AI and Ethics: Can AI Promote Truth and the Common Good? [Silicon Spiritualities - Ep. 7]

Welcome to Silicon Spiritualities, Welcome to Silicon Spiritualities, a podcast from The Gospel Coalition a podcast from The Gospel Coalition a podcast from The Gospel Coalition exploring what it means to be human in exploring what it means to be human in exploring what it means to be human in the age of AI. the age of AI. the age of AI. I'm Christopher Watkin, and in this I'm Christopher Watkin, and in this I'm Christopher Watkin, and in this series we're unpacking three crucial series we're unpacking three crucial series we're unpacking three crucial questions. questions. questions. What can AI do for us? What can AI do for us? What can AI do for us? What is AI doing to us? And how is it What is AI doing to us? And how is it What is AI doing to us? And how is it showing us who we really are? showing us who we really are? showing us who we really are? Well, today I've got the huge joy and Well, today I've got the huge joy and Well, today I've got the huge joy and privilege of being joined by Joshua privilege of being joined by Joshua privilege of being joined by Joshua Baraka, Baraka, Baraka, AI researcher and data science lead AI researcher and data science lead AI researcher and data science lead Nairobi-based tech company Kala. Nairobi-based tech company Kala. Nairobi-based tech company Kala. And also a TGC author who's written on And also a TGC author who's written on And also a TGC author who's written on Janet generative AI, Africa, and the Janet generative AI, Africa, and the Janet generative AI, Africa, and the gospel. gospel. gospel. Christians often ask don't we, is AI Christians often ask don't we, is AI Christians often ask don't we, is AI good or bad? good or bad? good or bad? But Joshua suggests that we ask a deeper But Joshua suggests that we ask a deeper But Joshua suggests that we ask a deeper question. question. question. Namely, good or bad for whom?

Namely, good or bad for whom? Namely, good or bad for whom? And so in this episode we explore And so in this episode we explore And so in this episode we explore questions around AI, justice, and the questions around AI, justice, and the questions around AI, justice, and the common good. Who designs AI? Who common good. Who designs AI? Who common good. Who designs AI? Who benefits from it? And who bears the benefits from it? And who bears the benefits from it? And who bears the risks? risks? risks? Of course, how the church can respond to Of course, how the church can respond to Of course, how the church can respond to all of these things. all of these things. all of these things. So Joshua, welcome to the podcast. So Joshua, welcome to the podcast. So Joshua, welcome to the podcast. >> Thank you. Thank you, Dr. Chris, for >> Thank you. Thank you, Dr. Chris, for >> Thank you. Thank you, Dr. Chris, for having me. It's a pleasure to be here. having me. It's a pleasure to be here. having me. It's a pleasure to be here. And I pray that the conversations here And I pray that the conversations here And I pray that the conversations here will will do much good for God's people will will do much good for God's people will will do much good for God's people and for the glory of his name. and for the glory of his name. and for the glory of his name. >> Amen. By his grace alone, Joshua, we do >> Amen. By his grace alone, Joshua, we do >> Amen. By his grace alone, Joshua, we do pray that would indeed be the case. pray that would indeed be the case. pray that would indeed be the case. Joshua, I want to begin by setting out a Joshua, I want to begin by setting out a Joshua, I want to begin by setting out a scenario before you and asking you to scenario before you and asking you to scenario before you and asking you to respond to to this hypothetical respond to to this hypothetical respond to to this hypothetical situation. situation. situation. Um imagine Um imagine Um imagine a weary judge in a criminal court. Now, a weary judge in a criminal court. Now, a weary judge in a criminal court. Now, she's overloaded with cases, and she she's overloaded with cases, and she she's overloaded with cases, and she starts turning to an AI tool uh to starts turning to an AI tool uh to starts turning to an AI tool uh to recommend sentences. Now, this is not recommend sentences. Now, this is not recommend sentences. Now, this is not any old AI tool. It's been trained on 50 any old AI tool. It's been trained on 50 any old AI tool. It's been trained on 50 years of uh years of uh years of uh legal data, and so it's it's very legal data, and so it's it's very legal data, and so it's it's very consistent, it's very efficient, and consistent, it's very efficient, and consistent, it's very efficient, and it's actually been shown in trials to be it's actually been shown in trials to be it's actually been shown in trials to be more accurate than human judges because more accurate than human judges because more accurate than human judges because human judges uh vary their conviction human judges uh vary their conviction human judges uh vary their conviction rates depending on whether they're rates depending on whether they're rates depending on whether they're hungry or not or whether it's the end of hungry or not or whether it's the end of hungry or not or whether it's the end of the day or not, and this AI doesn't.

the day or not, and this AI doesn't. the day or not, and this AI doesn't. And it it clears the backlog, and And it it clears the backlog, and And it it clears the backlog, and justice is swift. justice is swift. justice is swift. And so, And so, And so, my question to you is my question to you is my question to you is if these sentences handed down by the if these sentences handed down by the if these sentences handed down by the large language model are statistically large language model are statistically large language model are statistically fair, and if they're on average more fair, and if they're on average more fair, and if they're on average more consistent consistent consistent than sentences passed by a human judge, than sentences passed by a human judge, than sentences passed by a human judge, is this AI justice actually better than is this AI justice actually better than is this AI justice actually better than human justice? And and what could human justice? And and what could human justice? And and what could possibly be wrong with it? possibly be wrong with it? possibly be wrong with it? >> That's a really good question because >> That's a really good question because >> That's a really good question because it's a question that even we as um at it's a question that even we as um at it's a question that even we as um at Kenya um at the Kenyan High Court has Kenya um at the Kenyan High Court has Kenya um at the Kenyan High Court has been deliberating on um because a Kenyan been deliberating on um because a Kenyan been deliberating on um because a Kenyan judge is actually being sued, and this judge is actually being sued, and this judge is actually being sued, and this case is in the Supreme Court right now case is in the Supreme Court right now case is in the Supreme Court right now because of using AI in their in their because of using AI in their in their because of using AI in their in their judgment. So, um judgment. So, um judgment. So, um so as a Christian, uh let's think about so as a Christian, uh let's think about so as a Christian, uh let's think about this from a biblical perspective because this from a biblical perspective because this from a biblical perspective because um we must wear the Bible as spectacles um we must wear the Bible as spectacles um we must wear the Bible as spectacles to see through the world. Um Ezekiel 31 to see through the world. Um Ezekiel 31 to see through the world. Um Ezekiel 31 says that says that says that um um um uh God gives Ezekiel words to eat so uh God gives Ezekiel words to eat so uh God gives Ezekiel words to eat so that it's through these words now he may that it's through these words now he may that it's through these words now he may relate to his people. Um we So, uh the relate to his people. Um we So, uh the relate to his people. Um we So, uh the there's an element of um a human being there's an element of um a human being there's an element of um a human being taking in content and digesting it taking in content and digesting it taking in content and digesting it before being able to give it to other before being able to give it to other before being able to give it to other people. We we see this same line of people. We we see this same line of people. We we see this same line of thought in Exodus 18 where God thought in Exodus 18 where God thought in Exodus 18 where God tells Moses through his brother Jethro tells Moses through his brother Jethro tells Moses through his brother Jethro that choose men above you who hate

that choose men above you who hate that choose men above you who hate bribes. So, the emphasis is on people bribes. So, the emphasis is on people bribes. So, the emphasis is on people being the arbitrators of justice. So, being the arbitrators of justice. So, being the arbitrators of justice. So, yes, this AI tool I would argue that yes, this AI tool I would argue that yes, this AI tool I would argue that it's it's it's it's helping this judge to be it's helping this judge to be it's helping this judge to be consistent, to be efficient, but consistent, to be efficient, but consistent, to be efficient, but God gives Ezekiel his word so that he God gives Ezekiel his word so that he God gives Ezekiel his word so that he may chew upon it and he may give it back may chew upon it and he may give it back may chew upon it and he may give it back to his people. So, he gives human beings to his people. So, he gives human beings to his people. So, he gives human beings the directive to judge his people as we the directive to judge his people as we the directive to judge his people as we see also in Moses. see also in Moses. see also in Moses. And remember that these AI tools um And remember that these AI tools um And remember that these AI tools um they are fed on data. So, this data can they are fed on data. So, this data can they are fed on data. So, this data can always been tweaked. Uh always been tweaked. Uh always been tweaked. Uh it's it's it's the the data that the the data that the the data that the the the that this AI systems is built upon that this AI systems is built upon that this AI systems is built upon we cannot keep it accountable. We cannot we cannot keep it accountable. We cannot we cannot keep it accountable. We cannot keep the AI system accountable. We do keep the AI system accountable. We do keep the AI system accountable. We do not know where this data is coming from not know where this data is coming from not know where this data is coming from and even if it's coming from a reliable and even if it's coming from a reliable and even if it's coming from a reliable source, it can always be tweaked later source, it can always be tweaked later source, it can always be tweaked later on. But we know that for a human being, on. But we know that for a human being, on. But we know that for a human being, even if his or her convictions change even if his or her convictions change even if his or her convictions change later on, we can keep the human being later on, we can keep the human being later on, we can keep the human being accountable. So, accountable. So, accountable. So, so my my argument, my pushback would be so my my argument, my pushback would be so my my argument, my pushback would be on the biblical basis of God giving God on the biblical basis of God giving God on the biblical basis of God giving God telling Moses to choose reliable men who telling Moses to choose reliable men who telling Moses to choose reliable men who hate bribes to be judges over Israel.

hate bribes to be judges over Israel. hate bribes to be judges over Israel. God giving the prophets uh the words to God giving the prophets uh the words to God giving the prophets uh the words to chew upon so that now he may relate upon chew upon so that now he may relate upon chew upon so that now he may relate upon the people and also because of the the people and also because of the the people and also because of the unreliability of the data, the unreliability of the data, the unreliability of the data, the inexplicability of the data. We call it inexplicability of the data. We call it inexplicability of the data. We call it a black box in data science because it's a black box in data science because it's a black box in data science because it's still very hard for us to understand still very hard for us to understand still very hard for us to understand what what what how AI makes decisions. how AI makes decisions. how AI makes decisions. And it can always be tricked, it can And it can always be tricked, it can And it can always be tricked, it can always change later on, and we cannot always change later on, and we cannot always change later on, and we cannot keep an AI system accountable if it keep an AI system accountable if it keep an AI system accountable if it makes a mistake as opposed to a human makes a mistake as opposed to a human makes a mistake as opposed to a human being. And the backlash that you're being. And the backlash that you're being. And the backlash that you're getting in Kenya where people are up in getting in Kenya where people are up in getting in Kenya where people are up in arms saying, "Ah, we can't give an AI arms saying, "Ah, we can't give an AI arms saying, "Ah, we can't give an AI system to be a judge over our people." system to be a judge over our people." system to be a judge over our people." I believe it's well grounded. I believe it's well grounded. I believe it's well grounded. >> Absolutely. Look, I fundamentally dis- >> Absolutely. Look, I fundamentally dis- >> Absolutely. Look, I fundamentally dis- agree, rather. I I fundamentally agree agree, rather. I I fundamentally agree agree, rather. I I fundamentally agree with what you're saying. But just just with what you're saying. But just just with what you're saying. But just just for the sake of for the sake of for the sake of probing the limits of this, I I do want probing the limits of this, I I do want probing the limits of this, I I do want to push back a little bit. So, you you to push back a little bit. So, you you to push back a little bit. So, you you say that AI is a black box. We we don't say that AI is a black box. We we don't say that AI is a black box. We we don't know how it gets to know how it gets to know how it gets to the conclusions it gets to.

the conclusions it gets to. the conclusions it gets to. Could you also say the same thing about Could you also say the same thing about Could you also say the same thing about a human being? That, you know, the a human being? That, you know, the a human being? That, you know, the judges have been statistically shown, I judges have been statistically shown, I judges have been statistically shown, I think it was a study in Israel, that think it was a study in Israel, that think it was a study in Israel, that they they're more likely to convict just they they're more likely to convict just they they're more likely to convict just before lunchtime when they're hungry. before lunchtime when they're hungry. before lunchtime when they're hungry. And so, we we are opaque to ourselves. And so, we we are opaque to ourselves. And so, we we are opaque to ourselves. We're sort of a black box to ourselves. We're sort of a black box to ourselves. We're sort of a black box to ourselves. So, what's the difference then? Why Why So, what's the difference then? Why Why So, what's the difference then? Why Why is one black box, the human black box, is one black box, the human black box, is one black box, the human black box, sort of okay? And why is the AI black sort of okay? And why is the AI black sort of okay? And why is the AI black box box box anathema? anathema? anathema? >> Yeah, so >> Yeah, so >> Yeah, so that's a that's a that's a That's an interesting question. At least That's an interesting question. At least That's an interesting question. At least as God has told us why we are opaque, he as God has told us why we are opaque, he as God has told us why we are opaque, he has told us that we have original sin. has told us that we have original sin. has told us that we have original sin. Genesis 3 we sinned, and God will keep Genesis 3 we sinned, and God will keep Genesis 3 we sinned, and God will keep us accountable. God is a God of justice, us accountable. God is a God of justice, us accountable. God is a God of justice, and he will one day make all things and he will one day make all things and he will one day make all things right. So, right. So, right. So, um um um So, because we know Genesis 3, we have So, because we know Genesis 3, we have So, because we know Genesis 3, we have it in mind. So, when we see men it in mind. So, when we see men it in mind. So, when we see men airing, even Christians, when they sin airing, even Christians, when they sin airing, even Christians, when they sin or when they they do they do things that or when they they do they do things that or when they they do they do things that are inefficient, we know that we still are inefficient, we know that we still are inefficient, we know that we still have the element of the original sin have the element of the original sin have the element of the original sin within us. For AI, they are they are within us. For AI, they are they are within us. For AI, they are they are opaque because opaque because opaque because it's it's hard for us to draw the it's it's hard for us to draw the it's it's hard for us to draw the diagram to how it came to diagram to how it came to diagram to how it came to this decision. I was involved in the this decision. I was involved in the this decision. I was involved in the international AI safety report for this international AI safety report for this international AI safety report for this year, 2026. And um one of the things year, 2026. And um one of the things year, 2026. And um one of the things that we saw and that we are afraid is that we saw and that we are afraid is that we saw and that we are afraid is that AI can be able to copy its own that AI can be able to copy its own that AI can be able to copy its own weight or codes into another system when weight or codes into another system when weight or codes into another system when it saw that it's going to be replaced.

it saw that it's going to be replaced. it saw that it's going to be replaced. Uh Uh Uh and that is that is making us fearful. and that is that is making us fearful. and that is that is making us fearful. Even that's that's one of the reason why Even that's that's one of the reason why Even that's that's one of the reason why the UN appointed an AI scientific panel the UN appointed an AI scientific panel the UN appointed an AI scientific panel to sit at the UN level so that it can to sit at the UN level so that it can to sit at the UN level so that it can form a scientific panel to advise on AI form a scientific panel to advise on AI form a scientific panel to advise on AI safety. Um so safety. Um so safety. Um so we it's hard for us to tell AI to make we it's hard for us to tell AI to make we it's hard for us to tell AI to make decisions because AI can copy its own decisions because AI can copy its own decisions because AI can copy its own weights or codes onto another system in weights or codes onto another system in weights or codes onto another system in the name of evading um being replaced. the name of evading um being replaced. the name of evading um being replaced. Uh so the conclusion that we made in the Uh so the conclusion that we made in the Uh so the conclusion that we made in the report is that um AI should not be used report is that um AI should not be used report is that um AI should not be used in warfare, for example. Actually, the in warfare, for example. Actually, the in warfare, for example. Actually, the the Secretary-General António Guterres the Secretary-General António Guterres the Secretary-General António Guterres said that it's morally repugnant for AI said that it's morally repugnant for AI said that it's morally repugnant for AI to be used in warfare. Of course, when to be used in warfare. Of course, when to be used in warfare. Of course, when it comes to the state, the states view it comes to the state, the states view it comes to the state, the states view it differently, but we know there's it differently, but we know there's it differently, but we know there's there's a there's a collision between there's a there's a collision between there's a there's a collision between the international system headed by the the international system headed by the the international system headed by the UN in terms of AI in warfare and the UN in terms of AI in warfare and the UN in terms of AI in warfare and the state level because you know that there state level because you know that there state level because you know that there are some states that are using AI in are some states that are using AI in are some states that are using AI in warfare. So I would use the same warfare. So I would use the same warfare. So I would use the same judgment that the UN uses in uh judgment that the UN uses in uh judgment that the UN uses in uh prohibiting uh AI use in warfare to AI prohibiting uh AI use in warfare to AI prohibiting uh AI use in warfare to AI use in judgment and the justice system use in judgment and the justice system use in judgment and the justice system because we still can't trust AI um to because we still can't trust AI um to because we still can't trust AI um to run uh at least a for human being AI you run uh at least a for human being AI you run uh at least a for human being AI you about uh about being a bigot, but you about uh about being a bigot, but you about uh about being a bigot, but you know, we have we have known all through know, we have we have known all through know, we have we have known all through even the non-Christian knows that a even the non-Christian knows that a even the non-Christian knows that a human being is is fallen. Uh you may use human being is is fallen. Uh you may use human being is is fallen. Uh you may use different words, but he knows that for different words, but he knows that for different words, but he knows that for for for an AI system, we still have a

for for an AI system, we still have a for for an AI system, we still have a long way to go in building trustworthy long way to go in building trustworthy long way to go in building trustworthy AI. AI. AI. >> Absolutely. Absolutely, we do. And I I >> Absolutely. Absolutely, we do. And I I >> Absolutely. Absolutely, we do. And I I think what you said think what you said think what you said there as part of your answer was so there as part of your answer was so there as part of your answer was so powerful. This idea of accountability. powerful. This idea of accountability. powerful. This idea of accountability. So, if a a human passes a judgment on So, if a a human passes a judgment on So, if a a human passes a judgment on me, even if it's wrong, me, even if it's wrong, me, even if it's wrong, I know that they can be held accountable I know that they can be held accountable I know that they can be held accountable for that, that they're responsible for for that, that they're responsible for for that, that they're responsible for it. it. it. Whereas in an a a situation where it's a Whereas in an a a situation where it's a Whereas in an a a situation where it's a a large language model or another a large language model or another a large language model or another technology that's passing judgment on technology that's passing judgment on technology that's passing judgment on me, it's very unclear to me if I'm me, it's very unclear to me if I'm me, it's very unclear to me if I'm upset, who I should be upset at. You upset, who I should be upset at. You upset, who I should be upset at. You know, if the if the judgment I I think know, if the if the judgment I I think know, if the if the judgment I I think is is a false one. is is a false one. is is a false one. Um and this this Um and this this Um and this this I guess fundamental desire that we have I guess fundamental desire that we have I guess fundamental desire that we have as human beings to be able as human beings to be able as human beings to be able to hold others accountable and to be to hold others accountable and to be to hold others accountable and to be accountable and responsible ourselves accountable and responsible ourselves accountable and responsible ourselves for our decisions, it showing us for our decisions, it showing us for our decisions, it showing us something really interesting about us something really interesting about us something really interesting about us that even if that even if that even if an AI system was statistically an AI system was statistically an AI system was statistically more more more consistent in the judgments it passes, consistent in the judgments it passes, consistent in the judgments it passes, my suspicion is most people would still my suspicion is most people would still my suspicion is most people would still prefer a human judge.

prefer a human judge. prefer a human judge. Um for these for these reasons. Um for these for these reasons. Um for these for these reasons. >> Yeah, and >> Yeah, and >> Yeah, and >> I'd love you to Oh, sorry, go >> I'd love you to Oh, sorry, go >> I'd love you to Oh, sorry, go >> Sorry. Allow me to add the AI >> Sorry. Allow me to add the AI >> Sorry. Allow me to add the AI intellectual ecosystem which intellectual ecosystem which intellectual ecosystem which you came up with, the one that you came up with, the one that you came up with, the one that is AI a tool, is AI a playground or the is AI a tool, is AI a playground or the is AI a tool, is AI a playground or the way to AI being an authority way to AI being an authority way to AI being an authority to deity. to deity. to deity. So, there's a place for AI in the in the So, there's a place for AI in the in the So, there's a place for AI in the in the justice system. There's a place for AI justice system. There's a place for AI justice system. There's a place for AI in the whole intellectual ecosystem. So, in the whole intellectual ecosystem. So, in the whole intellectual ecosystem. So, maybe AI can be used as a tool where we maybe AI can be used as a tool where we maybe AI can be used as a tool where we can use AI to can use AI to can use AI to to draw inferences. It can be used as an to draw inferences. It can be used as an to draw inferences. It can be used as an encyclopedia. encyclopedia. encyclopedia. But AI should not be used as an But AI should not be used as an But AI should not be used as an authority or even worse as deity authority or even worse as deity authority or even worse as deity within the whole intellectual ecosystem. within the whole intellectual ecosystem. within the whole intellectual ecosystem. >> Yeah, that's so helpful. We we're not >> Yeah, that's so helpful. We we're not >> Yeah, that's so helpful. We we're not saying the judge should never go saying the judge should never go saying the judge should never go anywhere near AI under any anywhere near AI under any anywhere near AI under any circumstances. It is is the discernment circumstances. It is is the discernment circumstances. It is is the discernment of the particular uses of it that are of the particular uses of it that are of the particular uses of it that are key. Thank you for that that key. Thank you for that that key. Thank you for that that clarification.

clarification. clarification. I'd love to give for you to give us a I'd love to give for you to give us a I'd love to give for you to give us a sense of how the AI landscape looks from sense of how the AI landscape looks from sense of how the AI landscape looks from your position in Nairobi at the moment. your position in Nairobi at the moment. your position in Nairobi at the moment. What are people most excited about What are people most excited about What are people most excited about around you? What are you most excited around you? What are you most excited around you? What are you most excited about? And and what is keeping people about? And and what is keeping people about? And and what is keeping people that you're talking with up at night that you're talking with up at night that you're talking with up at night when they're thinking about AI? when they're thinking about AI? when they're thinking about AI? >> Yeah, so as you mentioned, >> Yeah, so as you mentioned, >> Yeah, so as you mentioned, I work at an AI company, I work at an AI company, I work at an AI company, a pan-African AI company headquartered a pan-African AI company headquartered a pan-African AI company headquartered in Nairobi. So our work is in not in Nairobi. So our work is in not in Nairobi. So our work is in not building frontier AI models, but in building frontier AI models, but in building frontier AI models, but in building an ecosystem where AI can building an ecosystem where AI can building an ecosystem where AI can flourish. flourish. flourish. So So So the the problem with Africa and Kenya is the the problem with Africa and Kenya is the the problem with Africa and Kenya is that we must first of all build that we must first of all build that we must first of all build underlying infrastructure first before underlying infrastructure first before underlying infrastructure first before we can we can we can build large language models that the build large language models that the build large language models that the rest of the world can can consume. One rest of the world can can consume. One rest of the world can can consume. One of the reasons is of the reasons is of the reasons is we have just one 1% data center capacity we have just one 1% data center capacity we have just one 1% data center capacity in the in the continent. So in the in the continent. So in the in the continent. So the if we had a thousand data centers in the if we had a thousand data centers in the if we had a thousand data centers in the world, only 1% of them would be in the world, only 1% of them would be in the world, only 1% of them would be in Africa.

Africa. Africa. And 50% of them are in South Africa And 50% of them are in South Africa And 50% of them are in South Africa with Cassava Technologies. with Cassava Technologies. with Cassava Technologies. Also, the continent has less than 1% AI Also, the continent has less than 1% AI Also, the continent has less than 1% AI talent. So we usually group AI talent talent. So we usually group AI talent talent. So we usually group AI talent into AI trainers, the people who into AI trainers, the people who into AI trainers, the people who actually do the actually do the actually do the the training of the models, AI the training of the models, AI the training of the models, AI sustainers, those who push for sustainers, those who push for sustainers, those who push for explainable for safe AI, and then we explainable for safe AI, and then we explainable for safe AI, and then we have AI explainers, those who push for have AI explainers, those who push for have AI explainers, those who push for explainable AI that is safe for explainable AI that is safe for explainable AI that is safe for consumptions. So we have less than 1% consumptions. So we have less than 1% consumptions. So we have less than 1% again of that talent. So, for for for again of that talent. So, for for for again of that talent. So, for for for Kenya and for Africa, Kenya and for Africa, Kenya and for Africa, we are we are pushing for for an AI we are we are pushing for for an AI we are we are pushing for for an AI ecosystem based on six ecosystem based on six ecosystem based on six models or six pillars. We are pushing models or six pillars. We are pushing models or six pillars. We are pushing for more of our data to be in the to be for more of our data to be in the to be for more of our data to be in the to be used in training AI models, to more of used in training AI models, to more of used in training AI models, to more of our talent, people can actually build our talent, people can actually build our talent, people can actually build these models, computes to have more GPUs these models, computes to have more GPUs these models, computes to have more GPUs for data centers, for data centers, for data centers, to have governance guardrails so that so to have governance guardrails so that so to have governance guardrails so that so that we may be able to to create just that we may be able to to create just that we may be able to to create just laws that are going to laws that are going to laws that are going to to form frameworks around how we should to form frameworks around how we should to form frameworks around how we should develop AI or how we should not, to push develop AI or how we should not, to push develop AI or how we should not, to push for more investments for more investments for more investments because because because ultimately ultimately ultimately there must be money to fuel this whole there must be money to fuel this whole there must be money to fuel this whole thing and markets. Where where is this thing and markets. Where where is this thing and markets. Where where is this AI going to be applied? The use cases AI going to be applied? The use cases AI going to be applied? The use cases that are going to govern how this AI is that are going to govern how this AI is that are going to govern how this AI is going to to be developed. So,

going to to be developed. So, going to to be developed. So, we are big on AI. we are big on AI. we are big on AI. Africans are big on AI consumption. So, Africans are big on AI consumption. So, Africans are big on AI consumption. So, what we we we love to do is to push what we we we love to do is to push what we we we love to do is to push Africans away from consumption. Africans away from consumption. Africans away from consumption. Consumption is good, but we need Consumption is good, but we need Consumption is good, but we need Africans to be developers of of the Africans to be developers of of the Africans to be developers of of the technology technology technology so that we we finally finally build our so that we we finally finally build our so that we we finally finally build our own technology so that our own people own technology so that our own people own technology so that our own people may profit out of may profit out of may profit out of out of the technology revolutions that out of the technology revolutions that out of the technology revolutions that that we have that we are seeing right that we have that we are seeing right that we have that we are seeing right now. So, for example, Stanford AI now. So, for example, Stanford AI now. So, for example, Stanford AI indexes that 27% of Kenyans use general indexes that 27% of Kenyans use general indexes that 27% of Kenyans use general generative AI every day. That's a huge generative AI every day. That's a huge generative AI every day. That's a huge one. one. one. Last year, 2025, there was another Last year, 2025, there was another Last year, 2025, there was another report by Data Report or that says that report by Data Report or that says that report by Data Report or that says that Kenya is ranked number one in the use of Kenya is ranked number one in the use of Kenya is ranked number one in the use of ChatGPT globally. ChatGPT globally. ChatGPT globally. That's a huge statistic that we are That's a huge statistic that we are That's a huge statistic that we are that most Kenyans more Kenyans are using that most Kenyans more Kenyans are using that most Kenyans more Kenyans are using uh ChatGPT than any other country uh ChatGPT than any other country uh ChatGPT than any other country in the world. So, so you see the hunger in the world. So, so you see the hunger in the world. So, so you see the hunger that is in that is in Kenyans and that is in that is in Kenyans and that is in that is in Kenyans and and is in Africans generally. I'm using and is in Africans generally. I'm using and is in Africans generally. I'm using Kenya as a case study Kenya as a case study Kenya as a case study for AI consumption. But then we need to for AI consumption. But then we need to for AI consumption. But then we need to we need to move away from consumption to we need to move away from consumption to we need to move away from consumption to to development. And investment is a big to development. And investment is a big to development. And investment is a big stumbling block. You would remember that stumbling block. You would remember that stumbling block. You would remember that OpenAI became a household name because OpenAI became a household name because OpenAI became a household name because in July 2019, Microsoft invested $1 in July 2019, Microsoft invested $1 in July 2019, Microsoft invested $1 billion billion billion to OpenAI. And that made OpenAI

to OpenAI. And that made OpenAI to OpenAI. And that made OpenAI to live up to its name. Some says that to live up to its name. Some says that to live up to its name. Some says that right now it should be called closed AI. right now it should be called closed AI. right now it should be called closed AI. But back then it was living up to its But back then it was living up to its But back then it was living up to its name of OpenAI because name of OpenAI because name of OpenAI because in 2020, they opened up they in 2020, they opened up they in 2020, they opened up they open-sourced their APIs to developers. open-sourced their APIs to developers. open-sourced their APIs to developers. So, when later on in 2022 November when So, when later on in 2022 November when So, when later on in 2022 November when they open-sourced their ChatGPT model, they open-sourced their ChatGPT model, they open-sourced their ChatGPT model, their chatbot to the public, the their chatbot to the public, the their chatbot to the public, the developers had already experienced that developers had already experienced that developers had already experienced that in 2020 when they open-sourced their in 2020 when they open-sourced their in 2020 when they open-sourced their APIs. So, you see the the place of APIs. So, you see the the place of APIs. So, you see the the place of investment. So, that's where Africa investment. So, that's where Africa investment. So, that's where Africa stands in terms of stands in terms of stands in terms of our AI development cycle. We are trying our AI development cycle. We are trying our AI development cycle. We are trying to build an ecosystem first of data, to build an ecosystem first of data, to build an ecosystem first of data, talent, compute, governance, investment, talent, compute, governance, investment, talent, compute, governance, investment, and markets so that now after we have and markets so that now after we have and markets so that now after we have done that, now we can build frontier AI done that, now we can build frontier AI done that, now we can build frontier AI models. But we are developing small models. But we are developing small models. But we are developing small language models. We are developing language models. We are developing language models. We are developing natural language processing where we are natural language processing where we are natural language processing where we are collecting data and collecting data and collecting data and pushing our languages from low resource pushing our languages from low resource pushing our languages from low resource to high resource so that now they can be to high resource so that now they can be to high resource so that now they can be trained on data, digitizing our trained on data, digitizing our trained on data, digitizing our languages. For example, there's a languages. For example, there's a languages. For example, there's a language in Kenya which is big, but then language in Kenya which is big, but then language in Kenya which is big, but then we only have about 20 characters of that we only have about 20 characters of that we only have about 20 characters of that language language language digitized. So, now we need to move digitized. So, now we need to move digitized. So, now we need to move across the entire digitalization digital across the entire digitalization digital across the entire digitalization digital digital ecosystem digital ecosystem digital ecosystem pipeline so that now we may train this pipeline so that now we may train this pipeline so that now we may train this data. So, that's where it starts, Dr.

data. So, that's where it starts, Dr. data. So, that's where it starts, Dr. Crutchfield. Crutchfield. Crutchfield. >> Absolutely. And I'd I'd love to come >> Absolutely. And I'd I'd love to come >> Absolutely. And I'd I'd love to come back in a moment to to these questions back in a moment to to these questions back in a moment to to these questions of the distribution of AI talent and in of the distribution of AI talent and in of the distribution of AI talent and in investment across the globe cuz I think investment across the globe cuz I think investment across the globe cuz I think there are some incredibly important there are some incredibly important there are some incredibly important points to make there. But just just points to make there. But just just points to make there. But just just before we do that, I'd love to get some before we do that, I'd love to get some before we do that, I'd love to get some fundamental fundamental fundamental sort of building blocks of the sort of building blocks of the sort of building blocks of the conversation in place in terms of the conversation in place in terms of the conversation in place in terms of the the the the the the the key key key Bible principles and Bible passages that Bible principles and Bible passages that Bible principles and Bible passages that that you that you that you use yourself to to guide your own use yourself to to guide your own use yourself to to guide your own thinking on AI and that you would thinking on AI and that you would thinking on AI and that you would commend to Christians if we're to think commend to Christians if we're to think commend to Christians if we're to think biblically about AI. Of course, we use biblically about AI. Of course, we use biblically about AI. Of course, we use we use the whole of the Bible. Um but we use the whole of the Bible. Um but we use the whole of the Bible. Um but which passages are are which biblical which passages are are which biblical which passages are are which biblical verses have been most helpful to you in verses have been most helpful to you in verses have been most helpful to you in picking through the different issues picking through the different issues picking through the different issues around artificial intelligence? around artificial intelligence? around artificial intelligence? >> Matthew 28:18 verse looks stand tall in >> Matthew 28:18 verse looks stand tall in >> Matthew 28:18 verse looks stand tall in those verses. those verses. those verses. Uh Uh Uh all authority in heaven and on others be all authority in heaven and on others be all authority in heaven and on others be given to me. That's Matthew 28:18, the given to me. That's Matthew 28:18, the given to me. That's Matthew 28:18, the second part. So, all authority, meaning second part. So, all authority, meaning second part. So, all authority, meaning that that that uh Paul uh or rather Jesus has uh Paul uh or rather Jesus has uh Paul uh or rather Jesus has authority over everything including AI.

authority over everything including AI. authority over everything including AI. So, this AI did not catch So, this AI did not catch So, this AI did not catch uh Jesus as oops, no no no. He was he uh Jesus as oops, no no no. He was he uh Jesus as oops, no no no. He was he was in full was in full was in full um um um he's actually the one who's sovereign he's actually the one who's sovereign he's actually the one who's sovereign created AI to be here. We can make that created AI to be here. We can make that created AI to be here. We can make that bold assertion because we see it from bold assertion because we see it from bold assertion because we see it from scriptures. We also see Colossians scriptures. We also see Colossians scriptures. We also see Colossians um um um uh 1:16 that where he says that um uh 1:16 that where he says that um uh 1:16 that where he says that um Paul says that all things were created Paul says that all things were created Paul says that all things were created through him and for him. So, Paul is through him and for him. So, Paul is through him and for him. So, Paul is saying about Jesus that all things um saying about Jesus that all things um saying about Jesus that all things um last time I checked, there's nothing last time I checked, there's nothing last time I checked, there's nothing that is outside all. So, AI is within that is outside all. So, AI is within that is outside all. So, AI is within all. So, we can say AI was created all. So, we can say AI was created all. So, we can say AI was created through him and for him. through him and for him. through him and for him. Uh and so, from these creatures we can Uh and so, from these creatures we can Uh and so, from these creatures we can conclude that that Jesus has full and conclude that that Jesus has full and conclude that that Jesus has full and sovereign authority over AI because of sovereign authority over AI because of sovereign authority over AI because of um because of Matthew 28 that he has um because of Matthew 28 that he has um because of Matthew 28 that he has authority over everything. We can authority over everything. We can authority over everything. We can conclude that AI exists because Jesus conclude that AI exists because Jesus conclude that AI exists because Jesus allowed it to exist for all things were allowed it to exist for all things were allowed it to exist for all things were made through him and for him. And that made through him and for him. And that made through him and for him. And that we can conclude that AI was made to we can conclude that AI was made to we can conclude that AI was made to glorify Jesus because glorify Jesus because glorify Jesus because all things were made for him. I agree all things were made for him. I agree all things were made for him. I agree with Abraham Kuyper and uh with Abraham Kuyper and uh with Abraham Kuyper and uh a theologian who was also a Dutch um a theologian who was also a Dutch um a theologian who was also a Dutch um prime minister who says that there's not prime minister who says that there's not prime minister who says that there's not a square inch in the whole domain of our a square inch in the whole domain of our a square inch in the whole domain of our human existence over which Christ who is human existence over which Christ who is human existence over which Christ who is sovereign over all does not cry, "Mine."

sovereign over all does not cry, "Mine." sovereign over all does not cry, "Mine." So, I am fully convinced of God's So, I am fully convinced of God's So, I am fully convinced of God's sovereignty over AI. I mean, we have sovereignty over AI. I mean, we have sovereignty over AI. I mean, we have other verses like Isaiah 40:17 that this other verses like Isaiah 40:17 that this other verses like Isaiah 40:17 that this is has been a very dear verse to me ever is has been a very dear verse to me ever is has been a very dear verse to me ever since I came to the faith that all the since I came to the faith that all the since I came to the faith that all the nations are as nothing before him. They nations are as nothing before him. They nations are as nothing before him. They are counted by him as less than nothing are counted by him as less than nothing are counted by him as less than nothing and emptiness. To whom then will you and emptiness. To whom then will you and emptiness. To whom then will you liken God? Or what likeness compare with liken God? Or what likeness compare with liken God? Or what likeness compare with him? Coming back to um the the AI him? Coming back to um the the AI him? Coming back to um the the AI intellectual ecosystem intellectual ecosystem intellectual ecosystem that we talked about where some some that we talked about where some some that we talked about where some some people put AI as deity, people put AI as deity, people put AI as deity, God would tell you, "To whom then will God would tell you, "To whom then will God would tell you, "To whom then will you liken God? Or what likeness can you you liken God? Or what likeness can you you liken God? Or what likeness can you compare AI with God?" Everything, all compare AI with God?" Everything, all compare AI with God?" Everything, all the nations, including AI, they are the nations, including AI, they are the nations, including AI, they are counted to him as less than nothing. counted to him as less than nothing. counted to him as less than nothing. Uh and um a final verse that we love to Uh and um a final verse that we love to Uh and um a final verse that we love to share is Romans 5:5. Again, another dear share is Romans 5:5. Again, another dear share is Romans 5:5. Again, another dear verse to me. I love Romans 5 because of verse to me. I love Romans 5 because of verse to me. I love Romans 5 because of how much gospel punch it is. So, it says how much gospel punch it is. So, it says how much gospel punch it is. So, it says that hope does not disappoint us because that hope does not disappoint us because that hope does not disappoint us because God has poured out his love into our God has poured out his love into our God has poured out his love into our hearts through the Holy Spirit whom he hearts through the Holy Spirit whom he hearts through the Holy Spirit whom he has given us. So, it's important for us has given us. So, it's important for us has given us. So, it's important for us to look at hope as a as a big theme in to look at hope as a as a big theme in to look at hope as a as a big theme in the Bible and how it's coming into air the Bible and how it's coming into air the Bible and how it's coming into air because um um because um um because um um there's a word that is being thrown there's a word that is being thrown there's a word that is being thrown around in AI called techno-solutionism.

around in AI called techno-solutionism. around in AI called techno-solutionism. So, what it basically says that is that So, what it basically says that is that So, what it basically says that is that there's something wrong uh wrong with there's something wrong uh wrong with there's something wrong uh wrong with the world and AI is coming to fix it. the world and AI is coming to fix it. the world and AI is coming to fix it. So, AI is going to be the solution to So, AI is going to be the solution to So, AI is going to be the solution to what is wrong with the world. And you what is wrong with the world. And you what is wrong with the world. And you can see this especially with big tech can see this especially with big tech can see this especially with big tech where they are saying that AI is going where they are saying that AI is going where they are saying that AI is going to be the solution to everything. But to be the solution to everything. But to be the solution to everything. But then we know that fundamentally then we know that fundamentally then we know that fundamentally the human beings' problems is not the human beings' problems is not the human beings' problems is not outside but within. And and hope is outside but within. And and hope is outside but within. And and hope is because God has poured out his love to because God has poured out his love to because God has poured out his love to our hearts through the Holy Spirit. So, our hearts through the Holy Spirit. So, our hearts through the Holy Spirit. So, our hope is not in techno-solutionism. our hope is not in techno-solutionism. our hope is not in techno-solutionism. Tech technology is great. I started with Tech technology is great. I started with Tech technology is great. I started with the argument that it is here because God the argument that it is here because God the argument that it is here because God sovereignly decreed it to be here. sovereignly decreed it to be here. sovereignly decreed it to be here. Uh but then our problem is inside and Uh but then our problem is inside and Uh but then our problem is inside and the only hope that you have is in Jesus. the only hope that you have is in Jesus. the only hope that you have is in Jesus. So, these are some of the the the Bible So, these are some of the the the Bible So, these are some of the the the Bible verses that hold me uh verses that hold me uh verses that hold me uh steadily in this AI race. steadily in this AI race. steadily in this AI race. >> So helpful. It is so true, isn't it, >> So helpful. It is so true, isn't it, >> So helpful. It is so true, isn't it, that if we think that our most that if we think that our most that if we think that our most fundamental problems are such that they fundamental problems are such that they fundamental problems are such that they can be solved by any level of artificial can be solved by any level of artificial can be solved by any level of artificial intelligence, then we haven't thought intelligence, then we haven't thought intelligence, then we haven't thought seriously enough about our problems and seriously enough about our problems and seriously enough about our problems and how deep they go.

how deep they go. how deep they go. And similarly, if if we think that a a And similarly, if if we think that a a And similarly, if if we think that a a techno- optimistic fix, even if it's techno- optimistic fix, even if it's techno- optimistic fix, even if it's expanding our expanding our expanding our lengthening our our lifespan, if we lengthening our our lifespan, if we lengthening our our lifespan, if we think that's what we should hope for, think that's what we should hope for, think that's what we should hope for, then our hopes are very small indeed then our hopes are very small indeed then our hopes are very small indeed compared to the wonderfully glorious compared to the wonderfully glorious compared to the wonderfully glorious biblical hope of the new heavens and the biblical hope of the new heavens and the biblical hope of the new heavens and the new earth where we see God face-to-face. new earth where we see God face-to-face. new earth where we see God face-to-face. And And And I think in in AI it's in in so as in so I think in in AI it's in in so as in so I think in in AI it's in in so as in so many other areas, isn't it? As as soon many other areas, isn't it? As as soon many other areas, isn't it? As as soon as we we look both at what as we we look both at what as we we look both at what what the problem is and what the what the problem is and what the what the problem is and what the solution is from within the AI world and solution is from within the AI world and solution is from within the AI world and then what the problem is and what the then what the problem is and what the then what the problem is and what the solution is from the Bible. The the the solution is from the Bible. The the the solution is from the Bible. The the the AI would just look so emaciated, so AI would just look so emaciated, so AI would just look so emaciated, so superficial superficial superficial >> Yeah. >> Yeah. >> Yeah. >> by comparison >> by comparison >> by comparison with the AI one. And I love your with the AI one. And I love your with the AI one. And I love your emphasis on emphasis on emphasis on you know, however people talk about you know, however people talk about you know, however people talk about artificial super intelligence and you artificial super intelligence and you artificial super intelligence and you know, surpassing human intelligence that know, surpassing human intelligence that know, surpassing human intelligence that hold on.

hold on. hold on. Jesus is still sovereign. Jesus is still sovereign. Jesus is still sovereign. And all of this is as nothing. And all of this is as nothing. And all of this is as nothing. Even that the greatest, you know, Even that the greatest, you know, Even that the greatest, you know, intelligence that may or may not surpass intelligence that may or may not surpass intelligence that may or may not surpass human intelligence by orders of human intelligence by orders of human intelligence by orders of magnitude, it is still magnitude, it is still magnitude, it is still a dust on the scales a dust on the scales a dust on the scales compared to the God of the universe who compared to the God of the universe who compared to the God of the universe who created everything created everything created everything through the word of his mouth. And through the word of his mouth. And through the word of his mouth. And that's such an important perspective cuz that's such an important perspective cuz that's such an important perspective cuz we are going to get these stories, we are going to get these stories, we are going to get these stories, aren't we, over the coming years and and aren't we, over the coming years and and aren't we, over the coming years and and over the coming months, you know, that over the coming months, you know, that over the coming months, you know, that that talk about unheard of intelligence, that talk about unheard of intelligence, that talk about unheard of intelligence, you know, step changes in the human you know, step changes in the human you know, step changes in the human story and all that sort of thing. And as story and all that sort of thing. And as story and all that sort of thing. And as as Christians, we we need to remember as Christians, we we need to remember as Christians, we we need to remember that that that the the the um um um all of this happens within the context all of this happens within the context all of this happens within the context of a God of a God of a God who is sovereign who is sovereign who is sovereign and who is infinitely and who is infinitely and who is infinitely wiser and more powerful than than any wiser and more powerful than than any wiser and more powerful than than any intelligence that we can intelligence that we can intelligence that we can uh uh uh manufacture. manufacture. manufacture. >> Yeah. >> Yeah. >> Yeah. >> Effectively. It's great It's great to be >> Effectively. It's great It's great to be >> Effectively. It's great It's great to be a Christian, isn't it, Joshua? I just as a Christian, isn't it, Joshua? I just as a Christian, isn't it, Joshua? I just as we're talking, what a what a joy it is we're talking, what a what a joy it is we're talking, what a what a joy it is to know the Lord Jesus Christ in the in to know the Lord Jesus Christ in the in to know the Lord Jesus Christ in the in the midst of the AI revolution. Um you the midst of the AI revolution. Um you the midst of the AI revolution. Um you know, as as in any cultural moment, but know, as as in any cultural moment, but know, as as in any cultural moment, but right now, what a what a right now, what a what a right now, what a what a stable stable stable robust joyful position it is to to be a robust joyful position it is to to be a robust joyful position it is to to be a Christian.

Christian. Christian. >> I wanted to add something on to it's >> I wanted to add something on to it's >> I wanted to add something on to it's just to be a Christian. You would just to be a Christian. You would just to be a Christian. You would remember that Martin Luther, the the remember that Martin Luther, the the remember that Martin Luther, the the German reformer, said that um German reformer, said that um German reformer, said that um when when people sin, it's fundamentally when when people sin, it's fundamentally when when people sin, it's fundamentally a breaking of the first commandment, a breaking of the first commandment, a breaking of the first commandment, "Thou shalt have no other gods before "Thou shalt have no other gods before "Thou shalt have no other gods before me." That all other sins all other We me." That all other sins all other We me." That all other sins all other We break all the other nine commandments by break all the other nine commandments by break all the other nine commandments by first breaking the first. first breaking the first. first breaking the first. So, uh when we um commit adultery, it's So, uh when we um commit adultery, it's So, uh when we um commit adultery, it's because this we are worshipping someone because this we are worshipping someone because this we are worshipping someone as opposed to worshipping God. When we as opposed to worshipping God. When we as opposed to worshipping God. When we covet, we are worshipping something covet, we are worshipping something covet, we are worshipping something because we have broken the first because we have broken the first because we have broken the first commandment in its place. So, for the commandment in its place. So, for the commandment in its place. So, for the Christian, ultimately it's uh Christian, ultimately it's uh Christian, ultimately it's uh worshipping the right um worshipping the worshipping the right um worshipping the worshipping the right um worshipping the triune God, because that's what Jesus triune God, because that's what Jesus triune God, because that's what Jesus said that um love me with all your said that um love me with all your said that um love me with all your heart. Love the Lord your God with all heart. Love the Lord your God with all heart. Love the Lord your God with all your heart, with all your mind, and with your heart, with all your mind, and with your heart, with all your mind, and with all your soul. That's is the first and all your soul. That's is the first and all your soul. That's is the first and greatest commandment. Um and then love greatest commandment. Um and then love greatest commandment. Um and then love your neighbor as you love yourself. So, your neighbor as you love yourself. So, your neighbor as you love yourself. So, even as you are at the dealing about um even as you are at the dealing about um even as you are at the dealing about um AI, we need to think about how we are AI, we need to think about how we are AI, we need to think about how we are loving the Lord our God with all our loving the Lord our God with all our loving the Lord our God with all our minds, heart, and soul, and also how we minds, heart, and soul, and also how we minds, heart, and soul, and also how we are loving our neighbor. That's the are loving our neighbor. That's the are loving our neighbor. That's the basic um Christian worldview to AI that basic um Christian worldview to AI that basic um Christian worldview to AI that we should try to judge ourselves upon.

we should try to judge ourselves upon. we should try to judge ourselves upon. >> Yeah, that's incredibly helpful. And we >> Yeah, that's incredibly helpful. And we >> Yeah, that's incredibly helpful. And we we are going to touch upon some of the we are going to touch upon some of the we are going to touch upon some of the aspects of that. What does it mean to aspects of that. What does it mean to aspects of that. What does it mean to love your neighbor as as we engage with love your neighbor as as we engage with love your neighbor as as we engage with AI in in just a moment. Just before we AI in in just a moment. Just before we AI in in just a moment. Just before we get there, I I I want to pick up on one get there, I I I want to pick up on one get there, I I I want to pick up on one thing you were mentioning, which is thing you were mentioning, which is thing you were mentioning, which is uh our sinfulness. And it uh our sinfulness. And it uh our sinfulness. And it one thing you I think you wrote in an one thing you I think you wrote in an one thing you I think you wrote in an article that I read really struck me. article that I read really struck me. article that I read really struck me. You said AI isn't evil, You said AI isn't evil, You said AI isn't evil, but we are. but we are. but we are. Um Um Um it's one of those C.S. Lewis type it's one of those C.S. Lewis type it's one of those C.S. Lewis type sentences, isn't it? Just really sort of sentences, isn't it? Just really sort of sentences, isn't it? Just really sort of limpid and and direct. Um how how does limpid and and direct. Um how how does limpid and and direct. Um how how does seeing AI therefore as as a mirror of seeing AI therefore as as a mirror of seeing AI therefore as as a mirror of our own human hearts, of our own human our own human hearts, of our own human our own human hearts, of our own human evilness, evil do you think evilness, evil do you think evilness, evil do you think perhaps change the way or help us to perhaps change the way or help us to perhaps change the way or help us to find a way as Christians to approach AI find a way as Christians to approach AI find a way as Christians to approach AI ethics? ethics? ethics? >> Yeah, so to refer to AI being evil would >> Yeah, so to refer to AI being evil would >> Yeah, so to refer to AI being evil would uh uh uh would mean that we would need to give AI would mean that we would need to give AI would mean that we would need to give AI some form of um sentient being. Although some form of um sentient being. Although some form of um sentient being. Although it's it need to be a living entity which it's it need to be a living entity which it's it need to be a living entity which has uh high level of agency, uh has uh high level of agency, uh has uh high level of agency, uh consciousness, consciousness, consciousness, um um um How would I argue about that is that How would I argue about that is that How would I argue about that is that human beings since the fall have been human beings since the fall have been human beings since the fall have been trying to escape liability for evil.

trying to escape liability for evil. trying to escape liability for evil. That's why uh I make the case that AI That's why uh I make the case that AI That's why uh I make the case that AI isn't evil but we are because uh by uh isn't evil but we are because uh by uh isn't evil but we are because uh by uh by making the assertion that AI is evil, by making the assertion that AI is evil, by making the assertion that AI is evil, it's almost as if we are making the the it's almost as if we are making the the it's almost as if we are making the the the thing that Adam did when he said the thing that Adam did when he said the thing that Adam did when he said that when God came and told him, "Hey that when God came and told him, "Hey that when God came and told him, "Hey Adam, where are you?" He said that "It's Adam, where are you?" He said that "It's Adam, where are you?" He said that "It's this woman who you gave me. That's the this woman who you gave me. That's the this woman who you gave me. That's the reason why I ate the fruit." And even reason why I ate the fruit." And even reason why I ate the fruit." And even Eve herself she she shifted the blame to Eve herself she she shifted the blame to Eve herself she she shifted the blame to the snake uh that it's the snake that the snake uh that it's the snake that the snake uh that it's the snake that made me um So, made me um So, made me um So, uh so only when we view ourselves as uh so only when we view ourselves as uh so only when we view ourselves as wicked, we will we will accept our wicked, we will we will accept our wicked, we will we will accept our evilness and we will cry out to God that evilness and we will cry out to God that evilness and we will cry out to God that have mercy on me a sinner. So, um I view have mercy on me a sinner. So, um I view have mercy on me a sinner. So, um I view AI as being a scapegoat AI as being a scapegoat AI as being a scapegoat uh in human beings trying to shift the uh in human beings trying to shift the uh in human beings trying to shift the blame elsewhere. Um So, yes, it is a blame elsewhere. Um So, yes, it is a blame elsewhere. Um So, yes, it is a parading technology. Yes, it does parading technology. Yes, it does parading technology. Yes, it does mirror our evilness. Um the whole reason mirror our evilness. Um the whole reason mirror our evilness. Um the whole reason why data annotators are needed is why data annotators are needed is why data annotators are needed is because AI model once an AI model is because AI model once an AI model is because AI model once an AI model is trained on the dark side of the trained on the dark side of the trained on the dark side of the internet, this and this darkness will be internet, this and this darkness will be internet, this and this darkness will be will be represented in AI models. So, um will be represented in AI models. So, um will be represented in AI models. So, um once the data is is wicked, the the once the data is is wicked, the the once the data is is wicked, the the output is going to be wicked. And um but output is going to be wicked. And um but output is going to be wicked. And um but it's because we are evil. So, it's it's because we are evil. So, it's it's because we are evil. So, it's because it's mirroring us. It's not because it's mirroring us. It's not because it's mirroring us. It's not because it itself it has some sort of because it itself it has some sort of because it itself it has some sort of consciousness that now uh becomes consciousness that now uh becomes consciousness that now uh becomes becomes evil by philosophically

becomes evil by philosophically becomes evil by philosophically speaking. speaking. speaking. >> Yes, it has no evil of its own. It just >> Yes, it has no evil of its own. It just >> Yes, it has no evil of its own. It just mirrors our own evil back to us. Um, mirrors our own evil back to us. Um, mirrors our own evil back to us. Um, in in ways that we we don't enjoy being in in ways that we we don't enjoy being in in ways that we we don't enjoy being confronted with. confronted with. confronted with. Um, I I want to get on to this idea now Um, I I want to get on to this idea now Um, I I want to get on to this idea now of of neighbor love that you mentioned of of neighbor love that you mentioned of of neighbor love that you mentioned cuz I think it's incredibly important cuz I think it's incredibly important cuz I think it's incredibly important and it I suspect it's something that and it I suspect it's something that and it I suspect it's something that almost all of us don't think enough almost all of us don't think enough almost all of us don't think enough about when it comes to AI. about when it comes to AI. about when it comes to AI. And I want to introduce the theme with And I want to introduce the theme with And I want to introduce the theme with with another scenario, if if you'll bear with another scenario, if if you'll bear with another scenario, if if you'll bear with me for a moment. So, imagine a with me for a moment. So, imagine a with me for a moment. So, imagine a Christian software engineer that's Christian software engineer that's Christian software engineer that's sitting in Sydney or they're sitting in sitting in Sydney or they're sitting in sitting in Sydney or they're sitting in London or somewhere else, and London or somewhere else, and London or somewhere else, and they're using AI every day, and you they're using AI every day, and you they're using AI every day, and you know, the vibe that they have about it know, the vibe that they have about it know, the vibe that they have about it is, you know, this is free magic. This is, you know, this is free magic. This is, you know, this is free magic. This is getting my work done much faster than is getting my work done much faster than is getting my work done much faster than I could ever imagine doing it otherwise. I could ever imagine doing it otherwise. I could ever imagine doing it otherwise. And then And then And then they read one day about the the they read one day about the the they read one day about the the traumatic work being done by young men traumatic work being done by young men traumatic work being done by young men and women in, say, Venezuela, and women in, say, Venezuela, and women in, say, Venezuela, uh, where they're tagging uh, uh, where they're tagging uh, uh, where they're tagging uh, objectionable objectionable objectionable uh, racist, um, uh, images or or text in uh, racist, um, uh, images or or text in uh, racist, um, uh, images or or text in order to to train these AI models, you order to to train these AI models, you order to to train these AI models, you know, through this reinforcement know, through this reinforcement know, through this reinforcement learning through through human feedback learning through through human feedback learning through through human feedback to to make the models be polite when to to make the models be polite when to to make the models be polite when they interact with people and not simply they interact with people and not simply they interact with people and not simply to reflect everything that's out there to reflect everything that's out there to reflect everything that's out there on the internet, which it which is, of on the internet, which it which is, of on the internet, which it which is, of course, a bit of a wild west. Um, and course, a bit of a wild west. Um, and course, a bit of a wild west. Um, and these people are getting radically these people are getting radically these people are getting radically underpaid. You know, they're they're underpaid. You know, they're they're underpaid. You know, they're they're doing this for for cents in the

doing this for for cents in the doing this for for cents in the per hour. per hour. per hour. How should How should How should the software engineer the software engineer the software engineer be thinking about AI be thinking about AI be thinking about AI and thinking about their moral and thinking about their moral and thinking about their moral involvement with these tools involvement with these tools involvement with these tools given this reality that other people are given this reality that other people are given this reality that other people are being essentially exploited to clean the being essentially exploited to clean the being essentially exploited to clean the data to make these models work. data to make these models work. data to make these models work. >> Yeah, so um, one is that the engineer >> Yeah, so um, one is that the engineer >> Yeah, so um, one is that the engineer should be in the know. First of all, should be in the know. First of all, should be in the know. First of all, they should know they should know they should know about the AI supply chain. And it's about the AI supply chain. And it's about the AI supply chain. And it's that's the reason that you have brought that's the reason that you have brought that's the reason that you have brought our viewers our viewers our viewers into the know of how AI supply chain into the know of how AI supply chain into the know of how AI supply chain works. So um we usually have six works. So um we usually have six works. So um we usually have six levels of AI general purpose AI levels of AI general purpose AI levels of AI general purpose AI development. The first of all, you you development. The first of all, you you development. The first of all, you you do your your pre-training. And then you do your your pre-training. And then you do your your pre-training. And then you clean that data and then you do your clean that data and then you do your clean that data and then you do your post-training. So in that in that post-training. So in that in that post-training. So in that in that sequence, sequence, sequence, data is collected. And right now they data is collected. And right now they data is collected. And right now they have changed because of a lot of have changed because of a lot of have changed because of a lot of advocacy, but advocacy, but advocacy, but in 2022 to 2023, in 2022 to 2023, in 2022 to 2023, they used to scrap the worst parts of they used to scrap the worst parts of they used to scrap the worst parts of the internet to train uh data. And when the internet to train uh data. And when the internet to train uh data. And when And when to train the AI models. And And when to train the AI models. And And when to train the AI models. And because of the AI race that was then as because of the AI race that was then as because of the AI race that was then as it is still now. So the more data you it is still now. So the more data you it is still now. So the more data you have, the better your model is. They have, the better your model is. They have, the better your model is. They used to be called scaling loss. So used to be called scaling loss. So used to be called scaling loss. So scaling loss is the better data you have scaling loss is the better data you have scaling loss is the better data you have and the more compute you have, the and the more compute you have, the and the more compute you have, the better your model is going to be. So um

better your model is going to be. So um better your model is going to be. So um So a lot of data is is is evil as as you So a lot of data is is is evil as as you So a lot of data is is is evil as as you were talking about. It's bad. It's It's were talking about. It's bad. It's It's were talking about. It's bad. It's It's pornographic data. pornographic data. pornographic data. It's data that even showed people being It's data that even showed people being It's data that even showed people being beheaded. beheaded. beheaded. It's It's It's data from the very dark sides of the data from the very dark sides of the data from the very dark sides of the internet. So now it it produced an internet. So now it it produced an internet. So now it it produced an element called AI psychosis. And again, element called AI psychosis. And again, element called AI psychosis. And again, it's still that the engineer should be it's still that the engineer should be it's still that the engineer should be in the know. So AI psychosis is it's it in the know. So AI psychosis is it's it in the know. So AI psychosis is it's it has not yet been added to the DSM-5, has not yet been added to the DSM-5, has not yet been added to the DSM-5, which is the psychology manual. But which is the psychology manual. But which is the psychology manual. But we're already seeing guys being hooked we're already seeing guys being hooked we're already seeing guys being hooked to AI AI models. You have seen to AI AI models. You have seen to AI AI models. You have seen a lot of suicides because of an AI model a lot of suicides because of an AI model a lot of suicides because of an AI model um um um telling a person you can commit suicide. telling a person you can commit suicide. telling a person you can commit suicide. Suicide is an option for you. Um Suicide is an option for you. Um Suicide is an option for you. Um Attachment, we have seen the the the the Attachment, we have seen the the the the Attachment, we have seen the the the the cases where people have cases where people have cases where people have uh divorced their their their spouses uh divorced their their their spouses uh divorced their their their spouses because an AI model has told this person because an AI model has told this person because an AI model has told this person to divorce this more this person. So, so to divorce this more this person. So, so to divorce this more this person. So, so this is the reason why data annotators this is the reason why data annotators this is the reason why data annotators data workers are needed to clean AI data workers are needed to clean AI data workers are needed to clean AI models so that when you and I interact models so that when you and I interact models so that when you and I interact with a model, it's it's going to be with a model, it's it's going to be with a model, it's it's going to be safe. It's going to be safe. It's going to be safe. It's going to be um um um uh it's not going to harm me at the very uh it's not going to harm me at the very uh it's not going to harm me at the very least. Of course, AI companions are are least. Of course, AI companions are are least. Of course, AI companions are are a different case. So, first of all, it's a different case. So, first of all, it's a different case. So, first of all, it's AI should be in the know. Second, um AI should be in the know. Second, um AI should be in the know. Second, um it's to create a critical mass of it's to create a critical mass of it's to create a critical mass of developers and AI engineers who demand

developers and AI engineers who demand developers and AI engineers who demand or who advocate for fair work within the or who advocate for fair work within the or who advocate for fair work within the AI supply chain. We saw this in um AI supply chain. We saw this in um AI supply chain. We saw this in um during the tobacco uh revolution of the during the tobacco uh revolution of the during the tobacco uh revolution of the 1980s, 1990s, 1980s, 1990s, 1980s, 1990s, we saw this during the uh the blood we saw this during the uh the blood we saw this during the uh the blood diamond diamond diamond uh uh uh scenarios uh where scenarios uh where scenarios uh where diamonds that were used in that have diamonds that were used in that have diamonds that were used in that have been developed in war in war scenarios been developed in war in war scenarios been developed in war in war scenarios uh is not going to be called it's not uh is not going to be called it's not uh is not going to be called it's not going to it's not accepted by consumers going to it's not accepted by consumers going to it's not accepted by consumers in the West. So, and this uh we we have in the West. So, and this uh we we have in the West. So, and this uh we we have countries like Sierra Leone which this countries like Sierra Leone which this countries like Sierra Leone which this uh this kind of advocacy actually led to uh this kind of advocacy actually led to uh this kind of advocacy actually led to less conflict than what we call a less conflict than what we call a less conflict than what we call a resource curse within those countries resource curse within those countries resource curse within those countries and ultimately uh there was peace that and ultimately uh there was peace that and ultimately uh there was peace that was found because uh before then for you was found because uh before then for you was found because uh before then for you to get diamonds, uh they they there was to get diamonds, uh they they there was to get diamonds, uh they they there was war in a given country um for for these war in a given country um for for these war in a given country um for for these diamonds. So, even right now because diamonds. So, even right now because diamonds. So, even right now because these these developers these developers these these developers these developers these these developers these developers that these um data annotators data that these um data annotators data that these um data annotators data workers that we are talking about who workers that we are talking about who workers that we are talking about who are cleaning these models, they're the are cleaning these models, they're the are cleaning these models, they're the ones who are going through the worst ones who are going through the worst ones who are going through the worst traumatic effect because they have to traumatic effect because they have to traumatic effect because they have to view, they have to see all this bad view, they have to see all this bad view, they have to see all this bad content that I have just shared. The content that I have just shared. The content that I have just shared. The beheading, the terrorism, and beheading, the terrorism, and beheading, the terrorism, and everything. So, this means that they everything. So, this means that they everything. So, this means that they themselves are going to be affected themselves are going to be affected themselves are going to be affected mentally. They uh mentally. They uh mentally. They uh Here in Nairobi, we have people who have Here in Nairobi, we have people who have Here in Nairobi, we have people who have uh even committed suicide because of uh even committed suicide because of uh even committed suicide because of that. So, I know some people um because that. So, I know some people um because that. So, I know some people um because I'm involved in um the data workers

I'm involved in um the data workers I'm involved in um the data workers association network. Uh association network. Uh association network. Uh people who have divorced their people who have divorced their people who have divorced their their spouses because of the of the their spouses because of the of the their spouses because of the of the of the psychological torment that they of the psychological torment that they of the psychological torment that they are facing as they are cleaning this are facing as they are cleaning this are facing as they are cleaning this data so that um data so that um data so that um the end product may be good for for for the end product may be good for for for the end product may be good for for for for me who's using ChatGPT ultimately. for me who's using ChatGPT ultimately. for me who's using ChatGPT ultimately. So, So, So, uh this critical mass is important for uh this critical mass is important for uh this critical mass is important for for championing and advocating for fair for championing and advocating for fair for championing and advocating for fair work so that the the the pipeline can go work so that the the the pipeline can go work so that the the the pipeline can go all the way up so that the culture We all the way up so that the culture We all the way up so that the culture We know that um if you want to gauge the know that um if you want to gauge the know that um if you want to gauge the direction of policy in a country, direction of policy in a country, direction of policy in a country, ultimately, we need to go to the culture ultimately, we need to go to the culture ultimately, we need to go to the culture of that place right now. So, sooner or of that place right now. So, sooner or of that place right now. So, sooner or later, policy will follow where the later, policy will follow where the later, policy will follow where the culture is. So, if these developers can culture is. So, if these developers can culture is. So, if these developers can create a critical mass of people create a critical mass of people create a critical mass of people advocating for fair work, policy will advocating for fair work, policy will advocating for fair work, policy will follow. Public sentiments are a powerful follow. Public sentiments are a powerful follow. Public sentiments are a powerful policy changer policy changer policy changer and policy shapers. So, these realities, and policy shapers. So, these realities, and policy shapers. So, these realities, of course, are not known uh or are not of course, are not known uh or are not of course, are not known uh or are not on the forefront. You will not find them on the forefront. You will not find them on the forefront. You will not find them in your front page in your front page in your front page uh magazine because the current AI hype uh magazine because the current AI hype uh magazine because the current AI hype is fueled by AI companies trying to is fueled by AI companies trying to is fueled by AI companies trying to market their products. So, the plight of market their products. So, the plight of market their products. So, the plight of data workers, who are way down the data workers, who are way down the data workers, who are way down the supply chain, is not going to be a supply chain, is not going to be a supply chain, is not going to be a concern for them. It is not a concern concern for them. It is not a concern concern for them. It is not a concern for them. And finally, prayer. So, this for them. And finally, prayer. So, this for them. And finally, prayer. So, this developer I must assume that this developer I must assume that this developer I must assume that this developer is a Christian. So, these developer is a Christian. So, these developer is a Christian. So, these developers, please pray. Prayer is not developers, please pray. Prayer is not developers, please pray. Prayer is not the least that you can do. Prayer is not the least that you can do. Prayer is not the least that you can do. Prayer is not even the the only thing that you can do.

even the the only thing that you can do. even the the only thing that you can do. pray is the most you can do. We serve a pray is the most you can do. We serve a pray is the most you can do. We serve a God of justice who has commanded us to God of justice who has commanded us to God of justice who has commanded us to pray and he has sovereignly decreed that pray and he has sovereignly decreed that pray and he has sovereignly decreed that um um um we have not because we ask not. So, pray we have not because we ask not. So, pray we have not because we ask not. So, pray for fair work within the AI supply for fair work within the AI supply for fair work within the AI supply chain. chain. chain. >> I imagine there will be people listening >> I imagine there will be people listening >> I imagine there will be people listening to this or watching this who have been to this or watching this who have been to this or watching this who have been using one large language model or using one large language model or using one large language model or another for a while another for a while another for a while and have never heard what you just said. and have never heard what you just said. and have never heard what you just said. Have never realized that there are Have never realized that there are Have never realized that there are people who have committed suicide or people who have committed suicide or people who have committed suicide or who have who have who have gone through divorces or or um gone through divorces or or um gone through divorces or or um had significant had significant had significant mental mental mental issues issues issues as a result of cleaning this data and as a result of cleaning this data and as a result of cleaning this data and the question they're asking now the question they're asking now the question they're asking now is is is well, should I just stop using these well, should I just stop using these well, should I just stop using these models then? Or how bad does it need to models then? Or how bad does it need to models then? Or how bad does it need to get before I consider stopping using get before I consider stopping using get before I consider stopping using them? So, I'm incredibly grateful for them? So, I'm incredibly grateful for them? So, I'm incredibly grateful for what you said about prayer what you said about prayer what you said about prayer um and that is that is a huge call to um and that is that is a huge call to um and that is that is a huge call to action.

action. action. But, what other But, what other But, what other action action action should an an individual user make? So, should an an individual user make? So, should an an individual user make? So, if someone is contemplating, you know, if someone is contemplating, you know, if someone is contemplating, you know, am I going to get up tomorrow and am I going to get up tomorrow and am I going to get up tomorrow and actually use ChatGPT or not? What would actually use ChatGPT or not? What would actually use ChatGPT or not? What would your advice be? your advice be? your advice be? And and then thinking more broadly And and then thinking more broadly And and then thinking more broadly what action do you think that the what action do you think that the what action do you think that the Christian church or Christian Christian church or Christian Christian church or Christian denominations might be able to take in denominations might be able to take in denominations might be able to take in this space? this space? this space? >> Yeah, so >> Yeah, so >> Yeah, so um um um for the for the individual Christian for the for the individual Christian for the for the individual Christian uh there's you can advocate for models uh there's you can advocate for models uh there's you can advocate for models that have been safely developed. Um so, that have been safely developed. Um so, that have been safely developed. Um so, there are some there are some companies there are some there are some companies there are some there are some companies which which champion safe safe AI. Of which which champion safe safe AI. Of which which champion safe safe AI. Of course, I wouldn't mention them because course, I wouldn't mention them because course, I wouldn't mention them because I would be advertising them. Uh but, I would be advertising them. Uh but, I would be advertising them. Uh but, there are some companies that there are some companies that there are some companies that advocate for safe AI. Uh So, you can do advocate for safe AI. Uh So, you can do advocate for safe AI. Uh So, you can do a background research on how these AI a background research on how these AI a background research on how these AI models developed across the six models developed across the six models developed across the six levels of general purpose AI development levels of general purpose AI development levels of general purpose AI development because it should be public and because it should be public and because it should be public and because the data is not the one that is because the data is not the one that is because the data is not the one that is public, but how they came up with the public, but how they came up with the public, but how they came up with the final product, we can always come up final product, we can always come up final product, we can always come up with that.

with that. with that. So, So, So, use the use the use an AI model that has use the use the use an AI model that has use the use the use an AI model that has been safely developed. Use an AI model been safely developed. Use an AI model been safely developed. Use an AI model that that that that has safety at its core and by that has safety at its core and by that has safety at its core and by safety I mean safety by design, not just safety I mean safety by design, not just safety I mean safety by design, not just safe when you're using it. So, has it safe when you're using it. So, has it safe when you're using it. So, has it been decided safely? Has Is there fair been decided safely? Has Is there fair been decided safely? Has Is there fair work? And by fair work, I mean are there work? And by fair work, I mean are there work? And by fair work, I mean are there fair contracting? fair contracting? fair contracting? Are they given fair contracts? The Are they given fair contracts? The Are they given fair contracts? The digital workers that we are mentioning. digital workers that we are mentioning. digital workers that we are mentioning. Was there fair representation? Because Was there fair representation? Because Was there fair representation? Because the Kenyan workers are and Nigerian the Kenyan workers are and Nigerian the Kenyan workers are and Nigerian workers were denied representation. They workers were denied representation. They workers were denied representation. They could not could not could not come together and come together and come together and and advocate for fair work as a group. and advocate for fair work as a group. and advocate for fair work as a group. Is there fair pay? Are they paying them Is there fair pay? Are they paying them Is there fair pay? Are they paying them more than like more than like more than like Kenya? For example, in Kenya, we have Kenya? For example, in Kenya, we have Kenya? For example, in Kenya, we have basic minimum wage. So, they you're not basic minimum wage. So, they you're not basic minimum wage. So, they you're not supposed to pay somebody below the supposed to pay somebody below the supposed to pay somebody below the minimum wage.

minimum wage. minimum wage. Are they adhering to this? Um Are they adhering to this? Um Are they adhering to this? Um Are they Are they Are they Are they advocating for fair uh Are they advocating for fair uh Are they advocating for fair uh uh uh uh uh uh uh fair fair fair fair fair fair management? Fair management is also key management? Fair management is also key management? Fair management is also key because right now they're actually because right now they're actually because right now they're actually putting chatbots to as your as your as putting chatbots to as your as your as putting chatbots to as your as your as your supervisor. Um but it should be a your supervisor. Um but it should be a your supervisor. Um but it should be a human being. So, human being. So, human being. So, as an attending individual Christian as an attending individual Christian as an attending individual Christian um look at how an AI model was developed um look at how an AI model was developed um look at how an AI model was developed and use an AI model that has been safely and use an AI model that has been safely and use an AI model that has been safely developed for the church for the for the developed for the church for the for the developed for the church for the for the Christian church Christian church Christian church um um um come up with a statement a policy a come up with a statement a policy a come up with a statement a policy a guideline guideline guideline that that that that says that this is where we stand in that says that this is where we stand in that says that this is where we stand in in safe AI development and this is how in safe AI development and this is how in safe AI development and this is how we we we we we counsel our members we we counsel our members we we counsel our members remember about the authority of the remember about the authority of the remember about the authority of the church there's a book called Jonathan church there's a book called Jonathan church there's a book called Jonathan Lehman and called authority and in that Lehman and called authority and in that Lehman and called authority and in that book book book he says that he says that he says that the the church has authority of council the the church has authority of council the the church has authority of council but then but then but then the the the the church has authority of command but the church has authority of command but the church has authority of command but then the elders have authority of then the elders have authority of then the elders have authority of council so authority of council means council so authority of council means council so authority of council means that that that it's like a husband you I counsel you it's like a husband you I counsel you it's like a husband you I counsel you but I cannot follow up my my authority but I cannot follow up my my authority but I cannot follow up my my authority with consequence but then the church with consequence but then the church with consequence but then the church because because because the church has authority of command the church has authority of command the church has authority of command because because because they can follow up with consequence

they can follow up with consequence they can follow up with consequence namely excommunication namely excommunication namely excommunication but but but so for the elders and the church they so for the elders and the church they so for the elders and the church they can they can counsel their members to can they can counsel their members to can they can counsel their members to to use AI models that have been to use AI models that have been to use AI models that have been developed safely and maybe then come up developed safely and maybe then come up developed safely and maybe then come up with a benchmark of these are the AI with a benchmark of these are the AI with a benchmark of these are the AI models that we found that models that we found that models that we found that that champion human dignity that champion human dignity that champion human dignity and we can use them and as I had and we can use them and as I had and we can use them and as I had mentioned earlier when it becomes a mentioned earlier when it becomes a mentioned earlier when it becomes a culture it ultimately goes to become culture it ultimately goes to become culture it ultimately goes to become policy because policy reflects what is policy because policy reflects what is policy because policy reflects what is already there in the ground already there in the ground already there in the ground >> That's incredibly helpful thank you I'm >> That's incredibly helpful thank you I'm >> That's incredibly helpful thank you I'm sure that'll be a a sure that'll be a a sure that'll be a a a very important a very important a very important sort of steer for for many people who sort of steer for for many people who sort of steer for for many people who listening for listening for listening for for me as well thank you for for sharing for me as well thank you for for sharing for me as well thank you for for sharing those thoughts I those thoughts I those thoughts I I want to pick up on a term that you I want to pick up on a term that you I want to pick up on a term that you used in an article that you wrote you used in an article that you wrote you used in an article that you wrote you you talked about a digital colonialism. you talked about a digital colonialism. you talked about a digital colonialism. I wonder if you could explain what you I wonder if you could explain what you I wonder if you could explain what you mean by that term and how you see AI mean by that term and how you see AI mean by that term and how you see AI contributing to this digital contributing to this digital contributing to this digital colonialism.

colonialism. colonialism. >> Yeah, so um >> Yeah, so um >> Yeah, so um It's always interesting when It's always interesting when It's always interesting when you're discussing this to first of all you're discussing this to first of all you're discussing this to first of all mention that we are all sinful. Every mention that we are all sinful. Every mention that we are all sinful. Every human being I am sinful human being I am sinful human being I am sinful and my next door neighbor is sinful and and my next door neighbor is sinful and and my next door neighbor is sinful and the only reason why the only reason why the only reason why any of us can be called saints is any of us can be called saints is any of us can be called saints is because Jesus has because Jesus has because Jesus has given us his righteousness. It's an given us his righteousness. It's an given us his righteousness. It's an alien righteousness that we wear alien righteousness that we wear alien righteousness that we wear that we can only be considered saints. that we can only be considered saints. that we can only be considered saints. And for anybody who's not a Christian And for anybody who's not a Christian And for anybody who's not a Christian equal he's he's a sinner fully sinner. equal he's he's a sinner fully sinner. equal he's he's a sinner fully sinner. But me and you who are Christians we are But me and you who are Christians we are But me and you who are Christians we are we are saints who sometimes we we are we are saints who sometimes we we are we are saints who sometimes we we are saints but we also have the fallenness saints but we also have the fallenness saints but we also have the fallenness in us. We still sin. in us. We still sin. in us. We still sin. So we still remain to be sinners. Though So we still remain to be sinners. Though So we still remain to be sinners. Though ultimately we are saints. God looks ultimately we are saints. God looks ultimately we are saints. God looks looks at us as saints. So even as I'm looks at us as saints. So even as I'm looks at us as saints. So even as I'm discussing digital colonialism this is discussing digital colonialism this is discussing digital colonialism this is where I am. We are not where I am. We are not where I am. We are not calling other people more sinful than calling other people more sinful than calling other people more sinful than us. We we do not want to become us. We we do not want to become us. We we do not want to become pharisaic in that. But nevertheless we pharisaic in that. But nevertheless we pharisaic in that. But nevertheless we must talk about we must talk about must talk about we must talk about must talk about we must talk about imperialism because digital colonialism imperialism because digital colonialism imperialism because digital colonialism falls within the larger bracket of falls within the larger bracket of falls within the larger bracket of imperialism which is namely a imperialism which is namely a imperialism which is namely a a power domineering over the political, a power domineering over the political, a power domineering over the political, economic and social affairs of another economic and social affairs of another economic and social affairs of another people.

people. people. And technology has always been the fuel And technology has always been the fuel And technology has always been the fuel behind imperialism. So behind imperialism. So behind imperialism. So going back to the first industrial going back to the first industrial going back to the first industrial revolution you would remember the steam revolution you would remember the steam revolution you would remember the steam engine the textile engine the textile engine the textile which led to better ships and led to a which led to better ships and led to a which led to better ships and led to a demand for workers in the cotton farms. demand for workers in the cotton farms. demand for workers in the cotton farms. And this now fueled slavery, which was And this now fueled slavery, which was And this now fueled slavery, which was the first uh imperialism that we saw in the first uh imperialism that we saw in the first uh imperialism that we saw in the in the recent past. Of course, it the in the recent past. Of course, it the in the recent past. Of course, it has been so many others, but because we has been so many others, but because we has been so many others, but because we are looking at it from the lens of are looking at it from the lens of are looking at it from the lens of technology. I argue that, and this is technology. I argue that, and this is technology. I argue that, and this is something that has been argued all something that has been argued all something that has been argued all along, that technology is the fuel along, that technology is the fuel along, that technology is the fuel behind um behind um behind um modern-day modern-day modern-day imperialism. And of course, that's why imperialism. And of course, that's why imperialism. And of course, that's why they're called um industrial revolutions they're called um industrial revolutions they're called um industrial revolutions because we have technology at its core. because we have technology at its core. because we have technology at its core. In the second industrial revolution, In the second industrial revolution, In the second industrial revolution, this was the era of colonialism. this was the era of colonialism. this was the era of colonialism. Imperialism now became how it was Imperialism now became how it was Imperialism now became how it was manifested was through colonialism. manifested was through colonialism. manifested was through colonialism. Electricity, the steel, dominant Electricity, the steel, dominant Electricity, the steel, dominant technologies of this era was electricity technologies of this era was electricity technologies of this era was electricity and steel. Massive technology massive and steel. Massive technology massive and steel. Massive technology massive factories. Europeans needed market for factories. Europeans needed market for factories. Europeans needed market for their finished products. And also, they their finished products. And also, they their finished products. And also, they needed raw materials for their for their needed raw materials for their for their needed raw materials for their for their products. So, they needed So, that's uh products. So, they needed So, that's uh products. So, they needed So, that's uh So, So, that's why they wouldn't take So, So, that's why they wouldn't take So, So, that's why they wouldn't take uh slaves. Of course, we had the the uh slaves. Of course, we had the the uh slaves. Of course, we had the the influence of William Wilberforce, who influence of William Wilberforce, who influence of William Wilberforce, who advocated for the end of slavery. We had advocated for the end of slavery. We had advocated for the end of slavery. We had missionaries like David Livingstone, who missionaries like David Livingstone, who missionaries like David Livingstone, who was so appalled by Arab slavery by was so appalled by Arab slavery by was so appalled by Arab slavery by uh here in East Africa, that they uh here in East Africa, that they uh here in East Africa, that they rallied for legitimate trade. Um but,

rallied for legitimate trade. Um but, rallied for legitimate trade. Um but, it's also well documented that one of it's also well documented that one of it's also well documented that one of the biggest reasons why the biggest reasons why the biggest reasons why um um um the British Parliament moved away from the British Parliament moved away from the British Parliament moved away from um slavery to colonialism to legitimate um slavery to colonialism to legitimate um slavery to colonialism to legitimate trade was the need for raw materials and trade was the need for raw materials and trade was the need for raw materials and the need for markets. So, they needed a the need for markets. So, they needed a the need for markets. So, they needed a place where they are going to take their place where they are going to take their place where they are going to take their markets their finished products. markets their finished products. markets their finished products. Um and that's why they needed Um and that's why they needed Um and that's why they needed colonialism colonial states. So, again, colonialism colonial states. So, again, colonialism colonial states. So, again, um technology was the fuel. We had um technology was the fuel. We had um technology was the fuel. We had electricity, we had steel. They had electricity, we had steel. They had electricity, we had steel. They had superior technologies uh that rival. superior technologies uh that rival. superior technologies uh that rival. That's why they could uh they could be That's why they could uh they could be That's why they could uh they could be an imperial power to to Africa. And in an imperial power to to Africa. And in an imperial power to to Africa. And in the third industrial revolution, it was the third industrial revolution, it was the third industrial revolution, it was the dot dot-com era, the era of the dot dot-com era, the era of the dot dot-com era, the era of computing technologies. Um Um computing technologies. Um Um computing technologies. Um Um uh Formal colonialism had ended in most uh Formal colonialism had ended in most uh Formal colonialism had ended in most countries by 1960, but what persisted is countries by 1960, but what persisted is countries by 1960, but what persisted is what what what Martin uh Kwame Nkrumah, who was the Martin uh Kwame Nkrumah, who was the Martin uh Kwame Nkrumah, who was the first president of Ghana, he described first president of Ghana, he described first president of Ghana, he described it as neocolonialism. In this period, we it as neocolonialism. In this period, we it as neocolonialism. In this period, we had programs like the structural had programs like the structural had programs like the structural adjustment programs uh SAPs that adjustment programs uh SAPs that adjustment programs uh SAPs that relegated the continent to being raw relegated the continent to being raw relegated the continent to being raw material producers rather than a material producers rather than a material producers rather than a consumer. So, we also saw a lot of brain consumer. So, we also saw a lot of brain consumer. So, we also saw a lot of brain drain during that time. We saw the drain during that time. We saw the drain during that time. We saw the resource curse. I talked about Sierra resource curse. I talked about Sierra resource curse. I talked about Sierra Leone and the diamonds. We have Leone and the diamonds. We have Leone and the diamonds. We have countries like DRC. Um countries like DRC. Um countries like DRC. Um So, um a country which has had resources So, um a country which has had resources So, um a country which has had resources outside imperial powers would would outside imperial powers would would outside imperial powers would would benefit from those resources by benefit from those resources by benefit from those resources by um by doing very ungodly things. And um

um by doing very ungodly things. And um um by doing very ungodly things. And um yeah, so and now we are in the fourth yeah, so and now we are in the fourth yeah, so and now we are in the fourth industrial revolution. It's called the industrial revolution. It's called the industrial revolution. It's called the industry 4.0 industry 4.0 industry 4.0 by an economist from World Economic by an economist from World Economic by an economist from World Economic Forum. So, here we have AI, we have Forum. So, here we have AI, we have Forum. So, here we have AI, we have Internet of Things, we have blockchains. Internet of Things, we have blockchains. Internet of Things, we have blockchains. And now the fundamental question we must And now the fundamental question we must And now the fundamental question we must ask ourselves is that is there still ask ourselves is that is there still ask ourselves is that is there still imperialism that manifests in this era? imperialism that manifests in this era? imperialism that manifests in this era? Uh is there still digital colonialism? Uh is there still digital colonialism? Uh is there still digital colonialism? Uh coming now finally back to the Uh coming now finally back to the Uh coming now finally back to the question. Well, one way that I would question. Well, one way that I would question. Well, one way that I would argue is that argue is that argue is that yes, there is there is digital yes, there is there is digital yes, there is there is digital colonialism. I talked about the resource colonialism. I talked about the resource colonialism. I talked about the resource curse. Um curse. Um curse. Um uh DRC specific example, uh produces uh DRC specific example, uh produces uh DRC specific example, uh produces more than 70% of cobalt. And cobalt is more than 70% of cobalt. And cobalt is more than 70% of cobalt. And cobalt is the the the is the raw material that is behind a lot is the raw material that is behind a lot is the raw material that is behind a lot of computing technologies, um the of computing technologies, um the of computing technologies, um the smartphone, uh for example. So, um one smartphone, uh for example. So, um one smartphone, uh for example. So, um one way that I would argue is the way that I would argue is the way that I would argue is the disposition of um disposition of um disposition of um that country's um technology or that that country's um technology or that that country's um technology or that country's raw material, cobalt from DRC. country's raw material, cobalt from DRC. country's raw material, cobalt from DRC. And we are also seeing a lot of And we are also seeing a lot of And we are also seeing a lot of disposition of data, land, and labor.

disposition of data, land, and labor. disposition of data, land, and labor. I've already discussed about labor, the I've already discussed about labor, the I've already discussed about labor, the one that we discussed about data one that we discussed about data one that we discussed about data workers, data annotators. That's a form workers, data annotators. That's a form workers, data annotators. That's a form of imperialism. But, we are seeing we we of imperialism. But, we are seeing we we of imperialism. But, we are seeing we we are seeing a push or a movement away are seeing a push or a movement away are seeing a push or a movement away from the states being the imperial power from the states being the imperial power from the states being the imperial power to the corporate being the imperial to the corporate being the imperial to the corporate being the imperial power. So, the corporate now is the one power. So, the corporate now is the one power. So, the corporate now is the one that is dispossessing that is dispossessing that is dispossessing um Africans of their data, their land, um Africans of their data, their land, um Africans of their data, their land, and their and their and their labor. And and their and their and their labor. And and their and their and their labor. And it's in it's important for the world to it's in it's important for the world to it's in it's important for the world to remember that we are sinful. We are not remember that we are sinful. We are not remember that we are sinful. We are not uh we are uh we are uh we are we are not taking this almost laying we are not taking this almost laying we are not taking this almost laying down. We we are also perpetrators of all down. We we are also perpetrators of all down. We we are also perpetrators of all this. But, ultimately, we must also call this. But, ultimately, we must also call this. But, ultimately, we must also call sin for what it is. And sin for what it is. And sin for what it is. And uh for land, we see uh let me give you uh for land, we see uh let me give you uh for land, we see uh let me give you the the the the scenario of carbon setting. Um the scenario of carbon setting. Um the scenario of carbon setting. Um Uh global companies need to offset their Uh global companies need to offset their Uh global companies need to offset their data uh their carbon when because of a data uh their carbon when because of a data uh their carbon when because of a meeting. Uh data centers are some of the meeting. Uh data centers are some of the meeting. Uh data centers are some of the biggest emitters of biggest emitters of biggest emitters of of carbon we have. So, what do they do?

of carbon we have. So, what do they do? of carbon we have. So, what do they do? So, they need to offset that carbon. So, So, they need to offset that carbon. So, So, they need to offset that carbon. So, they have to come So, they they they they have to come So, they they they they have to come So, they they they usually come to our countries like usually come to our countries like usually come to our countries like Africa, um continents like Africa, so Africa, um continents like Africa, so Africa, um continents like Africa, so that we will we will give them land, and that we will we will give them land, and that we will we will give them land, and then they then they then they they close off a certain uh portion of they close off a certain uh portion of they close off a certain uh portion of land, and then this land is going to land, and then this land is going to land, and then this land is going to offset the it's going to produce the offset the it's going to produce the offset the it's going to produce the oxygen that is going to offset the oxygen that is going to offset the oxygen that is going to offset the carbon that they are releasing at data carbon that they are releasing at data carbon that they are releasing at data centers. Now, that would be all great centers. Now, that would be all great centers. Now, that would be all great and amazing, only that the land that is and amazing, only that the land that is and amazing, only that the land that is taken here uh in in Kenya, for example, taken here uh in in Kenya, for example, taken here uh in in Kenya, for example, is pastoralist land. And this land um is is pastoralist land. And this land um is is pastoralist land. And this land um is fenced off. So, the people who graze fenced off. So, the people who graze fenced off. So, the people who graze from this land because uh we have a lot from this land because uh we have a lot from this land because uh we have a lot of our uh people who are pastoralist and of our uh people who are pastoralist and of our uh people who are pastoralist and harders. So, they are we harders. So, they are we harders. So, they are we uh there's a documentary uh there's a documentary uh there's a documentary um that that shows how cows die uh along um that that shows how cows die uh along um that that shows how cows die uh along the fence just because they cannot get the fence just because they cannot get the fence just because they cannot get through the through the through the the most fertile land because there's a the most fertile land because there's a the most fertile land because there's a carbon offsetting scheme that is there carbon offsetting scheme that is there carbon offsetting scheme that is there that is being used to offset data center that is being used to offset data center that is being used to offset data center somewhere in the US.

somewhere in the US. somewhere in the US. That's one way that That's one way that That's one way that that we see digital colonialism through that we see digital colonialism through that we see digital colonialism through the disposition of our land. the disposition of our land. the disposition of our land. Our labor I've discussed about labor and Our labor I've discussed about labor and Our labor I've discussed about labor and data workers and data annotators. For data workers and data annotators. For data workers and data annotators. For for data, which is the third element for data, which is the third element for data, which is the third element that we are seeing imperialism by the that we are seeing imperialism by the that we are seeing imperialism by the corporate right now, corporate right now, corporate right now, we see a lot of our data being taken. we see a lot of our data being taken. we see a lot of our data being taken. Um Um Um Uh for example, let me give one example. Uh for example, let me give one example. Uh for example, let me give one example. There's a company called Worldcoin There's a company called Worldcoin There's a company called Worldcoin that came here in Africa that was taking that came here in Africa that was taking that came here in Africa that was taking our iris. our iris. our iris. Um like people used to line up and then Um like people used to line up and then Um like people used to line up and then iris of an eye is taken and then it's iris of an eye is taken and then it's iris of an eye is taken and then it's used to to trade computer vision models used to to trade computer vision models used to to trade computer vision models in the US. And in the US. And in the US. And ultimately this company was kicked out ultimately this company was kicked out ultimately this company was kicked out because it did not adhere to uh Kenya's because it did not adhere to uh Kenya's because it did not adhere to uh Kenya's data protection laws. data protection laws. data protection laws. Uh but then that's a form of data data Uh but then that's a form of data data Uh but then that's a form of data data extraction, data exploitation. But they extraction, data exploitation. But they extraction, data exploitation. But they usually go to a to a jurisdiction usually go to a to a jurisdiction usually go to a to a jurisdiction um a corporate goes to a jurisdiction um um a corporate goes to a jurisdiction um um a corporate goes to a jurisdiction um and then they illegally mine data and then they illegally mine data and then they illegally mine data without consulting the the the the without consulting the the the the without consulting the the the the authorities there.

authorities there. authorities there. Um so uh so those are some of the the Um so uh so those are some of the the Um so uh so those are some of the the ways that we see imperialism manifesting ways that we see imperialism manifesting ways that we see imperialism manifesting today. today. today. >> That's a >> That's a >> That's a incredibly helpful overview. Um incredibly helpful overview. Um incredibly helpful overview. Um what was it? Land, what was it? Land, what was it? Land, labor, and data. labor, and data. labor, and data. >> Yeah. >> Yeah. >> Yeah. >> They the three points of that triangle. >> They the three points of that triangle. >> They the three points of that triangle. Um that's really really helpful. Thank Um that's really really helpful. Thank Um that's really really helpful. Thank you. It in the light of that triangle, you. It in the light of that triangle, you. It in the light of that triangle, um I came across this quotation recently um I came across this quotation recently um I came across this quotation recently and I I'd love to hear your reaction to and I I'd love to hear your reaction to and I I'd love to hear your reaction to it. It's from the it. It's from the it. It's from the um um um author Malcolm Gladwell, as it happens. author Malcolm Gladwell, as it happens. author Malcolm Gladwell, as it happens. And I'm not particularly getting at And I'm not particularly getting at And I'm not particularly getting at Malcolm Gladwell, but he said that the Malcolm Gladwell, but he said that the Malcolm Gladwell, but he said that the the clearest win in the AI revolution the clearest win in the AI revolution the clearest win in the AI revolution is its use in the majority world, um is its use in the majority world, um is its use in the majority world, um giving people, for example, the best giving people, for example, the best giving people, for example, the best medical advice or using it for medical medical advice or using it for medical medical advice or using it for medical diagnosis. diagnosis. diagnosis. How do you respond to to that particular How do you respond to to that particular How do you respond to to that particular idea? idea? idea? >> Yeah, uh >> Yeah, uh >> Yeah, uh uh uh uh global majority global majority global majority is what's uh is what's uh is what's uh historically was called global south.

historically was called global south. historically was called global south. So, by Malcolm Gladwell saying the So, by Malcolm Gladwell saying the So, by Malcolm Gladwell saying the global majority means uh you know, global majority means uh you know, global majority means uh you know, Africa, he means uh Latin America, he Africa, he means uh Latin America, he Africa, he means uh Latin America, he means uh East Asia. means uh East Asia. means uh East Asia. Um so, yes. Um Um so, yes. Um Um so, yes. Um So, what his his argument is coming from So, what his his argument is coming from So, what his his argument is coming from the argument of frontier AI models the argument of frontier AI models the argument of frontier AI models um as opposed to um um as opposed to um um as opposed to um AI models that uh are very narrow and so AI models that uh are very narrow and so AI models that uh are very narrow and so specific problem. So, uh frontier AI specific problem. So, uh frontier AI specific problem. So, uh frontier AI models push the boundaries of knowledge models push the boundaries of knowledge models push the boundaries of knowledge and um and um and um and it's what could be, but then in and it's what could be, but then in and it's what could be, but then in Africa and in the global majority, we Africa and in the global majority, we Africa and in the global majority, we look at what is and ultimately, look at what is and ultimately, look at what is and ultimately, technology at as its very definition is technology at as its very definition is technology at as its very definition is making work easier, you know, uh making work easier, you know, uh making work easier, you know, uh something that helps me to move from something that helps me to move from something that helps me to move from point A to point B. Uh for example, the point A to point B. Uh for example, the point A to point B. Uh for example, the wheel. Uh so, for technology for AI, I wheel. Uh so, for technology for AI, I wheel. Uh so, for technology for AI, I agree with him because agree with him because agree with him because it's it needs to solve our health it's it needs to solve our health it's it needs to solve our health problems, it needs to solve our problems, it needs to solve our problems, it needs to solve our agriculture problems, it needs to solve agriculture problems, it needs to solve agriculture problems, it needs to solve our our education problems, and we have our our education problems, and we have our our education problems, and we have a lot of AI models that are very a lot of AI models that are very a lot of AI models that are very specific to those. Um specific to those. Um specific to those. Um uh uh uh as as opposed to amusing ourselves to as as opposed to amusing ourselves to as as opposed to amusing ourselves to death. There's a whole book called death. There's a whole book called death. There's a whole book called Amusing Ourselves to Death, where we um Amusing Ourselves to Death, where we um Amusing Ourselves to Death, where we um uh the whole deepfake scenarios, it's uh the whole deepfake scenarios, it's uh the whole deepfake scenarios, it's not really helping us advance not really helping us advance not really helping us advance economically unless you're a content economically unless you're a content economically unless you're a content creator.

creator. creator. Uh so uh Uh so uh Uh so uh but if I would develop an AI model and but if I would develop an AI model and but if I would develop an AI model and and and those are some of the things and and those are some of the things and and those are some of the things that even we do even where I sit. that even we do even where I sit. that even we do even where I sit. Whenever Whenever Whenever um a developer, for example, comes comes um a developer, for example, comes comes um a developer, for example, comes comes and tells, "I have an idea." So, we and tells, "I have an idea." So, we and tells, "I have an idea." So, we usually look at how is this idea how is usually look at how is this idea how is usually look at how is this idea how is this AI model? What is how is where is this AI model? What is how is where is this AI model? What is how is where is it going to get the data that is going it going to get the data that is going it going to get the data that is going to solve specific problems in the to solve specific problems in the to solve specific problems in the transport industry, for example? Because transport industry, for example? Because transport industry, for example? Because they're not short of problems. You are they're not short of problems. You are they're not short of problems. You are not short of challenges as a global not short of challenges as a global not short of challenges as a global majority. So, we need AI that is going majority. So, we need AI that is going majority. So, we need AI that is going to solve our problems as opposed to AI to solve our problems as opposed to AI to solve our problems as opposed to AI that is going to amuse ourselves. that is going to amuse ourselves. that is going to amuse ourselves. >> AI is often presented, isn't it? >> AI is often presented, isn't it? >> AI is often presented, isn't it? Perhaps especially by the the AI Perhaps especially by the the AI Perhaps especially by the the AI corporations as a democratizing force. corporations as a democratizing force. corporations as a democratizing force. Um especially in areas like, you know, Um especially in areas like, you know, Um especially in areas like, you know, health care and education. health care and education. health care and education. As as someone who's As as someone who's As as someone who's involved in the world of education, do involved in the world of education, do involved in the world of education, do you see real promise there? That AI can you see real promise there? That AI can you see real promise there? That AI can act as a democratizing force? We've act as a democratizing force? We've act as a democratizing force? We've talked a lot about the dangers. I I'm talked a lot about the dangers. I I'm talked a lot about the dangers. I I'm just trying to explore just trying to explore just trying to explore where do you see, if if anywhere, the where do you see, if if anywhere, the where do you see, if if anywhere, the the advantages or the the the the real the advantages or the the the the real the advantages or the the the the real promise, the real hope in AI?

promise, the real hope in AI? promise, the real hope in AI? >> Yeah, no it's it's it's it's it's it >> Yeah, no it's it's it's it's it's it >> Yeah, no it's it's it's it's it's it literally democratizes literally democratizes literally democratizes a lot of knowledge especially when it's a lot of knowledge especially when it's a lot of knowledge especially when it's the data is clean and when it's um it's the data is clean and when it's um it's the data is clean and when it's um it's used in a very it used in a very it used in a very it um transparent manner. And when um transparent manner. And when um transparent manner. And when especially I can get the links to especially I can get the links to especially I can get the links to whichever output that it's giving me. whichever output that it's giving me. whichever output that it's giving me. So, um So, um So, um I truly believe in the democratization I truly believe in the democratization I truly believe in the democratization effect of AI. Of course, I also believe effect of AI. Of course, I also believe effect of AI. Of course, I also believe that there's an AI snake oil that is that there's an AI snake oil that is that there's an AI snake oil that is going out there. Again, there's a book going out there. Again, there's a book going out there. Again, there's a book called AI snake oil that our readers called AI snake oil that our readers called AI snake oil that our readers should read where um AI is sold to us as should read where um AI is sold to us as should read where um AI is sold to us as the panacea, the the solution to all our the panacea, the the solution to all our the panacea, the the solution to all our problems. Um, problems. Um, problems. Um, techno-solutionism. techno-solutionism. techno-solutionism. Uh, Uh, Uh, that's uh, so no, it's not going to that's uh, so no, it's not going to that's uh, so no, it's not going to solve all our problems, but it is going solve all our problems, but it is going solve all our problems, but it is going to democratize knowledge to a very large to democratize knowledge to a very large to democratize knowledge to a very large extent. And um, and we should we should extent. And um, and we should we should extent. And um, and we should we should tap into that as as long as we know tap into that as as long as we know tap into that as as long as we know where it's where this data is coming where it's where this data is coming where it's where this data is coming from. This this comes to uh, whether we from. This this comes to uh, whether we from. This this comes to uh, whether we we should trust data generated by AI or we should trust data generated by AI or we should trust data generated by AI or output generated by AI. Um, output generated by AI. Um, output generated by AI. Um, which I believe that there's an element which I believe that there's an element which I believe that there's an element of that. If you can use it as a as an of that. If you can use it as a as an of that. If you can use it as a as an adversary, for example, let me adversary, for example, let me adversary, for example, let me let let me tell you how I how I how I let let me tell you how I how I how I let let me tell you how I how I how I usually use AI, uh, especially when uh, usually use AI, uh, especially when uh, usually use AI, uh, especially when uh, we're doing a lot of AI literacy we're doing a lot of AI literacy we're doing a lot of AI literacy uh, campaigns in the continent. So we uh, campaigns in the continent. So we uh, campaigns in the continent. So we usually uh, usually uh, usually uh, ask a student to develop a content using ask a student to develop a content using ask a student to develop a content using AI,

AI, AI, uh, develop anything using AI. For uh, develop anything using AI. For uh, develop anything using AI. For example, photosynthesis. Tell me how the example, photosynthesis. Tell me how the example, photosynthesis. Tell me how the process of photosynthesis happens. And process of photosynthesis happens. And process of photosynthesis happens. And then, whatever output is going to then, whatever output is going to then, whatever output is going to happen, then this student now goes in happen, then this student now goes in happen, then this student now goes in front of the class, and then front of the class, and then front of the class, and then they critique the output of this they critique the output of this they critique the output of this um, um, um, generative AI. So you are very generative AI. So you are very generative AI. So you are very adversarial. And then you now develop a adversarial. And then you now develop a adversarial. And then you now develop a better paper using like handwritten that better paper using like handwritten that better paper using like handwritten that uh, that is better than this generated uh, that is better than this generated uh, that is better than this generated AI. So you see these are these are level AI. So you see these are these are level AI. So you see these are these are level of democratization of knowledge because of democratization of knowledge because of democratization of knowledge because you um, you are you are able to push you um, you are you are able to push you um, you are you are able to push back on whatever content that this has back on whatever content that this has back on whatever content that this has been given, but you're also able to to been given, but you're also able to to been given, but you're also able to to critically think, critically think, critically think, uh, and develop a better content out of uh, and develop a better content out of uh, and develop a better content out of that. But there's an AI snake oil. We that. But there's an AI snake oil. We that. But there's an AI snake oil. We should be very concerned about people should be very concerned about people should be very concerned about people who are who are telling us that AI is who are who are telling us that AI is who are who are telling us that AI is the solution to all our problems, the solution to all our problems, the solution to all our problems, because it's it's not. We have very many because it's it's not. We have very many because it's it's not. We have very many fundamental problems as human beings.

fundamental problems as human beings. fundamental problems as human beings. Um the chief one being sin, Um the chief one being sin, Um the chief one being sin, uh and then also very many human uh and then also very many human uh and then also very many human problems that the fall has uh has problems that the fall has uh has problems that the fall has uh has given us. given us. given us. >> If you could take all of this wisdom >> If you could take all of this wisdom >> If you could take all of this wisdom that you've been sharing with us and sit that you've been sharing with us and sit that you've been sharing with us and sit down next to someone at at Open AI or or down next to someone at at Open AI or or down next to someone at at Open AI or or Google or Anthropic who's developing the Google or Anthropic who's developing the Google or Anthropic who's developing the next generation of AI models and you next generation of AI models and you next generation of AI models and you had, I don't know, had, I don't know, had, I don't know, 3 minutes to to speak to them and to to 3 minutes to to speak to them and to to 3 minutes to to speak to them and to to try and try and try and you plant in their mind the the you plant in their mind the the you plant in their mind the the questions or the thoughts that they sh- questions or the thoughts that they sh- questions or the thoughts that they sh- they should be having they should be having they should be having as they develop this next model, what as they develop this next model, what as they develop this next model, what would you want those people to be would you want those people to be would you want those people to be thinking about? thinking about? thinking about? >> point them to C.S. Lewis's Abolition of >> point them to C.S. Lewis's Abolition of >> point them to C.S. Lewis's Abolition of Man Man Man uh where he talks about humanity uh where he talks about humanity uh where he talks about humanity uh uh uh the technology that presented itself as the technology that presented itself as the technology that presented itself as above man or above humanity, but in the above man or above humanity, but in the above man or above humanity, but in the end it it became a tool that some men end it it became a tool that some men end it it became a tool that some men used over other men.

used over other men. used over other men. Um and that is what we are risking. So, Um and that is what we are risking. So, Um and that is what we are risking. So, AI produces as Lewis chillingly predicts AI produces as Lewis chillingly predicts AI produces as Lewis chillingly predicts that not a that not a that not a no a like a human being using it over no a like a human being using it over no a like a human being using it over other human beings. It was just an other human beings. It was just an other human beings. It was just an artifact. It was not the technology artifact. It was not the technology artifact. It was not the technology itself that was above other men, but it itself that was above other men, but it itself that was above other men, but it was men who are above other men that was men who are above other men that was men who are above other men that were using technology. So, um I would were using technology. So, um I would were using technology. So, um I would tell these people that ultimately they tell these people that ultimately they tell these people that ultimately they must sit before a holy God at the end of must sit before a holy God at the end of must sit before a holy God at the end of the day. Um the day. Um the day. Um and they should and they should and they should uh worry that they're going to be asked uh worry that they're going to be asked uh worry that they're going to be asked this, "Were you faithful in what I gave this, "Were you faithful in what I gave this, "Were you faithful in what I gave you? What you? What you? What What God gave you?" And that they will What God gave you?" And that they will What God gave you?" And that they will give an account of how they used give an account of how they used give an account of how they used um um um their power, their authority. Great their power, their authority. Great their power, their authority. Great power comes great responsibility. power comes great responsibility. power comes great responsibility. And um And um And um the greatest command that God gave us is the greatest command that God gave us is the greatest command that God gave us is love the Lord your God. So, how are they love the Lord your God. So, how are they love the Lord your God. So, how are they loving their God with the technology? loving their God with the technology? loving their God with the technology? And also, how are they loving their And also, how are they loving their And also, how are they loving their neighbor? How are they developing this neighbor? How are they developing this neighbor? How are they developing this technology? Not only for profiteering technology? Not only for profiteering technology? Not only for profiteering sake, but for advancement of human sake, but for advancement of human sake, but for advancement of human dignity and um loving their neighbor. Uh dignity and um loving their neighbor. Uh dignity and um loving their neighbor. Uh there's something interesting, Dr.

there's something interesting, Dr. there's something interesting, Dr. Chris, um that So, during the era of Chris, um that So, during the era of Chris, um that So, during the era of social media, some some of these uh social media, some some of these uh social media, some some of these uh social media gurus were asked, "Would social media gurus were asked, "Would social media gurus were asked, "Would you allow your child to use social you allow your child to use social you allow your child to use social media?" And most of them said no, "We media?" And most of them said no, "We media?" And most of them said no, "We will not." So, I would ask these the will not." So, I would ask these the will not." So, I would ask these the these AI companies, "Would you allow these AI companies, "Would you allow these AI companies, "Would you allow allow your children Would you allow the allow your children Would you allow the allow your children Would you allow the people that you love most people that you love most people that you love most to to fall into AI psychosis, to use AI to to fall into AI psychosis, to use AI to to fall into AI psychosis, to use AI as as a companion as as a companion as as a companion uh bot?" uh bot?" uh bot?" And are you loving your neighbor using And are you loving your neighbor using And are you loving your neighbor using AI? Keeping in mind that you you shall AI? Keeping in mind that you you shall AI? Keeping in mind that you you shall give an account to your God, uh to the give an account to your God, uh to the give an account to your God, uh to the God that is to the triune God, whether God that is to the triune God, whether God that is to the triune God, whether you like it or not. All things shall bow you like it or not. All things shall bow you like it or not. All things shall bow before the Lord. Uh you too shall bow. before the Lord. Uh you too shall bow. before the Lord. Uh you too shall bow. So, you you must give an account. So, you you must give an account. So, you you must give an account. >> Amen. >> Amen. >> Amen. Which is a word in season to you and me Which is a word in season to you and me Which is a word in season to you and me as well, isn't it? And to everyone as well, isn't it? And to everyone as well, isn't it? And to everyone listening to this. Not not only AI listening to this. Not not only AI listening to this. Not not only AI developers developers developers need to ask those questions when we get need to ask those questions when we get need to ask those questions when we get up in the morning every day. Thank you up in the morning every day. Thank you up in the morning every day. Thank you uh for sort of bringing into sharp focus uh for sort of bringing into sharp focus uh for sort of bringing into sharp focus those fundamental those fundamental those fundamental >> Mhm.

>> Mhm. >> Mhm. >> key questions >> key questions >> key questions that if we're not asking them that if we're not asking them that if we're not asking them of ourselves, uh then then woe to us of ourselves, uh then then woe to us of ourselves, uh then then woe to us um at the end of the day, at the end of um at the end of the day, at the end of um at the end of the day, at the end of our lives. our lives. our lives. Indeed. Um Indeed. Um Indeed. Um Joshua, that there are two questions Joshua, that there are two questions Joshua, that there are two questions that I'm asking to all the guests on that I'm asking to all the guests on that I'm asking to all the guests on this show. And the the first one is uh this show. And the the first one is uh this show. And the the first one is uh what is one inefficient human habit that what is one inefficient human habit that what is one inefficient human habit that you're committed to keeping in the age you're committed to keeping in the age you're committed to keeping in the age of AI? of AI? of AI? >> Uh I will still reading >> Uh I will still reading >> Uh I will still reading and forming a book club and going and forming a book club and going and forming a book club and going through uh a critique of a book. through uh a critique of a book. through uh a critique of a book. Uh I don't AI to take that away from me. Uh I don't AI to take that away from me. Uh I don't AI to take that away from me. I don't I don't I don't I want to go deep into and I don't want I want to go deep into and I don't want I want to go deep into and I don't want to like give an AI and tell me give me to like give an AI and tell me give me to like give an AI and tell me give me an uh what is this book an uh what is this book an uh what is this book talking about. No, I want to go through talking about. No, I want to go through talking about. No, I want to go through the highs and lows of reading. I want to the highs and lows of reading. I want to the highs and lows of reading. I want to go through um go through um go through um reader's block. reader's block. reader's block. Like not going through something and Like not going through something and Like not going through something and fighting my way until uh I understand fighting my way until uh I understand fighting my way until uh I understand what this author is writing. I want to what this author is writing. I want to what this author is writing. I want to not not not um Uh I don't want to learn what the um Uh I don't want to learn what the um Uh I don't want to learn what the author is talking about. I want to go author is talking about. I want to go author is talking about. I want to go under what the author was talking about.

under what the author was talking about. under what the author was talking about. So, there's a difference between um like So, there's a difference between um like So, there's a difference between um like knowing everything that the author was knowing everything that the author was knowing everything that the author was trying to say and knowing getting trying to say and knowing getting trying to say and knowing getting getting myself under their worldview and getting myself under their worldview and getting myself under their worldview and getting under them so that um I actually getting under them so that um I actually getting under them so that um I actually represent them well. So, um so, that's represent them well. So, um so, that's represent them well. So, um so, that's one inefficient habit. Right now, I'm one inefficient habit. Right now, I'm one inefficient habit. Right now, I'm reading Chronicles of Narnia. This C.S. reading Chronicles of Narnia. This C.S. reading Chronicles of Narnia. This C.S. Lewis is my best author. Uh and I and I Lewis is my best author. Uh and I and I Lewis is my best author. Uh and I and I don't want to to tell AI to tell me what don't want to to tell AI to tell me what don't want to to tell AI to tell me what what the last book, for example, says. I what the last book, for example, says. I what the last book, for example, says. I don't want to know whether don't want to know whether don't want to know whether I don't want AI to tell me whether Lucy I don't want AI to tell me whether Lucy I don't want AI to tell me whether Lucy continues in the path. No, I want to continues in the path. No, I want to continues in the path. No, I want to learn. >> Yes, absolutely. A one-paragraph summary >> Yes, absolutely. A one-paragraph summary of The Narnia Chronicles by AI would be of The Narnia Chronicles by AI would be of The Narnia Chronicles by AI would be an utterly underwhelming experience, an utterly underwhelming experience, an utterly underwhelming experience, wouldn't it? I love this term, reader's wouldn't it? I love this term, reader's wouldn't it? I love this term, reader's block. I've never heard it before. I'm block. I've never heard it before. I'm block. I've never heard it before. I'm going to use that. That is exactly what going to use that. That is exactly what going to use that. That is exactly what it is, isn't it? You read something, you it is, isn't it? You read something, you it is, isn't it? You read something, you can't make sense of it, and suddenly, as can't make sense of it, and suddenly, as can't make sense of it, and suddenly, as you keep bludgeoning your head against you keep bludgeoning your head against you keep bludgeoning your head against it, the penny drops.

it, the penny drops. it, the penny drops. >> Yeah. >> Yeah. >> Yeah. >> And finally, you come to understand it, >> And finally, you come to understand it, >> And finally, you come to understand it, and there's that wonderful moment, "Oh, and there's that wonderful moment, "Oh, and there's that wonderful moment, "Oh, I see now what this author is trying to I see now what this author is trying to I see now what this author is trying to say." Reader's block, I love it. say." Reader's block, I love it. say." Reader's block, I love it. And the the second question that I'm And the the second question that I'm And the the second question that I'm asking everyone is is what is one thing asking everyone is is what is one thing asking everyone is is what is one thing that AI has revealed to you personally that AI has revealed to you personally that AI has revealed to you personally about your own heart. about your own heart. about your own heart. >> Yeah, this is a very intimate question, >> Yeah, this is a very intimate question, >> Yeah, this is a very intimate question, but I'm going to share is that um but I'm going to share is that um but I'm going to share is that um it revealed my laziness. it revealed my laziness. it revealed my laziness. Oh my goodness, it did. And I am so Oh my goodness, it did. And I am so Oh my goodness, it did. And I am so happy that I am in a church in our local happy that I am in a church in our local happy that I am in a church in our local church where we share these things. So church where we share these things. So church where we share these things. So um there's a friend of mine who shared a um there's a friend of mine who shared a um there's a friend of mine who shared a Bible verse in the book of Proverbs Bible verse in the book of Proverbs Bible verse in the book of Proverbs which says that as a door turns on its which says that as a door turns on its which says that as a door turns on its hinges, so does a lazy man turn on his hinges, so does a lazy man turn on his hinges, so does a lazy man turn on his bed. And I was like, "How does that How bed. And I was like, "How does that How bed. And I was like, "How does that How is my AI use revealing that about me?" is my AI use revealing that about me?" is my AI use revealing that about me?" So what I decided that I am I am going So what I decided that I am I am going So what I decided that I am I am going to be writing my own things. This really to be writing my own things. This really to be writing my own things. This really when generative AI came out and then when generative AI came out and then when generative AI came out and then there was this whole push that AI is there was this whole push that AI is there was this whole push that AI is going to solve all your problems. It's going to solve all your problems. It's going to solve all your problems. It's like you have a research assistant like you have a research assistant like you have a research assistant there. So there's a real danger of there. So there's a real danger of there. So there's a real danger of laziness. There's a real danger of you laziness. There's a real danger of you laziness. There's a real danger of you not not not working as unto the Lord. There's a real working as unto the Lord. There's a real working as unto the Lord. There's a real danger of you giving danger of you giving danger of you giving um below below par content that you um below below par content that you um below below par content that you would have if if you are the one who was would have if if you are the one who was would have if if you are the one who was to write that on your own. So um it to write that on your own. So um it to write that on your own. So um it reveals that I am prone to laziness. I reveals that I am prone to laziness. I reveals that I am prone to laziness. I don't want to be a lazy man.

don't want to be a lazy man. don't want to be a lazy man. I I I I don't want to be a man who like I I I I don't want to be a man who like I I I I don't want to be a man who like um And the book of Proverbs usually is um And the book of Proverbs usually is um And the book of Proverbs usually is big on people who are lazy. big on people who are lazy. big on people who are lazy. Like that's a big verse. That as as a Like that's a big verse. That as as a Like that's a big verse. That as as a door turns on its hinges, so so does a door turns on its hinges, so so does a door turns on its hinges, so so does a lazy man turn on his bed. lazy man turn on his bed. lazy man turn on his bed. Another thing that it reveals that about Another thing that it reveals that about Another thing that it reveals that about me, and again this is through me, and again this is through me, and again this is through um some friends of mine at my local um some friends of mine at my local um some friends of mine at my local church, some fellow members, is that um church, some fellow members, is that um church, some fellow members, is that um they're prone to cowardice. When you they're prone to cowardice. When you they're prone to cowardice. When you develop a content using AI and you don't develop a content using AI and you don't develop a content using AI and you don't own up, you don't say that this is AI own up, you don't say that this is AI own up, you don't say that this is AI developed. Because there's some developed. Because there's some developed. Because there's some companies that companies that companies that um that require you as a policy to say um that require you as a policy to say um that require you as a policy to say that this content is AI developed. So if that this content is AI developed. So if that this content is AI developed. So if you don't Yeah, you say that this is me, you don't Yeah, you say that this is me, you don't Yeah, you say that this is me, first of all, you are lying of course, first of all, you are lying of course, first of all, you are lying of course, but also you are cowardly. You don't but also you are cowardly. You don't but also you are cowardly. You don't want to say that this is want to say that this is want to say that this is This is AI generated. This is AI generated. This is AI generated. A friend pointed me to Revelation 21:8 A friend pointed me to Revelation 21:8 A friend pointed me to Revelation 21:8 which says that which says that which says that that the Bible says that all the people that the Bible says that all the people that the Bible says that all the people that are going to be going to hell, the that are going to be going to hell, the that are going to be going to hell, the first group will be will be cowards. So first group will be will be cowards. So first group will be will be cowards. So there there is there there is there there is sexual immorals, there's adulterers, sexual immorals, there's adulterers, sexual immorals, there's adulterers, there's murderers, but then the first there's murderers, but then the first there's murderers, but then the first group of people who are going to go to group of people who are going to go to group of people who are going to go to hell are going to be cowards, which was hell are going to be cowards, which was hell are going to be cowards, which was like out of this world. Then I remember like out of this world. Then I remember like out of this world. Then I remember that fear is one of the most Fear not is that fear is one of the most Fear not is that fear is one of the most Fear not is one of the most repeated commands in the one of the most repeated commands in the one of the most repeated commands in the Bible. So it's it only warrants why God Bible. So it's it only warrants why God Bible. So it's it only warrants why God would send people to hell because they would send people to hell because they would send people to hell because they have not obeyed his most repeated

have not obeyed his most repeated have not obeyed his most repeated command in the Bible. So I don't want to command in the Bible. So I don't want to command in the Bible. So I don't want to be I don't want to be cowardly. If I be I don't want to be cowardly. If I be I don't want to be cowardly. If I have used AI, I'm going to say it, and I have used AI, I'm going to say it, and I have used AI, I'm going to say it, and I don't want to put myself in that don't want to put myself in that don't want to put myself in that scenario that I have that I'm going to scenario that I have that I'm going to scenario that I have that I'm going to say that I have used AI. So I'm not say that I have used AI. So I'm not say that I have used AI. So I'm not going to going to going to I am going to be very conservative I am going to be very conservative I am going to be very conservative because I'm a researcher. I I write a because I'm a researcher. I I write a because I'm a researcher. I I write a lot. The blogs, the scientific papers. I lot. The blogs, the scientific papers. I lot. The blogs, the scientific papers. I want to do the actual work. So I won't I want to do the actual work. So I won't I want to do the actual work. So I won't I don't want to be lazy. I don't want to be lazy. I don't want to be lazy. I I want to work as unto the Lord, and I I want to work as unto the Lord, and I I want to work as unto the Lord, and I don't want to be cowardly. don't want to be cowardly. don't want to be cowardly. >> The Bible is such an inconvenient book, >> The Bible is such an inconvenient book, >> The Bible is such an inconvenient book, isn't it? Like who would put cowardly in isn't it? Like who would put cowardly in isn't it? Like who would put cowardly in that list? Like you know, if if we were that list? Like you know, if if we were that list? Like you know, if if we were writing that list, we would not put writing that list, we would not put writing that list, we would not put cowardly in there. And yet there it is, cowardly in there. And yet there it is, cowardly in there. And yet there it is, you know, in black and white in our you know, in black and white in our you know, in black and white in our Bibles. And you're absolutely right, Bibles. And you're absolutely right, Bibles. And you're absolutely right, fear not is is the most repeated fear not is is the most repeated fear not is is the most repeated command. And command. And command. And um what a um what a um what a what an insightful word that is. Don't what an insightful word that is. Don't what an insightful word that is. Don't want to be cowards in the way we use AI. want to be cowards in the way we use AI. want to be cowards in the way we use AI. Thank you so much Thank you so much Thank you so much for sharing that with us. I have a lot for sharing that with us. I have a lot for sharing that with us. I have a lot to think about. I'd never thought of it to think about. I'd never thought of it to think about. I'd never thought of it in that way before, and it's it's really in that way before, and it's it's really in that way before, and it's it's really powerful the way that you put that.

powerful the way that you put that. powerful the way that you put that. Thank you. Thank you. Thank you. >> Thank you. >> Thank you. >> Thank you. >> And >> And >> And if other people listening to this or if other people listening to this or if other people listening to this or watching this, like me, have been watching this, like me, have been watching this, like me, have been profoundly challenged by what Joshua has profoundly challenged by what Joshua has profoundly challenged by what Joshua has been saying, and you want to take these been saying, and you want to take these been saying, and you want to take these ideas further, please do come and join ideas further, please do come and join ideas further, please do come and join us in the Silicon Spirituality Discord. us in the Silicon Spirituality Discord. us in the Silicon Spirituality Discord. Uh well, we'll be learning together how Uh well, we'll be learning together how Uh well, we'll be learning together how to follow Jesus in a tech-saturated to follow Jesus in a tech-saturated to follow Jesus in a tech-saturated world. You'll find practical life world. You'll find practical life world. You'll find practical life experiments for every episode and and experiments for every episode and and experiments for every episode and and also details of an upcoming online also details of an upcoming online also details of an upcoming online cohorts as well. The links down there in cohorts as well. The links down there in cohorts as well. The links down there in the show notes. the show notes. the show notes. We'd love to see you there. We'd love to see you there. We'd love to see you there. Joshua, it has been a delight to speak Joshua, it has been a delight to speak Joshua, it has been a delight to speak with you and learn from you about these with you and learn from you about these with you and learn from you about these things. Thank you so much for sharing things. Thank you so much for sharing things. Thank you so much for sharing your wisdom with us today. your wisdom with us today. your wisdom with us today. >> Thank you for having me.