Your Undivided Attention - Former OpenAI Engineer William Saunders on Silence, Safety, and the Right to Warn

Episode Date: June 7, 2024

This week, a group of current and former employees from OpenAI and Google DeepMind penned an open letter accusing the industry’s leading companies of prioritizing profits over safety. This comes after a spate of high profile departures from OpenAI, including co-founder Ilya Sutskever and senior researcher Jan Leike, as well as reports that OpenAI has gone to great lengths to silence would-be whistleblowers. The writers of the open letter argue that researchers have a “right to warn” the public about AI risks and laid out a series of principles that would protect that right. In this episode, we sit down with one of those writers: William Saunders, who left his job as a research engineer at OpenAI in February. William is now breaking the silence on what he saw at OpenAI that compelled him to leave the company and to put his name to this letter. RECOMMENDED MEDIA The Right to Warn Open LetterMy Perspective On "A Right to Warn about Advanced Artificial Intelligence": A follow-up from William about the letterLeaked OpenAI documents reveal aggressive tactics toward former employees: An investigation by Vox into OpenAI’s policy of non-disparagement.RECOMMENDED YUA EPISODESA First Step Toward AI Regulation with Tom WheelerSpotlight on AI: What Would It Take For This to Go Well?Big Food, Big Tech and Big AI with Michael MossCan We Govern AI? With Marietje SchaakeYour Undivided Attention is produced by the Center for Humane Technology. Follow us on Twitter: @HumaneTech_

Hey everyone, this is Tristan. And this is Aza. You might have heard some of the big news in AI this week, that 11 current and former Open AI employees published an open letter called A.Right to warn. And it outlines four principles that are meant to protect the ability of employees to warn about under-addressed risks before they happen. And Tristan, I sort of want to turn it over to you because they're talking about the risks that happen

that are underdressed in a race to take shortcuts. Yeah, so just to link this to things we've talked about all the time in this podcast. If you show me the incentive, I will show you the outcome. And in AI, the incentive is to win the race to AGI, to get to artificial general intelligence first, which means the race to market dominance, getting as many users, onto your AI model, getting as many people using chat GPT, doing a deal with Apple, so the next iPhone comes with

your AI model, opening up your APIs so that you're giving every AI developer exactly what they want, have as many plugins as they want, do the trillion-dollar training run and, you know, show your investors that you have the new exciting AI model. Once you do all that and you're going at this super fast clip, your incentives are to keep releasing, to keep racing. And those incentives mean to keep taking shortcuts. Given the fact that there is no current regulation in the U.S. for AI systems, we're left with, well, who sees the early warning signs? It's the people inside the companies. And those people can't really speak out, which is what we're going to get into in this interview.

Just days after OpenAI released their latest model, GPT40, two of their most senior researchers, that's co-founder Ilya Sutskhaver and Ian Leika, announced that they were leaving the company, that they'd resigned. And in fact, they're part of a steady stream of engineers and researchers leaving OpenAI, and they've all given pretty much the same reason for the departure, that Open AI is prioritizing market dominance and speed at the expense of safety, which is exactly what the incentives predict. And that brings us to the letter and our guest today.

William Saunders is a former engineer at OpenAI, where he worked for three years until his resignation in February. He helped write the letter to draw attention to the safety issues and the lack of transparency from within all of the AI companies. As one of the very few insiders talking publicly, we are keen to share his insights with you. So, William, thank you so much for joining us. Thanks for talking to me.

So we absolutely want to dive into the open letter, the right to warn that you helped write. But first we need to give listeners a little bit of context about who you are. Can you tell us what your role at OpenEye was, how long you worked there, and what did you do? Yeah, so I worked at OpenEII for three years. I did a mixture of research and engineering necessary to do that research.

I was working on the alignment team, which then became the super alignment team. And I was always motivated in my work to sort of think about what are the issues that are going to arise with AI technology, not today, but tomorrow, and sort of thinking about how can we do the technical work that we need to prepare to address these issues. Could you quickly just define the difference between alignment and super alignment? So alignment is broadly trying to make systems that you know will sort of do what the user of the system wants and also be good citizens in the world, not do things that like people, you know, society generally wouldn't want. And then super alignment was more specifically focused on the problem of how can you do this when the systems that you're building might be as smart or smarter than you.

So that was sort of the first area that I worked on. And I think this was worked on before this became sort of a big problem that has now been in the news recently about Google search, the AI system producing sort of incorrect answers. And then from there, I transitioned to working on interpretability. So William, what is interpretability research and why is it important to safety? Interpretability is about trying to understand what goes on. inside of these large language models. So these large language models and other sort of machine learning systems

that we produce are somewhat unique amongst technologies in that they aren't produced by sort of like humans putting together a bunch of pieces where they each have designed the pieces and they understand how they fit together to achieve some goal.

Instead, these systems are produced by starting with what you want the system to do So, for example, you produce a system that takes like some piece of text from the internet and then tries to predict what would be the next word that comes up in this text. And then the machine learning process produces at the end a system that can do this task very well, but there is no human who understands the individual steps that go into this. And this can be a problem if, for example, you train the system to do one thing, but then in the world it's applied in a new context, then it can sometimes be hard

to predict what the system will actually do. So we worked on doing this and then doing further research, building on these techniques to try to understand what is going on inside any like given language model, like the reasoning process it went through. I think something that most people listening to this podcast might not understand is that it's not like when you as an engineer are working on a AI system, you're not coding how the system works line by line, you are

training the system, and it develops sort of emergent capabilities, the sort of metaphor that came into my mind, is that our genes, like DNA genes, they just want to propagate, make more of themselves, and in the process of doing that,

it creates all of this extra behavior, which is humans, human culture, like language, these are not things that the genes ever asked for is just them following this sort of very simple process. And the job of interpretability is sort of like saying, I'm looking at a whole bunch of DNA as a scientist. What does it do? How do I figure out which things are proteins, how that interacts with the system? And it's a very complex, very challenging thing to do because DNA is also sort of a black box. Is that a good way of describing it? Yeah, I think that's a really good

analogy. So the way that machine learning systems are produced, one analogy you can think of is you take a box with a bunch of parts. It's got a bunch of gears and a bunch of springs and a bunch of levers or whatever. And then you give the box a shake. So it starts off in some kind of random configuration. And then, you know, suppose on one end of the box you like enter some input and then on the other end of the box, it prints out a word. And then you can see sort of given the inputs, does the box, you know, print out the right word at the end. The next step is you give the box a shake and you try

to only change the pieces that aren't working well and keep the pieces that are performing the task you want and are predicting well. And you do this millions and billions of times and eventually at the end you produce a box that can take in a sequence of text and

then produce coherent answers at the other end. And after that your hands and your arms are very tired from shaking the box so many times. Except that you had a computer do it billions of times for you. So if you try to ground this for listeners, you're taking a big risk here with your colleagues at OpenA.I. And you're coming out and saying, we need a right to whistleblow about important things that could be going wrong here. So far, what you've shared is sort of more of a technical description of the box and how do we interpret the neurons in the box and what they're doing.

Why does this matter for safety? What's at stake if we don't get this right? I think, you know, you can take, suppose we've like taken this box and it like does the task. And then, you know, let's say we want to take every company in the world. and integrate this box into every company in the world where this box can be used to, you know, answer customer queries or process information. And let's suppose, you know, the box is like very good at, you know, giving advice to people. So now, you know, maybe CEOs and politicians are like getting advice. And then maybe as things progress into the future, maybe this box is generally regarded as being, you know, as smart or smarter than most humans and able to do most jobs.

better than most humans. And so now we've got this box that nobody knows exactly how it works, and nobody knows sort of how it might behave in novel circumstances. And there are some specific circumstances where, like, the box might do something that's different and possibly malicious.

And again, this box is as smart or smarter than humans. It's right in OpenEI's charter that this is like what opening eye and other companies are aiming for. Right? And so, you know, maybe the world rewards AI's that try to sort of like gather more power for themselves.

If you give an AI a bunch of money and it goes out and makes more money, then you give it even more money in power and you make more copies of this AI. And this might reward AI systems that like really care more about getting as much money and power in the world

without any sense of ethics and what is right or wrong. And so then suppose you have a bunch of these questionably ethical AI boxes integrated deeply into our society, advising politicians and CEOs, this is kind of a world where you could imagine, gradually or suddenly, you wake up one day and, like, humans are no longer really in control of society. And, you know, maybe they can

run subtle mass persuasion to, you know, convince people to vote the way they want. And so it's very unclear how rapidly this kind of transition would happen. I think, you know, there's a broad range of possibilities. But some of these are on timescales where it would be very hard for people to sort of realize what's going on. This is the kind of scenario that keeps me up at night that has sort of driven my research. You want some

way to learn if the AI system is giving you bad information. But we are already in this world today. I think what we've established is a couple things. One is that, like, William, you're right there at the frontier of the techniques

for understanding how AI models work. and how to make them safe, that I think what I'm hearing you say is there's sort of like two major kinds of risks, although you said there are even more. One of them is if AI systems are more effective at doing certain kinds of decision-making than us, then obviously people are going to use them

and replace human beings in the decision-making. If an AI can write an email that's more effective at getting sales or getting responses than I am, then obviously I'm sort of a... sucker if I don't use the AI to help me write that email. And then if we don't understand how they work, something might happen, and now we've integrated them everywhere, and that's really scary. So it's sort of like risk number one, and then risk number two is that we don't know their capabilities. I remember GPT3 was shipped to at least tens of millions of people before

anyone realized that it could do research great chemistry, or that GPT4 had been shipped to 100 million people before people realized it actually did pretty well at doing theory of mind, that is being able to strategically model what somebody else's mind is thinking and change its behavior accordingly. And those are the kinds of behaviors we'd really like to know before it gets shipped, and that's in part what interpretability is all about, is making sure that there aren't hidden capabilities underneath the hood. And it just leaves me actually to sort of a very personal question for you, which is, if you've been thinking about all of this stuff, like why did you want to work at Open AI in the first place? So one point to

clarify interpretability is certainly not the only way to do this, and there's a lot of other research into sort of like trying to figure out what are the dangerous capabilities and even try to predict them. But it is still in a place where nobody, including people at Open AI, knows what the next frontier model will be capable of doing when they start out training it or even when they have it. But yeah, the reasoning for working at Open AI came down to I wanted to do the most useful cutting-edge research. And so both the research projects that I talked about were, you know, using the current like state of the art within opening eye. The way that the world is set up, there's a lot more friction and difficulty if you're

outside of one of these companies. So if you're in a more independent organization, you know, you might have to, you have to wait until a model is released into the world before you can work on it. You have to access it through an API. And there's only sort of like a limited set of things that you can do. And so the best place to be is within one of these AI labs and that comes with some strings attached. What kinds of strings? So while you're working at a lab, you have to worry about if you communicate something publicly, will it be something that someone at the company will be unhappy with?

In the back of your mind, it is always a possibility to be fired. And then also there's a bunch of subtle social pressure. You don't want to annoy your coworkers, the people you have to see every day. You don't want to, like, criticize the work that they're doing. Again, the work is usually good, but the decisions to ship, you know, the decision to say, like, we've done enough work, we're prepared to put this out into the world, I think is a very tricky decision. So maybe you should just quickly ground that for listeners, because my understanding is you also wanted to work at OpenAI because they were a place that wanted to do AI safely. Yes. But what you saw were that there were decisions to take shortcuts in releasing AI systems that would provoke

this sort of need to speak up, can you talk about what kinds of shortcuts you're worried about, what kinds of shortcuts you might have seen be taken in the past that are safe for you to talk about? Or maybe one of the points of your letter is that there's many that you can't talk about that you want protections so that you can because it's important for the world to know. Yeah, so I think it's like if you have this new model, there's always a question of like, what is the date that you ship and then how much work do you do in preparing the model to be ready for the world. And I accept that this is a complex question with a lot of tradeoffs. However, I would see these decisions being made in a way where there was like additional work that could

be done to make it better, or there was work that was rushed and not very solid. And this would be sort of made in service of meeting the shipping date. And it's complicated because there were also times when, you know, they would say that like the ship date is pushed back so that we can do more safety research. But like overall, over time, over multiple instances, I had other people at the company felt that there was a pattern of putting more pressure to ship and, you know, compromising processes related to safety. And problems that happened in the world that were preventable. So, for example, some of the weird interactions with the Bing model that happened at deployment, including conversations where it ended up like threatening journalists, I think that was

avoidable. I can't go into the exact details of why I think that was avoidable, but I think that was avoidable. What I wanted from Open AI, and what I believed that Open AI would be more willing to do was, you know, let's take the time to get this right. When we have known problems with the system. Let's figure out how to fix them. And then when we release, we will have sort of like some kind of justification for like, here's the level of work that was appropriate. And that's not what I saw happening. Was there a single thing that made you want to resign? I think it was like, again, a pattern of different incidents over time. and I think you can go from an attitude

like when I started of sort of like hoping that opening eye will do things right and hoping that they will listen and then you slowly move towards an attitude of I'm kind of afraid that things are like not happening correctly but I think it's good for people like me to stay at the company

in order to be a voice that is like pushing more for taking things more cautiously and carefully and getting it right before shipping But then you go from that to a perspective of they are not listening to me. And I am afraid that they won't listen to me even if the risks and the stakes get higher. And there's like more immediate risk. I also think as time goes on, right, there will be even more gigantic piles of money going into this, into these systems. Every generation is sort of like another multiplicative increase in the end.

amount of money that's, like, required. And so there will also be this extraordinary opposing pressure to get the value from this massive investment. And so I don't think you can trust that this will improve. I think this is such a critical point, you know, what was it, GPT3 was roughly $10 million, GPT4 was roughly $100 million to train, GPT5 for it is like roughly a billion dollars, and this is their multiplier. And I think the point you're making is if you've just spent a billion dollars to trade a model or a $10 billion to trade a model, your investors are going to require return.

And so there's a very strong incentive to release, to product ties. And let's return to that. But I really am curious, your experience inside, did you specifically raise safety concerns that then were ignored? Or how did that process go? let's see maybe I can talk about there were some public comments from another member of the super alignment team who sort of talked about raising security concerns and then being reprimanded for this and then later sort of like being fired with this cited as one of the reasons that they had done this in an improper way but I don't think think those concerns that were raised were addressed in a way that I felt comfortable with. I think I was, like, worried about the concerns that were raised there, and it was even more worrying to see, like, their response is, like, reprimanded the person who raised the concerns

for raising them in a way that was impolite, rather than being like, okay, this wasn't the best, but, yes, these are serious concerns, and we will address them. That would have made me feel a lot more comfortable rather than the response that I saw. I know I think in your pre-interview with Sasha, you did also mention that you thought the investigation into Altman wasn't satisfactory. I don't want to make it personal in any way, but I just want to make sure we're covering any bases that are important for people to know, and then we can move on. And if not, it's fine, we can skip it. Yeah, it is, like, I think I want to be careful what I say.

I think when Sam Altman was fired and I think ever since then it was sort of a mystery within the company what exact events led to this sort of like what interactions were there with Sam Altman and the board or other people within the company that led to this and I think the investigation when they reported they said words to the effect of like we conclude that the board acted within their authority to fire Sam Altman

but we don't believe that they had to fire Sam Altman and I think this leaves open the mystery of what happened and how serious it is because this is like perfectly compatible with Sam Altman having a history of unethical but technically legal behavior. And I really would have hoped that the investigation would have shared more, at least with the employees, so that people could decide for themselves how to weigh this instead of leaving open this mystery. And so even if Sam, you know, was wronged by this process, I can't tell. And so, yeah, I felt very uncomfortable with how the investigation concluded. It did not make me feel like the issue was closed.

One of the pieces of rhetoric we're starting to hear from companies is about science-based concerns that the kinds of risks that you were talking about at the beginning, They're sort of trying to paint as fantasy, future, and what the companies care about are science-based risks. And I just would love for you to talk a little bit, maybe, like, debunk, like, what's going on there when they're trying to use this phrase to discredit the risks that even Sam Altman has talked about in the past? So one way to maybe put this is, like, suppose you're, like, building airplanes, you know, and you've so far, like, only run them on short flights overland. and then you know you've got all these great plans of like flying airplanes over the ocean so you can go between like America and Europe and then someone you know like starts thinking like gee if we do this then maybe like airplanes might crash into the water and then someone

else comes to you and says like well we haven't actually had any airplanes crash into the water yet like you think you know that this might happen but we don't really know so let's just you know like let's just start an airline and then see if maybe some planes crash into the water in the future. You know, if this, if enough planes crash into the water, we'll fix it. Don't worry. You know, I think there's a big, there's a, there's a, there's a, there's a really important but subtle distinction between putting in the effort to prevent problems versus

putting in the effort after the problems happen. And I think this is going to be critically important when we have, you know, AI systems that are at or exceeding human level capabilities. think the problems will be so large that we do not want to, you know, see the first, like, AI equivalent of a plane crash, right? I don't know to what degree we can prevent this, but I would really, really, really, really want us to make our best shot. And I would really, really want a strong reason to do something that is, you know, we're releasing earlier than making our best shot. And I never had anything like this that convinced me while I was at Open

one of the overall themes that's related to this interview in your letter or right to warn is the safety of people who are able to speak up about an issue my understanding is that wilmer hale the law firm that was tasked with investigating and interviewing people about sam's behavior did not grant confidentiality to the people that they interviewed and so there's sort of a parallel here to if you don't have confidentiality to share certain things about what's going on, you're not going to be able to share important information. Which brings me back to the letter that you co-wrote, which has 13 signatories, and 11 of them are current Open AI employees. It's also been endorsed by some of the biggest names in AI, Joshua Benjillo, Jeffrey Hinton,

Stuart Russell. And in the letter, which they call a right to warn, you explain, you have four basic principles. Can you give an brief overview of what a right to warn principles are? Yeah, I think the current incentive structure is the only people with the information. are inside of these companies. And then they're sort of, in some ways, subtly and in some ways, like, more overtly discouraged from interacting with the media or posting things in public when they might be, you know, contrary to the image projected by the company. And I think a big part of this problem here is, like, if there is something really big,

if there is a group of people who are being seriously harmed, you know, I really think that people will sort of ignore these incentives and speak up. But when there are some things that are starting to go wrong, when it's like the process wasn't great, but the outcome was okay, the thing we were shipping wasn't dangerous. I think these kinds of things aren't as big and dramatic, but they need to be addressed. And so these incentives really prevent people from, you know,

talking about this category of things, which then means that the company, doesn't face any pressure to address them before there's a big crisis. So the first principle really arose out of the situation at Open AI where, you know, when I resigned from the company, I was given a non-disparagement agreement, and, you know, I was informed that I would, if I did not sign this non-disparagement agreement, my vested equity would be canceled, which is something like shares in the company. that I was hoping that I would be able to sell at some later date. And the value of this, of these shares, was like millions of dollars.

And then this agreement said that you can't disparage the company, which means that you sort of can't even make statements that are based purely on public information, but that are negative about the company. And then this agreement itself had to be kept secret, so you couldn't say to anyone, like, oh, opening eye forced me to sign in on a agreement, so I can't say anything negative, right? And so you effectively, you can only say positive things or say nothing at all. And I think this is like a really unethical practice. And this, the original version of this agreement, I was told that I would have to decide whether

it to sign within seven days, which places a lot of time pressure. So like all of this, I think, was an extraordinary unusual practice. You know, the only other place that I've seen evidence of doing this kind of thing is TikTok, which is not very illustrious company to be in. Forbidding statements that are based on public information that are critical of the company is just unacceptable, especially when it applies for the rest of your life, potentially, even after you've left. So the company told me in a letter that they do not intend to enforce the agreement that I signed. However, they still have the power to never let me sell the shares that I have in the company. And there are no limits on this power. They have not clarified

as of yet like how they would use this power. And so by talking to people like you and the media, it's possible that the company has already decided that they will like never let me sell. And so I think you should just never have to face like, do I lose millions of dollars or do I say something that's like slightly critical of the company? That's pretty ridiculous. Right. So we've heard you say, you know, this mix-ups. of legal threats and threatened loss of equity is discouraging people from speaking out. The second principle, you wrote, is to make sure companies establish an anonymous process for employees to raise risk-related concerns to the board and regulators?

Yes. You know, we wanted to go on from this and then be like, what would a truly responsible and open company actually do? And that sort of generated the ideas for the other principles. So principle two is about, what's the way that's most compatible with the company's legitimate interest to protect confidential information and intellectual property, but still allows the concern to go to the appropriate parties so that an employee can feel like it will be properly addressed and it can't sort of be dismissed or hushed up or downplayed. And so the idea is here

you have some kind of hotline or process for like, you know, submitting concerns where the person submitting the concerns is anonymous, so they can't be retaliated against. And then the concern simultaneously goes to the board of directors of the company, to any appropriate regulators, and also to someone who is both independent and has the technical expertise to be able to evaluate the concern. And I think this is important because a lot of these kinds of risks are not necessarily covered by existing forms of regulation.

And even if they are, the regulatory bodies might not have the expertise to be able to understand and evaluate them. And I think, you know, a lot of the concerns that I had, if they could go to someone who is independent and understands them and they say, okay, William, I understand what you're saying, but, you know, I think this isn't a critical problem. I think this isn't creating a huge issue or it's fine if the company, you know, takes some time to address this later.

I would be, you know, much more at ease. And then I think principle three is, you know, about this, creating a culture where it is okay to say something that might be critical about the company and it is okay to say something that might be like talking about things inside of the company. So like the confidentiality provisions can be any non-public information. So the idea is just make it so that it is clear that it is okay for everybody to talk about these kinds of things that don't touch core intellectual property or trade secrets. And then principle four is sort of what happens if these other

processes aren't implemented, or if these other processes have failed to adequately address the concern. And this is more like a request to companies that if someone has tried to submit this through proper channels, and it's like clear that this has not been adequately addressed, it's a request to not retaliate against employees who then go public. Yeah, what I hear you saying is you're trying to be balanced in not giving whistleblowers is carte blanche to just say negative things, but at the same time, you know, it's sort of an analogy, like imagine a power company was developing nuclear fusion technology, and they believed that that nuclear fusion technology was extremely dangerous, and then the safety team left

and were blocked from being able to talk about their safety concerns. That would be extremely worrying. And that's sort of the place that we are now. It's sort of like, you know, the safety teams, and you are a canary in the coal mine for extremely powerful technology. And we need to know, there's a right for the public to know if the canaries are dying or filling ill. And that's what this letter is asking for. It's the right to warn. That's what you're asking for here. Right.

And I think really adopting these principles would cause less conflict and drama around this that I don't think any of us want. But yeah, and I think another analogy to make here is just like, you know, in the history of social media, right, you know, if you're dealing with a social media algorithm and there's like a way that it is set up that is like clearly not in the public interest, right? The company would say, oh, this is confidential information, right? And so, again, it's a similar situation of the only people who know aren't allowed to talk about it. And so there's no public scrutiny of what goes on. And I think adopting these kinds of principles would have. have helped a lot in those kinds of situations in the past. Yeah, and moreover, you know, we have an experience with speaking with whistleblowers and social media, Francis Hogan, among them, that the net effect of people like Francis speaking out is that companies have an incentive to shut down their internal research on safety and trust and integrity because now they, if they know and they looked, then they're liable for the fact that they, what they found, what they saw. And so what we've seen at Facebook, for example, is

through whistleblowing, it disincentivized more research, and it shut down CrowdTangle, which was the internal tools to help determine what's going on in various countries. And so how do we incentivize that earlier on? And I just love that provocation. Like if we had had the right to warn in social media earlier in such a way that we also required companies to look at things at the same time rather than allowed them to shut down internal research on safety, you know, where could we be in a different, you know,

where could we be now? Yeah. And if you look at open eye, like, look, the people. People that I trusted most to try and look at the problems that could happen as this technology is scaled up and try to work in preventing them, they are leaving, right? That is like happening. You know, they will no longer be there. Yeah. It should be very concerning for listeners.

Take a breath. Like, you've taken a really major stand, right? Like, you are at risk are millions of dollars, potentially reputation, ability to work in other companies, potentially. Like, you're really seeing something that's important. And from that vantage point, what do you really want people to know? Like, what's the one most important thing? Like listeners to this podcast, which are regulators and technologists, what's the most important thing for them to take away? I think if any company in this industry comes to you and make some claim like we have done a bunch of work to make the product safe before deployment or we have a team that is responsible for making the product safe before deployment or we have this process that we follow to make sure that the product is not dangerous for deployment. I think

I think nobody should take this at face value from any company in this industry. Again, there are a lot of people who really want to get this right. There is a lot of genuinely good work that happens. But I think the decision of whether this is sufficient or whether the amount of work that has been done is in line with the public interest, you know, I don't think you can trust anyone at these companies to be making that judgment themselves. And so really the world that I would like to move towards is where there can be somebody that can independently evaluate. Have you gone through the process properly? Have you met the commitments that you've previously made? Have you addressed

the like known problems? And I think that's sort of the only way to move forward. But I think companies, you shouldn't allow companies to deflect this concern by saying, like, we did this list of five things or whatever, because you can be like, yes, we did these five things, but there's a bunch of other things we didn't do, or we don't know how much of the problems that these five things would have done. And so, again, the only way I can see is independent experts who have access to see what is actually being done, and you can actually make this call without having a massive conflict of interest. And I got maybe one final sentiment here is I do think we can figure this technology out.

I do think we can do it properly. You know, I think that is totally possible. But we have to, you know, be willing to put in the hard work to do that. William, thank you so much for coming on your undivided attention and for really taking the risk that you're taking to have the world understand the risk that we're under. and what we need to make it more safe. Thank you for on behalf of all of us. Thanks for having me on this podcast.

Your undivided attention is produced by the Center for Humane Technology, a nonprofit working to catalyze a humane future. Our senior producer is Julia Scott. Josh Lash is our researcher and producer, and our executive producer is Sasha Fegan. Mixing on this episode by Jeff Sudaken, original music by Ryan and Hayes Holiday. And a special thanks to the whole Center for Humane Technology team

for making this podcast possible. You can find show notes, transcripts, and much more at HumaneTech.com. And if you like the podcast, we'd be grateful if you could rate it on Apple Podcast because it helps other people find the show. And if you made it all the way here, let me give one more thank you to you for giving us your undivided attention.