pace at which India and the scale at which India has adopted AI, the challenge would be to create talent which is upskilled and reskilled and have the required skills for this?
Absolutely. So the relationship that we’re going to have with intelligent AI systems is going to be similar to the relationship that a leader in business politics or academia or some other domain has with their staff. AI is going to be our staff. Every one of us is going to be a manager of a staff of intelligent machines. They’ll do our bidding. They might be smarter than us. But certainly if you are an academic or a politician, you work with staff that are smarter than you. In fact, that’s the whole point. attract people who are smarter than you because that’s what makes you more productive. For an academic, it’s students who are smarter than their professor.
It’s not the professor that teaches graduate students. It’s the other way around, actually. And certainly, we have a lot of examples of politicians who are surrounded by people who are smarter than them.
Earlier today, when Prime Minister Modi addressed the gathering, he said that India doesn’t fear AI. We are seeing this as our destiny future, which is Bhagya. Do you see that with a summit of this nature being hosted in India, it’s a message to the global south? And that’s where perhaps the next big innovation in AI could be coming from?
Well, long term, it’s going to come from countries that have, for example, favorable demographics. And that means India, Africa. You know, the youth is the most creative part of humanity and there’s sort of a deficit of that in the North, largely. So, you know, the scientists, the top scientists of the future, in fact, many of the present are from India and in the future will be from mostly Africa. So what does that mean, though? Right. It means having incentives for young people to kind of study, first of all. So the idea that somehow we don’t need to study anymore because AI is going to do it for us. And, you know, that’s completely false, absolutely completely false.
And it’s not because I’m a professor, OK, that I’m saying this. On the contrary, we’re going to have to study more. We see, for example, a trend. Where in industry, in the past, in certain countries, it’s certainly true for India, but it’s also true in European countries. And certainly in the U .S., we see. more demand for people with more education at the PhD level, for example. The demand for PhD -level scientists in industry has grown in the last 15 years, in part because of AI, but because of everything, because technological progress hinges on scientific progress, and scientific progress is brought about by scientists, and scientists mostly have done PhDs. And so there is more demand for education, not less.
And so for countries in the Global South, that means investing in education and youth.
And making AI more accessible, something that India believes in, democratizing AI, AI for all, is the theme of this summit as well. Do you think AI can become that accessible, particularly for a… country as large as ours with 1 .4 billion people?
Yeah, in all kinds of ways. Unfortunately, the cost of inference for AI system has to come down to kind of become practical for the vast majority of population in a country like India. Right now, the inference is just too expensive. And, you know, energy costs and things like that. It’s mostly energy costs, actually. So this has to come down, but then it will play a role in education. AI will improve the quality of education, not degrade it. Once we figure out how to use it best, it will improve agriculture and everything else. And healthcare in particular. Healthcare, of course, right? So I don’t work at Meta anymore, but there was an experiment a couple of years ago or a year ago that was run by my former colleagues where they gave smart glasses to people.
Agriculture, you know, to farmers in India. And they could talk to the AI assistant to figure out, like, you know, what is this disease on my plant or should I harvest now or what’s going to be the weather?
Yes, it is being used a lot in agriculture as well. That’s right. It is assisting farmers to ensure that their produce gets better. They make right choices. But when you say about education, will AI assist education in terms of making students or youth of the country more literate or will they become more AI dependent?
Well, I mean, we’re dependent on technology, right? I’m dependent on this pair of glasses. Otherwise, I don’t see you. So that has been with us for centuries. Yeah, we’ll be dependent on AI, of course. But AI will facilitate. Access to knowledge and thereby going to be a tool for education. I think the effect on society. might be extrapolated from what was observed in the 15th century when the printing press started enabling the production of printed matters and the dissemination of knowledge. It had a huge effect on society worldwide, at least in countries that allowed it to flourish. And I think it’s going to be a similar transformation with AI, of course, in the modern world, just more access to knowledge.
The Internet played also a similar role. And I think this is just going to make people more informed, smarter, able to make more rational decisions if it’s deployed in the proper way.
So if you were to define this moment, which we are witnessing in history, we are living it, how will you say it? Is it like the advent of electricity?
Yeah, people have made that claim. They have made that claim, yes.
Including?
The printing economy is the new electricity. I think it’s more like the new printing press, really. Again, in this vision of more dissemination and sharing of knowledge and amplification of human intelligence. But the impact on society and the way countries need to be run is very difficult to predict at this point. I’m sort of an optimist in the sense that I think societies would figure out how best to use that technology for the benefits of their population.
While I am an optimist, nevertheless, I’m going to ask this question to you, Professor. Are we overestimating the change or underestimating what has struck us?
So, usually in technological shifts of this type, we are overestimating. And the changes in the short term and overestimating them in the long term. Now, I think for AI, it’s a little bit different because there’s been a huge amount of hype and expectations that, you know, the transition to human -level AI, superhuman -level AI is going to be an event and is going to happen within the next few years. And people have been making that claim for the last 15 years, and it’s been false. In fact, they’ve been making it for the last 60 years or 70 years, and it’s been false. Every time in the history of AI that scientists have discovered kind of a new paradigm of AI, how you build intelligent machines, people have claimed, you know, within 10 years, the smartest entity on the planet will be a computer.
And that just proved to be wrong, you know, four or five times in the last 70 years. It’s still wrong. We’re still very far from that. We’re not very far. We’re getting close, right? We’re seeing the end of the tunnel. But it’s not like, you know, we’re going to have. Super intelligent systems within two years. It’s just not happening because of this gap. You know, where is the robot that can learn to drive? 20 hours of practice like a 17 -year -old, even though we have millions of hours of training data of people driving cars around, we should be able to train an AI system to just imitate that. That doesn’t actually quite work. It’s not reliable enough.
Okay, so let’s try and wrap up this conversation with who gets to define intelligence now onwards. Will it be actually humans, machines, or both together?
Probably both together, but mostly humans. We set the agenda, and the biggest difficulty is not to get fooled into thinking that a computer system is intelligent simply because it can manipulate language. We tend to think of language as the epitome of human intelligence, right? But in fact, it turns out language is easy to deal with because language is really a sequence of discrete symbols of which there is only a finite number. And that turns out to make things easy when you train a system to predict what the next word is in a text, which is what LLMs are based on. It turns out the real world is much, much more complicated. And it’s been known in computer science for many years.
It’s called the Moravec paradox after roboticist Hans Moravec. And so the company I’m building and the research program I’ve been working on for the last 15 years or so is intelligence for the real world. You know, how to deal with high -dimensional, continuous, noisy signal that the real world is, which your house cat is perfectly able to deal with or a squirrel or whatever, but not computers yet. That’s the big challenge for the next few years in AI, dealing with the real world. And that’s the point of the company I’m building.
So AI has to deal with the real world or real world has to deal with AI.
AI has to deal with the real world, the messiness of the real world, the unpredictability of the real world.
All right. Thank you so much for this conversation, Professor. Thank you, Mr. Yeltsin.
“Yann LeCun said it is unlikely that human‑level AI will be achieved in his lifetime, estimating that such capabilities are at least a decade away.”
LeCun has been quoted as saying that achieving human-level AI may be at least a decade away and that current systems fall short of true reasoning, memory, and planning [S100].
“LeCun described large language models as sophisticated information‑retrieval tools that lack true reasoning about the physical world.”
The knowledge base notes that LLMs fall short of genuine reasoning, memory, and planning, supporting the characterization of them as primarily retrieval-oriented systems [S100].
The discussion shows a clear convergence among the moderator and the AI expert on several key themes: the political nature of AI‑generated wealth distribution, the necessity of reducing inference costs for mass accessibility, the historical tendency to over‑estimate AI breakthroughs, the role of AI as an intelligence amplifier, and the need for widespread up‑skilling as AI becomes a managerial staff for everyone.
Moderate to high consensus – the speakers largely agree on the challenges (cost, policy, skills) and on a realistic, incremental view of AI’s impact, suggesting that policy‑makers and technologists can coordinate on pragmatic strategies rather than speculative hype.
The conversation shows limited outright conflict; most points are complementary. The clearest disagreements revolve around the expected scale of AI‑driven economic transformation, the survivability of openness in a commercialising AI market, and the balance between AI‑enabled empowerment versus dependence in education. A notable unexpected disagreement concerns the technical cost barrier to AI accessibility in large‑population contexts like India.
Low to moderate. While the speakers share a broadly optimistic view of AI as an amplifier of human capability, they diverge on the magnitude of economic impact, the political versus technical framing of openness and wealth distribution, and the practical pathways to achieve inclusive benefits. These differences suggest that policy discussions will need to reconcile optimistic expectations with realistic economic and technical constraints.
The discussion’s momentum was driven by LeCun’s ability to repeatedly re‑anchor the conversation from hype‑filled expectations to concrete, human‑centered realities. Each of his key remarks introduced a new lens—amplification vs. replacement, retrieval vs. reasoning, economic modesty vs. policy, and the necessity of world models—prompting Maria to probe deeper, shift topics, and explore practical implications. Collectively, these comments transformed a potentially superficial Q&A into a nuanced exploration of AI’s role as an augmentative tool, the technical gaps that remain, and the socioeconomic frameworks needed to harness its benefits.
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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