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Karen Hao is an award-winning journalist covering artificial intelligence, currently contributing to The Atlantic. Previously, she was a foreign correspondent at The Wall Street Journal focused on AI, China tech[…]
Robert Chapman-Smith is the Editor-in-Chief of Big Think. As Editor-in-Chief, Rob guides coverage and works with the Big Think team to explore the world’s biggest questions with the world’s biggest[…]

Journalist Karen Hao joins Big Think’s Editor-in-Chief, Robert Chapman-Smith, to discuss the recent events at OpenAI, including the ousting and reinstatement of CEO Sam Altman, as well as the ideological clashes regarding the development and release of powerful AI models like ChatGPT.

KAREN HAO: I remember early in the days of OpenAI, when I was covering it, I mean people would joke, like if you ask any employee what we're actually trying to do here and what AGI is, you're gonna get a different answer. Yeah, artificial general intelligence, AGI, this term is not actually defined. There's no shared consensus around what AGI is- and of course there's no consensus around what is good for humanity. So if you're going to peg your mission to like really, really vague terminology that doesn't really have much of a definition, what it actually means is it's really vulnerable to ideological interpretation.

ROBERT CHAPMAN-SMITH: Today on Big Think, we're gonna be talking with Karen Hao, a contributing writer for The Atlantic who focuses on technology and its impacts on society. In this interview, we're gonna be talking about artificial intelligence and specifically OpenAI and the events that led to the ouster and reinstatement of Sam Altman as CEO. Karen, thank you for joining us on Big Think today.

KAREN HAO: Thank you so much for having me.

ROBERT CHAPMAN-SMITH: So Karen, I'm curious what happened at OpenAI recently?

KAREN HAO: That's a great question. So in the last week we sort of saw a very dramatic ousting of the CEO by the board of the company, the revolt of hundreds of employees after this happened, and then the reinstatement of the CEO. And to sort of understand what actually happened in this very chaotic moment, we kind of have to first look at the way that the company was founded. OpenAI is very different from a traditional tech company in that it was actually founded as a nonprofit specifically to resist the tech industry. Elon Musk and Sam Altman co-founded the company on the basis that artificial intelligence is a very important technology for our our time, our era, and it needs to be shepherded; the development of it needs to be shepherded very carefully, and therefore it should not actually be attached to for-profit motivated companies. And so they founded it as a nonprofit in 2015, a few years down the line in 2019, they realized that this nonprofit structure was not actually a way, it wasn't actually gonna help them raise enough money to perform the specific type of AI research that they wanted to do, which was going to be very, very capital intensive. And so in 2019 they did this really weird thing where they nested a for-profit entity or what they call a "capped-profit" entity under the nonprofit. And so what we saw with the board's actions firing the CEO, Sam Altman, is the nonprofit has a board of directors that are beholden not to shareholders, but to a original, the original mission of OpenAI, which is to try to create artificial general intelligence for the benefit of humanity. And so the board, they haven't actually specified why they fired the CEO, but they said that they acted with the mission in mind. So absolutely nothing related to, for sort of fiduciary responsibility, nothing related to the customers that OpenAI has, but specifically they felt that the company was no longer headed in the direction that they thought was aligned with the mission and therefore they ousted the CEO.

ROBERT CHAPMAN-SMITH: There's so much that even just goes into what you were just talking about. I'm curious to, to go back to talking about this nonprofit genesis of OpenAI and sort of how that came to be. Oh, you, you mentioned sort of it was built around trying to not have profit motives necessarily, but sort of just figure out a safe way to shepherd the creation of artificial intelligence as tools for humanity. I would love it if you could walk me through the origins of OpenAI as an organization.

KAREN HAO: Absolutely. So one of the things to sort of understand about AI research in general is AI is not actually new. It's been around since the '50s, and it was originally an academic field that then tech giants in Silicon Valley started seeing massive commercial potential for, and so they kind of plucked this technology out of the scientific academic realm and then tried to start deploying it into products. And the thing that's happened in the last decade in particular is that there's been an enormous shift in the field where because tech giants have realized that this technology can be very, very lucrative, Google and Facebook, they use it for things like ad targeting. They have increasingly pulled more and more researchers from the academic field, from universities into their corporations to develop this technology, not for scientific discovery, not for any goal other than specifically that they would like to commercialize and continue to make more money. So the reason why OpenAI was founded as a nonprofit, I- the story goes that Elon Musk in particular was very worried about Google because Google had been early, an early mover in recognizing the commercial potential of AI, started building this really big lab and kind of poaching all of the top talent from all around the world and trying to basically establish a stronghold in AI leadership. And Elon Musk felt that this was not the appropriate way to develop AI because AI could be very beneficial for many different things, not just for commercial products. And that actually, if the development of AI were attached to commercialization, it could in fact also be harmful because of things that we were already sort of starting to see at the time around social media and sort of for-profit incentives corrupting the use of a powerful technology. And so that was ultimately the vision this, this nonprofit vision. But the thing that sort of thwarted, I guess this vision is the fact that when OpenAI went to hire its first founding team- its founding team had 10 members- they specifically brought on this researcher called Ilya Sutskever, who is now OpenAI's chief scientist. At the time, he was at Google and he already had a very prestigious reputation. He was the co-author of a groundbreaking AI research paper that had actually sparked a lot of the commercialization efforts around this technology. And when they brought him on Ilya Sutskever had a very particular philosophy around AI research, which was that in order to see the full potential of this technology, we need to scale it dramatically. So there were sort of different competing philosophies at the time. Do we actually have the techniques for AI, advanced AI or do we actually need to create more techniques? And he thought we have it, we just have to sort of explode the scale, feed evermore data, evermore computer chips into these AI models and that's when we'll start seeing real emergent intelligent behaviors in these digital technologies. So when he made that decision and when OpenAI set on that path, that's when they started running into financial issues and realized the nonprofit was no longer viable.

ROBERT CHAPMAN-SMITH: I think it's interesting that that was sort of a tension point. They had this nonprofit mention to sort of make this technology and, and really the 'open' in their name sort of goes back to a lot of things like open source I imagine as as part of the initial founding, but then they realized that they had to have some sort of commercial arm for the technology. I'm curious when that decision was made, how many of the players that were involved in what happened just recently were also at OpenAI at the time when they made that decision to create the commercial entity, the for-profit en entity underneath the nonprofit.

KAREN HAO: There were sort of three main characters at OpenAI for this week of events: There's the CEO Sam Altman, there's the chief scientist Ilya Sutskever, and then there's the president Greg Brockman. All three of them were the ones that created this nonprofit, capped-profit model. So they were the architects of it. And at the time I had actually interviewed Greg Brockman and Ilya Sutskever about a few months after they had created that model. And they were very sincere about this idea that even though they needed to change the nonprofit structure a little bit in order to raise enough capital for the things that they wanted to do, that this was somehow sort of the perfect solve. Like that they were creating this clever solution to the central problem of wanting to raise money but also be beholden to the mission. And what's interesting is, I mean now in hindsight we can say that it was this very structure that kind of created a very dramatic ousting of the CEO and then the reinstatement of him and now a, a lot of uncertainty in the air about what the future of the company will continue to be. But that was the original intention was they felt that they needed to change the structure in order to get the money, but that they didn't want to actually change the mission.

ROBERT CHAPMAN-SMITH: So were there any consequences when OpenAI switched from being a nonprofit to having the capped-profit entity underneath the nonprofit?

KAREN HAO: I think the main consequence was actually just that employees suddenly were getting higher compensation. So, so there was, you know, there was a little bit of controversy within the company. There were people that were, that had joined OpenAI on the premise that I would be a nonprofit. So they were worried about what is this legal structure that is suddenly emerging? What do you mean that we're turning into a for-profit, capped-profit kind of hybrid. But one of the things that this kind of model enabled was that OpenAI started paying employees more. Within the world of AI research, there really actually aren't that many senior researchers because this field, even though it's been around for decades, there aren't that many people in the world that have the kind of skills that they need to develop this kind of technology and that have also specifically worked in kind of environments where they know how to commercialize and productize it as well. And so OpenAI was actually losing a lot of its talent, like it would hire talent, try to retain them, but then lose the talent because Google or DeepMind, which were two different entities at the time, were just paying more. And by changing into this weird hybrid structure and raising venture funding, they were able to issue stocks and start giving much higher compensation packages based not just in cash but also in stocks to this capped-profit arm. So that was honestly the main consequence in that moment in time was they finally were able to compete on talent. But then of course with kind of this model, the reason why they set it up was so that they could get the investment in. And once you start getting investment in the biggest investor of which was Microsoft, that's when you start also having strings attached to the money. And that's when the kind of move towards more and more commercialization and less and less research started happening.

ROBERT CHAPMAN-SMITH: So who is Sam Altman and how does his role as CEO sort of just play into this picture and the, and potentially to the board's decision to let him go and then eventually rehire him?

KAREN HAO: Before Altman was CEO of OpenAI, he was president of Y Combinator, which is arguably the most famous Silicon Valley startup incubator. And basically as the head he became the president. I mean- he inherited it from Paul Graham who was the original founder of Y Combinator. And at the time when he was hired as the president, he was really young, I can't remember exactly, but he was early thirties I believe. And people were really surprised. They were like, "Who is this guy?" And then he rapidly made a name for himself as one of the most kind of legendary investors that was really good at taking startup ideas and then scaling them massively into successful aggressive tech behemoths. And so you can kind of see with this particular career path how his imprint has been left on OpenAI because OpenAI before he became officially the CEO, even though he co-founded it, he wasn't taking very active of a role until 2019 when he officially stepped into the CEO role. And before 2019 OpenAI was, it was, I mean it was a nonprofit, it was basically kind of just academic, like it kind of just operated like a university lab. People saw it as an alternative to being a professor where you get to do this like fun research and there's not really any strings attached- and you also get paid a lot more. And the moment that Sam joins the company in 2019, or the nonprofit at the time in 2019, that's when you start seeing the push to commercialize the push to scale. You know, like after ChatGPT, OpenAI now has a growth team that's dedicated to growing its user base. I mean this is, you would never see that with a academically focused or research focused lab, but it's certainly kind of like an iconic feature of kind of the types of startups that Altman was shepherding into the world as president of of YC. So I think he is a bit of a polarizing figure. When I've been interviewing employees, current and former employees, this has sort of come up as some people see him as you know, one of the most legendary people within the valley and just love and follow his leadership. Other people find him very difficult to read and very difficult to pin down in terms of what he actually believes as a person, which makes them very nervous. And some people would go as far as to say that he's a little bit duplicitous in this regard. And it is even for me, like I find it very difficult to pin him down and, and what does he ultimately believe and so did he rapidly, you know, start commercializing OpenAI because he believes truly in the techno-optimist narrative of reaching this, this is how you reach beneficial AGI or is it actually a bit of a habit? You know, he's been doing this for so long that by default he just gravitates towards what he knows what he's good at. Another kind of example of this is when he joined OpenAI, he started a fund for OpenAI to invest in other startups. And at the time people were like, "Why is OpenAI investing in other startups when they themselves are not profitable?" And it's, "Well Sam Altman's an investor!" So like, it's just sort of habitual for him. I can't personally say like what he truly believes as a person or what his values are as a person, but certainly from his career you can see that it makes a lot of sense why OpenAI has headed in the way that it has.

ROBERT CHAPMAN-SMITH: And I'm curious to talk about Greg Brockman a bit. You know, he's a, he's been around technology in Silicon Valley for a while. He was part of an, one of the original members of Stripe, I believe he helped ship some of the early versions of that product. And you know, one of the pieces of commentary that I saw was that if you're building a ChatGPT wrapper and you're using Stripe for your payment system, it's very likely that Greg Brockman built like 50% of your application. So I'm curious to know more about him and his role in what happened, and just how people conceive of him as a technologist, but also in his role in OpenAI.

KAREN HAO: Greg Bachman is very similar to Sam in that he also has had his full career in startups. He went originally went to Harvard, dropped out, then went to MIT, dropped out, and went straight to the valley, joined Stripe, became the chief technology officer. His adrenaline rush comes from building and shipping products and seeing people use the things that he's made with his own two hands. Like he talked about that a lot when I interviewed him, right when Microsoft invested, it made its first investment in OpenAI. So that is also his instinct. And what's interesting to take a step back from OpenAI within kind of the landscape of different AI firms, OpenAI is seen by others as the most Silicon Valley of them all because you could, like at the time when it was founded, DeepMind was the other kind of major model for this kind of research. And it was seen as this is like a very academic endeavor. We, yes, we've been acquired by Google and we're gonna help Google commercialize some things, but like it is still going to retain this very academic environment and and be kind of away from the Silicon Valley scene of build, build, build and move fast and all of that. So the fact that Sam Altman and Greg Brockman both come from this we're seen by the broader AI community as, okay, this is what OpenAI is about now when when Sam Altman joined and then Greg was already there, they like joined forces. It's seen as neither of them are AI researchers, they don't come from an academic background, they come from this commercialization background. Greg was the first employee of OpenAI in that when Sam Altman and Elon Musk decided, "Hey, we should put down money to fund this thing," Brockman was the first one to raise his hand to say, "I will make it happen." And he was the one that recruited the first original team of 10 and then he led the company before Altman stepped in as CEO and he's very much a Sam ally. So part of the reason why in the events when Sam got fired, Greg immediately announced that he was leaving as well is because these two have a very close relationship and they share kind of core ideologies around what's good for the world. And so Greg, when he left, I think that was a moment for employees that was very scary because when the board says to you that your CEO has been fired, the first instinct is, "Oh well what did he do?" But when the first employee and co-founder and huge ally of the CEO and one of the most, you know, senior people also announces that he's leaving, that was when the employees went, "Oh crap, like what does this actually mean for the functioning of this organization? And is this actually somehow nothing to do with what Sam did, but somehow a power grab." So I think he is sort of like very respected and like an early employee that did build, he did engineer a lot of the things early in the company, and he does have a lot of sway as well within the organization. And he was kind of the canary in the coal mine I guess you could say for employees, that something bad was happening.

ROBERT CHAPMAN-SMITH: I'm curious to talk about the employees because it it, I mean you sort of just mentioned it right there that it it must've been a whirlwind experience for them and you know, nobody had advanced notice that the board was doing, not even the investors of OpenAI of the, of the capped-profit entity had advanced knowledge- and the employees learned basically when everybody else was learning or or shortly before about what was happening. What have you heard about what it was like for the employees to experience this and they, we sort of saw this rallying cry around the, the leadership team that was exiled from the company. Just talk to me about just what happened after the news broke and how employees were feeling and just the events that occurred afterwards.

KAREN HAO: I think it was a very tumultuous and very emotional and very sleep deprived period of days after Altman was fired and reinstated for the employees. Of course, like you said, none of them knew that this was happening, they had no idea what was going on and the board never explained really why they had ultimately fired Altman. And so it kind of, the, the progression of like their emotions went from like confusion to fear when Brockman leaves and then three senior research scientists also leave two anger at the board, like really, really deep anger because they were like, "If you're going to do something dramatic, we deserve answers as employees." And when they didn't, the, the longer they didn't get answers, the more and more worked up it became. And part of this is, I mean many companies within Silicon Valley have this- they really emphasize that companies are families and you as an employee are not just an employee of any company, you, it is your identity OpenAI takes this to the max, like the fact that they say that their mission is for the benefit of humanity. Like people genuinely believe this and they think that this is like they're dedicating their life to this. It's not just like, "This is my job and then I go home." This is like all, all they think about sometimes. And so it's like that level of anger of like, if you are going to do something that could ruin this company that we genuinely believe is doing good for the world, like how dare you not tell us why and how dare you continue to kind of leave us in the dark and and and not treat us as like critical stakeholders in this, in this whole fiasco. And so what happened was kind of organically the employees started rapidly organizing on Twitter. So they, they started like posting very similar messages by the hundreds kind of on Twitter of like every time Sam Altman said, "I love OpenAI so much, I miss it." You know, you would see like employees retweeting it with a heart emoji and just it would, like when I opened my Twitter feed, it was just like dozens and dozens and dozens of heart emojis. Not because I was looking at any like OpenAI specific feed that was just what was showing up on my regular feed. And then there were the the like OpenAI is nothing without its people that everyone started tweeting as well. And that was sort of a way to try and pressure the board to give answers. And then of course that ultimately escalated to over 700 employees outta 770 signing a letter saying that like, if Sam is not reinstated, they're all gonna quit. And so I think another dimension that's sort of important to add to this is most if not all of the OpenAI employees, their compensation packages are majority stock and Bloomberg has a good article on this, you know like the average compensation is around like 800,000 to a million dollars and maybe 60% or something of that like that is actually stock. So if a company, if the company does not exist anymore, all of a sudden your stock goes to zero. And that was also extremely stressful for people because people were banking on, you know, some, some people were, had already bought houses based on projected income or were looking to buy houses based on the projected income that were suddenly worried about paying their mortgage. There were people that were on visas that if the company doesn't exist anymore and they don't get hired fast, then their ability to stay in the country is jeopardized and maybe they already have family and then like, you know, that's gonna throw their entire family into disarray as well. So there were a lot of other aspects of it, not just the identity or the ideology piece that led employees to kind of have this very emotional and tumultuous time. And when Altman was reinstated there were some great details that were reported in the information about how employees like gathered at the office and they were crying and cheering and just, it was like a huge massive sigh of relief honestly, that they have their jobs still and that this company still exists and all the things that they've been working towards are going to continue to exist in some form or other and that they can move on with their lives, basically.

ROBERT CHAPMAN-SMITH: This recent situation with OpenAI is not the first time this company has gone through something like this. I would love for you to walk me through some of the history of the disruptions that have happened inside this company and some of the consequences that those events have meant for OpenAI and the rest of the AI industry.

KAREN HAO: One of the things, just to take a step back before we kind of go through the, the tumultuous history leading up to this point, one of the things that's kind of unique about OpenAI, I mean you see this in a lot of Silicon Valley companies, but OpenAI does this more than anyone else I would say, which is they use incredibly vague terms to define what they're doing. Artificial general intelligence, AGI, this term is not actually defined. There's no shared consensus around what AGI is and of course there's no consensus around what is good for humanity. So if you're going to peg your mission to like really, really vague terminology that doesn't really have much of a definition, what it actually means is it's really vulnerable to ideological interpretation. So I remember early in the days of OpenAI when I was covering it, I mean people would joke like if you ask any employee what we're actually trying to do here and what AGI is, you're gonna get a different answer. And that was, that was sort of almost a feature rather than a bug at the time in that they said, "You know, we're on a scientific journey, we're trying to discover what AGI is." But the issue is that you actually just end up in a situation where when you are working on a technology that is so powerful and so consequential, you are going to have battles over the control of the technology. And when it's so ill-defined what it actually is, those battles become ideological. And so through the history of the company, we've seen multiple instances when there have been ideological clashes that have led to friction and fissures. The reason why most people haven't heard of these other battles is because OpenAI wasn't really in the public eye before, but the very first battle that happened was between the two co-founders, Elon Musk and Sam Altman. Elon Musk was disagreeing with the company direction, was very, very frustrated, tried to take the company over, Sam Altman refused. And so at the time Elon Musk exited, this was in early 2018 and actually took all of the money that he had promised to give OpenAI with him. And that's actually part of the reason why this for-profit entity ends up getting constructed because in the moment that OpenAI realizes that they need exorbitant amounts of money to pursue the type of AI research that they wanna do is also the moment when suddenly one of their biggest backers just takes the money. The second like major kind of fissure that happened was in 2020, and this was after OpenAI had developed GPT-3, which was a predecessor to ChatGPT. And this was when they first started thinking about how do we commercialize, how do we make money? And at the time they weren't thinking about a consumer-facing product, they were thinking about a business product. So they developed the model for delivering through what's called an application programming interface. So other companies could like rapidly build apps on GPT-3. There were heavy disagreements over how to commercialize this model, when to commercialize the model, whether there should be more waiting, more safety research done on this. And that ultimately led to the falling out of one of the very senior scientists at the company, Dario Amodei, with Sam Altman, Greg Brockman, and Ilya Sutskever. So he ended up leaving and taking a large chunk of the team with him to found what is now- one of open AI's biggest competitors, Anthropic.

ROBERT CHAPMAN-SMITH: AI has been a technology that's had a lot of hype cycles and a lot of sort of failed delivery on those hype cycles. I think a lot of folks remember Watson from IBM and all the hype that surrounded that and was gonna revolutionize healthcare and a lot of those things that didn't come to bear or even just the, the small little colloquial examples of it playing Jeopardy or some of the, the AI models that were playing AlphaGo or chess and things like that. But one of the things I find particularly interesting is that the, the fear around these technologies and whether they're safe or not actually cause some folks to not release these models publicly, the the transformer, the general pre-trained transformer that is the basis of this GPT technology that OpenAI is using for these large language models was actually developed inside of Google before it became, you know, widely released to the public and utilized. I'm curious when those debates were happening with the split with Anthropic and OpenAI, how was a similar sort of tension between we shouldn't be releasing these models without thoroughly testing it, it's not ready for public consumption and like what were the contours of that conversation between the different schools of thought on AI?

KAREN HAO: In general, including the OpenAI-Anthropic split there have emerged kind of two major camps but also some sub-camps: So we'll review all of them, but the, but there's kind of two philosophies that exist within OpenAI and also the general AI community around how do you actually build beneficial AGI, and one of those camps is sort of in the most extreme version is the techno-optimist camp of we get to beneficial AGI by releasing things quickly, by releasing them iteratively so people become more familiar with the technology so institutions can evolve and adapt instead of, you know, withholding it until suddenly capabilities become extremely dramatic and then releasing it onto the world. And also that we build it more beneficially by commercializing it so that we have the money to continue doing safety research, what's called safety research. The other major camp is basically sort of like the, the existential-risk camp again, kind of the extreme version of this camp, which basically says we, in order to get to beneficial AGI, we don't want to release it until we know for sure that we've like done all of the possible testing. We've like tweaked it and tuned it and tried to foresee as much as possible how this model is going to affect the world. And only then do we maybe start releasing it and making sure that it it it only produces positive outcomes. I think both of these, these are both very, very extreme in the sense that they've almost become quasi-religious ideologies around the development of AGI and like how to actually approach it. And there's sort of many, you, you could say that each camp over the years has sort of cherry-picked examples to support why they are correct in their argument. But when the OpenAI-Anthropic split happened, it was exactly this disagreement. So Sam Altman and Greg Brockman, they were very much, we need to continue releasing and get people used to it, get more money in so that we can continue doing this research. And Dario Amodei and his sister Daniela Amodei who was also at OpenAI, they were very much at the camp of no we should, we should be doing as much as possible to try and tweak and tune this model before it goes out into the world. And that that was ultimately sort of the clash that happened then and has continued to happen ever since.

ROBERT CHAPMAN-SMITH: It's clear now that OpenAI is shipping a lot of AI models available for consumers. It was, I think it's something around like a hundred million users are on ChatGPT: What has changed in terms of the perception of shipping these AI models to the public and how did that potentially lay the groundwork for the firing of Sam Altman that we, we experienced last week?

KAREN HAO: So these camps existed in the company and have existed in the company since the founding, but what happened in the last year was the release of ChatGPT and just as it was very shocking for everyone in the public and kind of a step-change in people's understanding of the capabilities, it was also kind of a dramatic transition point for the company itself. And part of the reason is when you suddenly put a technology that you've been developing in the company in the hands of a hundred million users, you start to get kind of crazy strain on the company infrastructure and kind of test cases on the ideologies that had already been operating in the theoretical realm within this company. So for the techno-optimist camp within OpenAI, they saw the success of ChatGPT and were seeing, you know, all of these use cases of people using it in wild and imaginative ways and they thought this is the perfect demonstration of what we've been talking about all along; like we should be releasing these technologies iteratively, watching people adapt to them and then look at all of the amazing things that they do. Once that happens, we should continue building on this momentum and continue advancing the, the productization of our technology. For the existential-risk camp ChatGPT was also the perfect demonstration of all of the, the fears that they had around harms of the tech, the technology, again, when you put the the technology in hands of a hundred million people, you're going to see some people using it in really horrible ways, in really abusive ways. And the company was not prepared for many of these in part because they didn't actually think that ChatGPT would be a big deal. So they did not in any way prepare for supporting the a technology that's used by a hundred million people. And so one of the things that the existential-risk camp got very, very scared of was if we couldn't predict even how this technology would be popular, how could we predict how this technology could be devastating? ChatGPT was sort of an accelerator for both camps in their ideology towards polar opposite extremes. And the reason why, or we don't, again, we don't know the actual reason why the board ended up trying to fire Sam Altman, but I think this is sort of, this context is very telling because ultimately what we saw with the board ousting Altman is this kind of struggle between the nonprofit and for-profit kind of arms of the company where the board says the nonprofit is still the, the mission and the fact that we're not actually doing this for money still should be the the central path forward. Whereas all of these people within the for-profit arm and Sam Altman himselfm were thinking, "No, we need to, we need to continue pushing ahead with this, this commercialization effort." So that kind of collision I think very likely, very strongly played into the board's decisions.

ROBERT CHAPMAN-SMITH: And what do we actually know about the board's decision to fire Sam Altman? Has there been any new information that has come in that has sort of clarified why the board has made its decision or are we still sort of in the dark about what was happening, what inputs led to that moment?

KAREN HAO: We are still completely in the dark. The board has issued very, very few statements, I believe actually even just one statement and it was just the one that they issued when they fired Altman and it, it just said that they had lost trust in Altman as a leader. They had lost trust in him being consistently candid in his communications and that was, they haven't really updated it. And I have sort of been talking with a lot of sources within the company or former employees as well since the events have happened and none of the employees know either this, there has been no communication from the board internally. There has been of course massive amounts of speculation. One of the things that came out after Altman was reinstated was reports from Reuters and The Information about how there had been staff researchers that had sent a letter to the board warning about a so-called breakthrough within the company; OpenAI confirmed to me that this was not actually related to the board's actions. And one of the things to sort of note in this particular instance is "breakthrough" is a very subjective term, and so it's in science usually breakthrough- something becomes a breakthrough when there has been significant testing validation, continued experimentation on an idea and a consensus forms around whether or not something actually was a breakthrough because it withstood the test of time. In this instance there's no, there's no kind of contestation and validation that's happening because all of this is within OpenAI's closed doors. And so breakthrough in this instance was sort of a term that was assigned to it- it was like sort of a subjective interpretation that several employees assigned to what they were seeing within the company, and not everyone within the company even agrees with this small contingent that supposedly sent the letter to the board.

ROBERT CHAPMAN-SMITH: It seems like there's a lot of like in the eye of the beholder in the AI industry where folks aren't necessarily sure, they're not necessarily sure what they've built, they're not fully aware of all the capabilities of these tools because it's, it's almost impossible to test something like a large language model completely in terms of all the things that it's capable of doing. From your conversations with people, how can you even just like tell that something is worthwhile to consider to be dangerous or not? Like what sort of steps do they go through if they're really concerned with safety inside of these organizations; how do they deal with that concern in an actionable way?

KAREN HAO: One of the things that OpenAI has started, has developed and Anthropic and Google also do this now too, is this process called 'red teaming' where they try to bring along a wide array of experts from cybersecurity backgrounds or national security backgrounds or biology backgrounds to try to make the model do bad things before they release it. So they did this process with GPT-4 and then typically the way that it goes is when the expert figures out, "Oh I can do X-thing with the model, like I can create malware very easily with this model," then the researchers within the company take that feedback and try to iteratively refine the model until it stops doing that. Like it'll, it'll refuse the request if someone says, "Please code me up a malware." This process has also been very heavily criticized because OpenAI sort of has from the very beginning had a particular bent around how they perceive risks, which is more focused on extreme risks like such as these, these existential risks, more focused on national security risks, and they sort of have often ignored other types of harms that are very present and impactful to many people here and now. One of the examples is discrimination: AI models have a lot of discrimination baked in because they are reflective of the data that they're trained on and it is very hard, basically impossible, to get a data sample that is perfectly, you know, aligned in every different dimension of what we wanna capture about our world. So one of the classic examples of this is facial recognition does not work as well on dark-skinned individuals and doesn't work as well on women than light-skinned individuals and men. And part of the reason was when the technology was first developed, the photos that were gathered for training this technology were predominantly light-skinned male individuals. And this is true of every AI model that has ever been developed with large language models. We've also seen, you know, GPT-4 or we've seen with large language models in general that if you kind of prompt it to talk about engineers it will often use the pronouns he/him instead of she/her or they/them. And those are all like, they're kind of codified into the model and OpenAI has done research on this more recently, but historically this is one thing that they've set kind of like de-emphasized within the company and they focused more on these kind of extreme risks, and OpenAI has continued to also very heavily rely on sort of experts that they curate themselves. And again, this is all, this all goes back to sort of like who has the power in this situation and how do we define what this technology is, whether it works, who it works for. And so much of the way that OpenAI operates is kind of through their lens with their choices, their determinations, and with very, very little feedback from, you know, the vast broader population in the world.

ROBERT CHAPMAN-SMITH: I'm curious to put the, the conversation around safety in the way that, you know, people are defining whether a model is safe in the context of the recent recent upheaval. If part of this philosophical differences was the baseline, or at least perhaps of for some of the reasons why the board were, were to fire Sam Altman, how can they actually change the way in which they operate to where that's no longer sort of this debate internally in inside of them and they can move forward with a, an approach to releasing these models that is- helps with some of the commercialization aspect where they need the capital in order to get more researchers in and build these models and complete what they say is their mission of building artificial general intelligence, but also help with their mission of doing this in a safe way.

KAREN HAO: I wanna start by unpacking the word "safety" first. And I know we've sort of been talking about a lot of different words with squishy definitions, but safety is another one of those where AI safety as OpenAI defines it, is kind of different from what we would typically think around like engineering safety. You know, there, there have been other disciplines, you know, like when we talk about a bridge being safe, it means that it holds up and it works and it resists kind of collapsing under the weight of a normal volume of traffic or even like a massive volume of traffic. With AI safety, the brand of OpenAI's AI safety, they- it is more related to this, this kind of extreme risk they have. Again, they have started adopting more of this like also focusing on current harms like discrimination, but it is primarily focused on these extreme risks. So the question I guess to to kind of reiterate is sort of like will OpenAI continue to focus on research that is very heavily indexed on extreme risks? I think so, but how are they going to change the structure to make sure that these ideological clashes don't happen again? I don't actually think that's possible, and I also think that part of what we learned from this weekend is that we shouldn't actually be waiting for OpenAI to do something about this. There will always be ideological struggles again because of this fundamental problem that we have, which is that no one knows what AGI is, no one agrees with what it is. It's all a projection of your own ideology, your own beliefs and the AI research talent pool and the broader Silicon Valley talent pool of engineers, product managers, all of those people are also ideologically split on these kind of techno-optimist versus existential-risk divides. So the, even if you try to restructure or rehire or shuffle things around, you're always going to kind of get an encapsulation of this full range of ideological beliefs within the company, and you're going to end up with these battles because of disagreements around what is actually- what are we actually working on and how do we actually get there. So I personally think that one of the biggest lessons to take away is for policymakers and for other members of the general public and consumers to recognize that this company and this technology is very much made by people. It's very much the product of conscious decisions and, and an imprint of very specific ideologies. And if we actually want to facilitate a better future with better AI technologies and AI technologies that are also applied in better ways, and it's actually up to much more than OpenAI it's up to policymakers to regulate the company, it's up to consumers to make decisions that kind of financially pressure the company to continue moving towards directions that we collectively as a society believe are more appropriate. And ultimately what this boils down to is I think like AI is such an important technology and so consequential for everyone that it needs to have more democratic processes around its development and its governance. We can't really rely on a company or a board that is, you know, tiny to represent the interests of all of humanity.

ROBERT CHAPMAN-SMITH: Yeah. In some ways the, the mission of OpenAI for sort of doing it for the benefit of all humanity is, is kind of interesting to have in a technology space- computers broadly-where like open source protocols and ways of working has been such the norm. The board firing Sam Altman and ultimately rehiring him, it sort of kicked off seemingly an awareness of the importance, for some people, of open source AI development and particularly models in that arena, which is, you know, you're, you're mentioning the democratization of AI tools and some, for some folks that is the democratization of these tools. The fact that you can release these models on GitHub or use them on Hugging Face and everybody has access to them. I'm, I'm curious the acceleration of that space tied with the fact that there's gonna be many more competitors that are looking to capture customers off of the turmoil that existed within OpenAI; in some ways it feels like the race for being dominant players in this space might move much more quickly than it was before when it seemed like a OpenAI was just the dominant player and nobody was going to take away the customer base that they had been in the lead that they had had in the models that they had created. I'm curious about your thoughts there.

KAREN HAO: I think what we've seen is not with, with open source models, I would not describe that as democratizing AI development in the way that I was sort of trying to evoke earlier. The thing about democratizing AI development and governance is that people should be also able to say that they don't want things to be released. So, you know, Meta has taken, they're sort of seen as a, as a bit of a champion around open source AI development. They've taken a stance of we're going to release these models and allow anyone to commercialize off of these models. But no one actually has a say other than Meta about whether they released those models, you know, so that in and of itself I think is undemocratic. And part of the, part of the issue as well with the way that Meta so-called "open sources" its AI models is they allow anyone to take it, download it, and manipulate it, but they don't actually tell anyone how this technology was developed. So one of the things that people have really pushed for heavily-researchers, certain researchers have pushed for heavily-is the fact that Meta or any company could actually open source the data that they use to train these models. You know, you could open source the data, understand far more about what's actually being poured into these technologies and you wouldn't actually accelerate necessarily- it's sudden proliferation everywhere. So one, one of the concerns with the way that Meta behaves that, you know, an OpenAI or other type of company that has a more closed model approach argues is, "Oh look Meta is just accelerating this race, this competitive race and actually creating more dangerous dynamics," sort of to your question of like, does that actually make things worse? And, and I would say like there are actually many ways to increase the democratic governance of these, these technologies and kind of the scientific examination of these technologies without actually accelerating this race such as the data open sourcing. And by doing that you then enable many more scientists to kind of study what actually we're- what are we even feeding these models to then also study how what we feed in relates to what comes out. And then you end up hopefully in a situation through a lot of more scientific research and debate and contestation just better models that don't break that work for more people that are hopefully less computationally intensive and less data intensive so they're not as costly to the environment. And you would end up with- what I think would be more beneficial AI development.

ROBERT CHAPMAN-SMITH: The dust hasn't completely settled yet where there it seems like there's gonna be an investigation into the board's decision. Sam is no longer on the board, he was previously on the board, but part of the deal that was made is that he is no longer on the board and neither is Greg Brockman. There's going, there's a temporary interim board that's going to be in a hopefully apoint a larger board you know, given where we are now, what to think of the, the big lessons going forward and potentially where this space is likely to move in 2024 just given this upheaval at the end of the year that nobody was expecting?

KAREN HAO: I think the biggest lesson is that self-regulation just doesn't work. I mean this was the most creative and unique solution for self-regulation that has ever been seen potentially in Silicon Valley history. The idea of having a nonprofit structure that is beholden to no financial anything- and clearly it spectacularly failed. And, and one of the things that I've sort of been thinking a lot about throughout this whole sequence of events is could it have actually actually happened any other way if you set up the structure that way you, you know, like a lot of people were criticizing the board, you know, they, they did not communicate things in the right way. They still have not actually given full transparency about their decision, but arguably they actually did their job. The job description that was written by Altman, by Brockman, by Sutskever was that if they believed that the CEO was no longer upholding the mission, that they fire him. So at some point, it probably was going to happen and the fallout would've been dramatic. And so the fact that it couldn't have happened any other way suggests that in general, even this like super creative over-engineered way of self-regulation is kind of a sham. And the only way to kind of get around regulating the development of these technologies is to have governance by people who govern by people that we've elected that already like the institutions that we've already set up to do these kinds of things. And I think that is, that is like the most important lesson and until we have, you know, I mean I know a lot of policymakers are already moving very aggressively on trying to figure out a way to regulate these companies and regulate this technology, but until we get to that point, and I also think that just anyone in the world who is going to have AI impacting them, their education, their work, their life should recognize that they also have a voice in this. Like we collectively kind of watch these events happening at OpenAI almost completely on the sidelines, right? Like none of us were able to participate in this. None of us were able to kind of vocalize. But policymakers are working on regulations right now. Like there are many Congress people that are working on regulations, there are many agencies in the U.S. or in Europe or in China or anywhere, everyone is sort of advancing on these regulations and in the U.S. we have a unique ability to actually tell the people who represent us what we want to see in those regulations. And I think this was sort of a wake-up call and a rallying moment for all of us to realize that this is what happens when you take a step back and don't participate in voicing your concerns around one of the most consequential technologies.

ROBERT CHAPMAN-SMITH: In the lead up to, you know, the release of some of OpenAI's models, there, there's been sort of like a speaking tour of folks in going to Washington talking to legislators about AI and there was a, the worry at that time was about regulatory capture. Like are they, are folks going to essentially gate the technology in such a way that smaller players are not gonna be able to play ball? And we've seen regulatory capture happen a lot within the political realm within Washington. But there's also this question of like effectiveness in terms of regulation. Like just because the regulation has passed doesn't mean it's actually a good regulation or if this body of Congress is actually able to regulate this fast-moving technology well, like they can't even pass a budget, like how are they gonna keep up with the pace of AI change? So I'm curious about that as a tool for dealing with AI safety because it in some sense it feels like one the the legislative body or processes or capable to be captured by interested parties and two, even when they do regulate sometimes they just do a poor job, they just miss the thing that is the key regulatory factor. So I'm curious about your conception there, and how to deal with some of the messiness that comes with those types of approaches to dealing with technological safety.

KAREN HAO: Regulatory capture is a huge issue and it is definitely a big concern of mine in that and and one of, one of the reasons why we would naturally see regulatory capture in this moment, regardless of whether it's OpenAI at the helm, is that there is a particular narrative that in order to understand and shepherd AI development, you have to be an AI expert. And I think that that narrative is completely wrong because if AI affects you, you have a say, and actually stories about people who are impacted in unexpected ways by these technologies is, as a reporter, that is one of the most enlightening types of stories for me in understanding how a technology should be developed, is seeing how it falls apart and and seeing when sort of- things that were unanticipated end up happening in the real world. And in OpenAI's case in particular, they have also kind of tried to solidify this narrative of expertise by also saying, "Well we're the only ones that see our models," without necessarily acknowledging that it's in part because they won't let anyone else see them. And because regulators, because it is important for regulators to engage with the developers of these technologies, sort of by default, they just seek out OpenAI's opinions on what they should do or Google's opinions on what they should do; Meta's opinions on what they should do. And that's when regulatory capture happens is there's already a baseline belief that only people with expertise should participate. And then on top of that, companies are like trying to entrench this and fuel this narrative and then policymakers buy into the narrative. And that's how you end up with like Sam Altman on this global tour seeing all the heads of state and the heads of state not necessarily creating the same kind of grand welcome for other stakeholders within this AI debate. You're right also that there are concerns around how effective that regulation can be. I do think what I'm talking about with like having more people speak up about how AI affects them and their concerns about the technology is one antidote to ineffective regulation because the more that policymakers can understand the literal real-world examples of the technology interfacing with people, the more that they can design regulation that is effective. But the other thing is I think we focus a lot on kind of federal-level regulation and we focus a lot on in our government, international regulation, but there's a lot that happens at the local level as well, like school boards. Schools are thinking about how to incorporate AI in into the classroom right now. And as a parent, as a teacher, like you should have a say in that you are the one, if you're a teacher, you're the one that's using this technology and you're the one that knows your students. So you will be the most informed in that kind of environment to say whether or not you think this technology is going to help in kind of the general mission to educate your kids. It's also like police departments are acquiring AI technologies and people within cities should have a say as to having more transparency around the acquisition of these, these technologies and whether or not that should be acquired at all. And I think in these local contexts, sometimes these contexts, actually regulation is more effective because it is more localized, it is more bespoke to that context, and it also moves faster. So I think that is sort of an important dimension to add is when I say "Speak up and voice your opinions," it's not just to the federal agencies, it's not just to the Congress people actually, just like within your city, within your town, within your school, within your workplace, these are all avenues in which you can kind of speak up and help shepherd the development, adoption and application of the technology.

ROBERT CHAPMAN-SMITH: Karen, thank you so much for joining us on Big Think and sharing your expertise with our audience about OpenAI and all the things that are happening in the world of AI.

KAREN HAO: Thank you so much, Robert.