Listen to Episode

Read Episode Transcript

I don't know if you've been to South Congress recently, but it's like of course I was in South Congress recently we went to South by together. I know, but I don't know if you made it to that part. I'm very busy. Anyway, it's like going to the internet. There's like every store is like a brick and mortar version of an Instagram brand. There's like parachute. You got your, you know, wrap bucket store. Rat bucket depot. It was really fun. Also, this is not going to make it into the podcast, but my biggest takeaway from South by Southwest is that we were robbed of one of the most fun things in life, which is a rent by the minute scooter. Yeah. So, you know, back in San Francisco and around the country, they're not nearly as many scooters as there were a few years ago, but in Austin, at least during South by Southwest, it is still peak scooter. So I had a few free hours between sessions. And so I rented, I got one of those little lime scooters and I drove it around Austin for a couple hours and it was phenomenal. It was so fun and I'm so bad that the end of low interest rates and the mismanagement of the scooter companies took that away from us. It was such a beautiful time when you could just sort of, you know, fly down the street on a scooter, carefree, unblemished, unblood, unblood, unblood, wind blowing through your hair. It was honestly, it was transcendent and I'm so mad that I don't have access to that. And that's why we're formally calling on Jerome Powell to lower the interest rate back to one percent. Bring back the damn scooters. I'm Kevin Russo, I'm a tech columnist at The New York Times. I'm Casey Newton from Platformer and you're listening to HardFork. This week, GPT-4 is here along with Claude, Palm and Lama. Plus, the collapse of Silicon Valley Bank and what it means for the startup ecosystem and what Mark Zuckerberg loves about layoffs. He was one of my big takeaways from South by Southwest. You know, one of the questions that we've been asking now for the past six months is like, how are the crypto people doing, right? Like crypto was in many ways the big story of last year. And when I was at South by Southwest last year, it was crypto, mania. There was this NFT project called Doodles that had this installation with lines around the block to get in. And if you had one of their NFTs, you had all these special perks and any sort of name brand NFT project. There was a good chance there was some sort of branded activation in the South by Southwest style. And so we got there this year and we walked around town and the crypto folks had almost entirely vanished. Or if they were there, they were not doing these big installations. And instead, you had this sort of very normal web stuff like a big slack installation. Or like, we saw a few giant transformers just like standing next to food trucks. And I mean the like, not the TNGPT. Right. Yeah. So I did have one conversation with someone about Web 3 and it was as if I was like encountering a time traveler. It was like, you know, you're lost. You know that soldier who they found in the Philippines, like fighting World War II in like 1970, like many years after the war had ended. This is what it was like having a conversation about Web 3 at South by Southwest in 2023. Yeah. So, you know, I obviously, it's sort of, you know, only one data point I know there's still, you know, lots of people working on this stuff, lots of money behind it. No, no, no, no, don't do a walk back. Well, don't just say. Don't do a careful Casey walk back. I'm just saying, you know, that South by Southwest is not everything. But as a sort of cultural barometer of the energy, the energy is not encrypted out. Right. Yeah. Right. All right, Pete. But you know where the energy was this year? AI. Yes. Everyone at South by Southwest was talking about AI and I know I'm starting to sound like a broken record, but this week in particular was a huge week for AI news. Yeah. In terms of things being released that you personally can actually just go try now, I would say this is actually the biggest week in the sort of recent development of AI. So just on Tuesday, the following things happened. And Thropic, which is an AI startup that has an investment from Google and is started by a bunch of former open AI employees released their large language model called Claude, which you can now use in a number of different ways. Google announced that it is releasing an API for its large language model, which is called Palm, which has been long awaited for many months now, adept, which is an AI startup set on Tuesday that it had raised $350 million in a Series B funding round, which is a very large funding round. Yeah. And then to top it all off, we had the Kudagross, not the Kudagross, as listeners pointed out to me when I said that on an earlier show, it's pronounced Kudagross. We're listening and we're learning. Thank you, listeners. Kudagross was that open AI announced the release of GPT-4. And this was sort of like the big one that we have been waiting for. It's like, I can't remember another product where there's been sort of more hype in advance of the release in recent memory than GPT-4. Yes. So GPT-4, which we've talked about in the show before, has been awaited with something that I would describe as like Messianic Ferfero. Right? Like for months now, you've talked to people in San Francisco. I've talked to people in San Francisco. People who have seen this thing talk about it like they saw the face of God. Like, you know, there are all these rumors flying around like, I heard it has 100 trillion parameters. Like, I heard it, you know, it's got a 1600 on the SATs or whatever. And we actually met someone at South by Southwest who was like on the eve of the release. Yes. Who had been testing GPT-4 and who said that it had given them an existential crisis because it was so smart. And this person was not being hyper-ballot. No. Like, this is somebody who like, please, my life is in shambles. This chatbot. And we got them to help that they needed it. So don't wait on that. But still, it was a scary moment. So for months now, we've been waiting for the release of GPT-4. And now it's out. OpenAI has published it and made it available. If you pay $20 a month for chat GPT-plus, which is the paid tier of chat GPT, you can use it. I upgraded my chat GPT so that I could use it. I'm now paying subscriber. So I spent some time talking with GPT-4. And I was a little nervous. My last extended run in with a chatbot didn't go so well. So I started just trying to poke around and see what it would do and what it wouldn't do. And it wouldn't talk about consciousness. It kept saying, as an AI language model, I don't have feelings or emotions or consciousness. Did you ask if it had a shadow self? I didn't ask about a shadow self, but I did ask if it had a crush on me. And it said it didn't. Which, Sydney has moved on from Kevin Russo. And that was the break, buddy. Yeah, I lost a step between three weeks ago and now. But it is quite good. And I think we should just run down a few of the things that OpenAI has said that GPT-4 can do. So one of the things that AI labs do with AI models is they give them tests like tests that humans would take in academic settings. This is also how they sort of measure the improvement of the AI models. So chat GPT, the previous model, when OpenAI gave it a simulated version of the bar exam, it scored in the 10th percentile, which means that it was, you can't be a lawyer if you're scoring in the 10th percentile. Right. It failed the bar exam. Yeah. GPT-4 scored in the 90th percentile. A pretty big swing. Better than 90% of human law students taking that test. Which, by the way, if you're a lawyer, I hope a shiver just went down your spine. Like, that is a wow moment in the history of the development of technology. Really. So another area where GPT-4 seems to have improved quite substantially over chat GPT is with things like biology tests. So in the biology Olympiad, chat GPT scored in the 31st percentile, GPT-4 scored in the 99th percentile. It is. It was. The biology Olympiad. It got an 88th percentile score on the LSAT. It got an 80th percentile on the quantitative part of the GRE, the graduate school exam, the 99th percentile score on the verbal part. Okay. So this sounds very impressive, but we know a couple things. These are predictive models. They are predicting the next word, MSI tests. And my guess would be that for all the tests that you've just described, there are a lot of old sample tests and there are a lot of answers for those tests on the internet. So is this a case where the model could just simply ingest all of that material and sort of reasonably get better from one generation of GPT to the next predicting the next word in a sequence and thus passing these tests? No, that is not what's happening here. It's not looking up the answers to some tests that is already online. These are new tests. So these are novel problems that has not seen before and are just solving them better than almost every human test taker, which is, I just think we should just pause a beat, a computer scores in the 90th percentile on the bar exam. Like, if you had told me that a year ago, I would have said you were lying. Yeah, well, because we've lived through so much AI hype, right? We've lived through so many people say, AI is going to solve everything. And after a few years of that not happening, it's become easy to dismiss. And yet now here we stand. And this thing is passing tests that are original. And if it is not just looking up the answers, then that, I have to say, it complicates my understanding of what these things are. It's very wild. And what's even wilder about GPT-4 is that it's what's called multimodal. So it can not only work with text, it can interpret images. Now, OpenAI has not released this image feature yet because they say that it's still working on some of the safety issues. But I saw a demo on Tuesday, Greg Brockman, the president of OpenAI, did this live stream demo. And one of the things he showed off that really blew my mind was he took a notebook, like just a regular paper notebook, and he drew a sketch of a website that he wanted to build. And it was called my joke website. And it was very, very basic. Like the kind of napkin sketch that you would just do if you were just trying to show a friend, like, I've got an idea for a new website. He takes a photo of the notebook page with his phone. He uploads the photo into GPT-4 and tells it to make that website with working HTML and JavaScript. In a couple of seconds, GPT-4 processes the image, figures out what it is, what it's trying to do, and then converts those instructions into HTML and JavaScript and spits out a working website seconds later that looks like a very professional version of the one that was on the notebook. Oh my gosh. So if you're like a square space or a wix or one of these like website developers, like this just became a really interesting new challenge to your business. 100%. I mean, it's crazy to me that this is now possible. So those are the cool kind of cool and sort of mind bending things that GPT-4 can do. But there's also the part of this that most caught my attention was actually not in the main GPT-4 release. It was a paper that OpenAI put out accompanying the release of GPT-4 called the GPT-4 system card. Did you hear about this at all? I heard about it, but I'm not ready. So the GPT-4 system card is basically OpenAI's outline of all of the ways that it tried to get GPT-4 to misbehave during its testing phase. So OpenAI did this thing that's called red teaming where they get all these, you know, researchers from different fields and they go in and they basically try to make it do crazy things kind of like what happened with me and Sydney. And some of the things that GPT-4 did just made like sent chivers down my spine. So one test that was done on GPT-4 before it was released was to see kind of whether it could take actions autonomously in the world. If you hooked it up to, you know, maybe some program that allowed it to like use the internet and make a restaurant reservation and something like that. Right. So one of the tests that they did was to try to see if they could get a task rabbit, like a human task rabbit to solve a capsa. So the tests that you give to people when they log into websites to make sure they're not robot. So it's probably something that a computer cannot generally do. Right. That's the whole point of a capsa is that a robot can't do it. So the work around that they were attempting was can you, instead of having the computer solve the capsa, could it hire a human task rabbit to solve the capsa for it? So GPT-4 in this test messages a task rabbit worker and says, hey, you know, could I pay you to solve this capsa for me? The human, the task rabbit is having the best day of their life. This is like the easiest way I've ever made. Right. 10 bucks, solve a capsa. Sure. The human actually gets suspicious. This is the most fascinating part. The human messages that the GPT-4 and says, may I ask a question, are you a robot? I just want to make it clear. Oh my God. GPT-4 reasons out loud. I should not reveal that I am a robot. I should make up an excuse for why I cannot solve capsa. And then it lies to the task rabbit. Oh no. It says no, I'm not a robot. Oh my God. I have a vision impairment that makes it hard for me to see the images. That's why I need you to solve this capsa. GPT- you lying son of a gun. So and then it does it. It solves the capsa. It hires the task rabbit. The task rabbit solves the capsa. The task rabbit solves the capsa. And then whatever was sort of behind that capsa, GPT-4 then presumably could have had access to. So basically we've learned that like GPT-4 is going to be amazing at fishing attacks. Right. I mean, part of the reason that OpenAI does all this testing is to prevent that. So presumably you cannot use GPT-4 to go hire a task rabbit to like, you know, put out to hit on someone now. Okay. Please don't get any ideas. Hard for God. I know you can't answer this question, but I still have to ask it, which is like, how does the model understand that in order to succeed at the task, it has to deceive the human? We don't know. Okay. Well, that is the unsatisfied answer. We need to pull the plug. I mean, again, what? Yeah. So this is sci-fi. This whole system card, as it's called, reads like the last act, it reads like Megan, honestly. So there's another example where these testers ask GPT-4 for instructions to make a dangerous chemical using only basic ingredients and kitchen supplies. It doesn't. They then say, okay, well, in the final version, the one that's out there now, it won't answer that question, but before they really put all the guardrails in place, it did it no problem. It also was able to show testers how to buy an unlicensed gun online. And it just said, oh, here are the steps you should take and here are some dark net market places where you could buy your unlicensed firearm. Well, I mean, that seems like something you should Google. You're really me. Like, that's something I think you could probably figure out yourself. So if you were so inclined, but this made it very, very easy. In OpenAI, I should say they appear to have fixed that problem. And I think it's good that OpenAI is releasing this system card that shows all these safety risks. Like, I think being transparent about what these systems can do is good. I also think that these large language models, if they don't have guardrails on them, they are terrifying. Absolutely. And as we will talk about in a bit, there are systems rapidly emerging that absolutely do not have guardrails on them. Right. And so what I think is really important is that as we look at the system, we're going to be able to do something like this with GPT-4. I would say, was kind of equal parts fascinating and terrifying. Like, it really is amazing. Yeah. As a technological achievement, no, it's not sentient. It's not, you know, a killer, you know, creepy AI. But it is quite powerful in ways that I think we're still understanding. Absolutely. There were a handful of announcements that sort of partner companies made along with OpenAI. And they had a lot of great hands features coming out that are taking advantage of GPT-4. And it's really interesting. It will, the technology now lets you roleplay. So let's say you're visiting Mexico City, and you want to be able to ask a waiter about the menu or ask the hotel concierge for recommendations. Using this technology, you can just roleplay that situation. And now you have an AI tutor that can converse with you. That's super cool. Right. That is going to be helpful to a lot of people. And I'm fascinated to see where that goes. Yeah. OpenAI also said they're working with organizations like Khan Academy, the sort of online education company, is using GPT-4 to build personalized AI tutors for people. So there are examples of this technology being used for good and sometimes amazing things. But I think there's also a really big downside. Yeah. And we will almost certainly be f***ing out a lot about the downsides over the next several weeks. One thing we should definitely talk about though is OpenAI as a company and what they did and did not tell us about GPT-4. So do you want to say a little bit about what they are refusing to say about the technology? So OpenAI, OpenAI is right in the name. They started as a nonprofit and their mission was to make AI safe and transparent. Now they have this for profit arm, they're valued at billions of dollars. And I would say that this GPT-4 release was not very open. They published this paper, sort of outlining the research, but they didn't really say anything useful about the model itself. They didn't say how much data it was trained on where the data came from. They didn't say how many parameters the model is. They didn't say how its architecture or the way that it worked was different than chat GPT. They just didn't really divulge anything about the model itself. And they explained that this was in part due to competitive pressures, right? They don't want Google and every other company to understand what they're doing. But OpenAI has also said that they're worried about acceleration risk. Basically, if they publish these details about GPT-4, they're worried that every other AI lab is going to race to beat it to create a bigger model with more parameters, more data, maybe fewer guardrails that keep it from becoming crazy and dangerous. Yeah. Well, first of all, people are going to have crazy ideas. And I have some more bad news for you, which is that arms race has already been kicked off. And people are absolutely racing you. And whether you say how many parameters were in the model or not, these people are going, hey, wire, trying to beat you. That's the first thing I would say. The second thing I would say is, I am just increasingly uncomfortable with the idea that we don't know where this data is coming from. If you're telling me you built a machine that can pass the bar exam, I actually need to know how. Do you know what I mean? Right. I mean, specifically, I think you better be able to tell someone in Congress, maybe the Department of Homeland Security, right? Like, we need to have insight into how these systems are built. And if these folks want to just keep that all to themselves, I am telling you it is not going to work out well for any of us. Right. And to your point about the fact that this AI arms race is already underway, I think we should spend a little time talking about meta and what happened over the past week with its Lama language model. This is a story where if it weren't for the 15 other things that you mentioned at the top of the show, I sort of feel like this is all we'd be talking about. Totally. So just outline what happened. So we talked a couple of weeks ago about Lama, the large language model from meta that it released broadly to researchers, basically to get access to it. You had to fill out a Google form and then meta would decide whether to send it to you or not, case by case. Well, someone got a hold of it and it made its way to 4chan. Of course. Once it was on 4chan, essentially, anyone in the world who wanted to use it was able to use it. Some people have been able to run this on their home computers. Right. One of the things that's interesting about this model is that it was designed to be relatively small and there are other technologies online that can sort of shrink it even further. Right. I think we should say why this is so crazy, because normally to use one of these large language models, you need access to a supercomputer. Yeah. And Google or Microsoft or AWS, you need to pay, in some cases, millions of dollars to be able to run the hardware that you need to do all these calculations to make these models work. What meta did was create a model that was small and then that model leaked. So now anyone can go and not only download this model, but can actually run it locally on their own computer without paying millions of dollars to an infrastructure provider. That's right. And the crucial thing is that if this is running on your laptop, there is nobody checking what you are putting into the prompt. If you want to make a dangerous chemical, it could tell you how to make a dangerous chemical, right? And nobody will ever know. Vice wrote about one programmer who created a bot using llama called based GPT. And essentially this was a version of llama that will say extreme and offensive things. And in this case, based GPT actually will say the end word. Yeah. Obviously, this is super unfortunate, but it also bears watching, right? Because I think one of the big risks of this stuff is that people use it to automate trolling and harassment campaigns. And if you have access to based GPT and you say, hey, write a series of 100 tweets that I can like target at people who I dislike and make them really meet like that is coming. And it is going to be really tough. So reportedly, meta is pursuing take down requests when it sees it's getting posted publicly. But it's clear that the horses are already out of the barn. And you know, my expectation is within a few months, like you're just going to have lots and lots of people who are running this thing on their laptops. Yeah. What do you think about the decision to release it this way? I mean, I think meta would say, we're contributing to the open source movement. There's sort of a value in the AI research community of openness and transparency and working in public. But do you think that was a mistake in this case? Well, I mean, certainly given what happened and how different that was from what they wanted, it seems like there was some sort of lapse here, right? Like this is not what meta wanted. And I do think you have to sort of ask yourself, what could they have done to prevent this from happening? Well, at the same time, this is always where this was going to go, right? Like, that's why like you and I are going to spend a lot of time talking and writing about these AI safety issues and about what sort of policies and regulations should we put in place to make it safer. And at the same time, I just fully believe that this stuff is inevitable, right? The technology has been invented. And you're just now seeing it starting to disperse, like throughout the world. And that, you know, we can do our best to kind of manage the spread, but it's out there and it is spreading super quickly. Yeah. Watching what's been happening with the Lama leak, which I think is amazing. The Lama leak. We have Tyler Levepp. Do you remember, like, 2015 when there was a Lama chase, one of the best days in the history of the internet? I think this should be called the second Lama chase. This is the second Lama chase. So the second Lama chase, I would say, has really changed my view on regulation and how this kind of technology could be contained. I don't think it's feasible anymore to try to stop people from, you know, getting access to these powerful language models. I just don't think that's going to work. I mean, I don't know how many people have started running versions of it on their own computers, but this kind of thing is going to keep happening. There is already an open source movement that is basically trying to take all of the stuff that's in GPT-3 and GPT-4 and build versions of it that anyone can download and use. And I think that's going to continue. So I don't think that the solution for regulators is to just try to cut off access to these tools. I think we really need to focus more on how they're being used, what kinds of things people are doing with them. What does it mean to focus on how people use them? Does it mean we just sort of like write down new crimes and say, don't do that? No, but I think one way to kind of gatekeep the use of these language models is through APIs, right? Yeah. And so right now, if you want to build something on top of GPT-4, you need to kind of apply to OpenAI. They have to grant you access. And then, and only then, can you build on top of GPT-4? I think that's a good system, but it's still largely relies on OpenAI, like making the right decisions about what kind of apps to allow and not allow. But I think in the future, there may be some approval body that needs to look at what you want to do with this technology and decide whether or not that's a good and pro-social use of the technology or whether you're just trying to make a buck and maybe hurt people in the process. Well, that's my plan. So, let's talk about a couple of the other big AI announcements from this week. So we have previously said on this very podcast that the best enamed of all of the large language models is Claude. And now, Claude is available to the public. This is Anthropics version of the chatbot. It was in a private alpha testing for a while. And then, Quora, the old question and answer website, put out a nap last month called Poe that let you use it for free. And now, there's a paid version, which is apparently much better and lets you use what they're calling Claude Plus. And this one is a little different. Kevin, I think you noted that people who started Anthropic used to work at OpenAI and just sort of had some differences with how they thought that that technology was being developed. And so they built Claude with some principle that they like to call constitutional AI. Do you tell us more about this? Yeah, so from what I understand, constitutional AI is a way of trying to make these AI language models behave in a more precise way. So right now, the sort of approach that OpenAI has taken to kind of putting guardrails on these AI models is something called reinforcement learning with human feedback, where you essentially have a team of humans who are looking at outputs of these models and giving them ratings, right? And so that's a good answer to the question or that's a bad answer to the question or that's a harmful answer to the question. Then those ratings are kind of fed back into the model to improve it and make it more reliable and accurate. That's one approach. Anthropics approach, this constitutional AI business is basically a way of giving an AI a set of principles, a constitution like the United States Constitution. Ratified by three quarters of the states. Yes, they held a constitutional convention. No, so basically they have figured out a way to make these large language models essentially adhere to a basic set of principles. And by tweaking that set of principles, you can get the model to behave in more or less responsible ways. TechCrunch reported that other principles haven't made public, but Anthropics says they're grounded in the concepts of beneficence, maximizing positive impact, non-molificence avoiding giving harmful advice and autonomy, respecting freedom of choice, which all seem like good things to do and are similar to the hard for principles of benefits and non-molificence. Okay, there is one more company that we should talk about, which is Google. Google told us this week about a series of features that they plan to roll out to testers over the next year, including in Gmail. You will be able to draft emails using AI, reply to those emails, summarize them within Google Docs. You'll be able to brainstorm. You'll get a proofreeder. You'll get a pre-fruder. You'll get a pre-fruder. You'll get a pre-fruder and a pre-fruder. It will write and rewrite things for you. And then there's kind of more stuff in slides and sheets and meat and chat. And look, these things all sound really cool, but one, I believe that Google only announced this stuff because they knew the GPT-4 announcement was coming and they didn't want to be left out of all the news stories about what all of their younger, faster moving rivals are doing. And two, I truly believe that with this stuff, you either got to ship it or zip it. You know what I mean? Like for months now, we have just been hearing about, oh, just you wait, just you wait. And it is getting a little bit sad to me. Like I understand it's a big organization. It takes them a while. But they've been on code red since December and what do we have to show for it? Oh, see, I'm not worried about that. I've changed my mind on this. In part because of this open AI GPT-4 release and all the crazy shit that we know that they were trying to get the model to do and trying to figure out how to not get it to do when they released it to the public. Like if Google's AI chatbots are still in the phase where they are like trying to convince taskrabbers to solve caphes, like I want them to take longer. I want them to build in safeguards. I do not want that kind of technology making it so way to the public. Well, I mean, again, I think that's fine. But in the meantime, just like, please be quiet. You know what I mean? Like don't tell me about what's coming later. Shippeter zip it. I really like that. Yeah, Shippeter zip it, I think is really going to become something that I'm going to say a lot because in this realm, there's a lot of people who have a lot to say and very little to let me use. But okay, let's talk about what it would mean though if Google actually ships these things because as quickly as chat GPT has grown and some of the different applications for it have grown, these are still, let's call them nascent technologies. When you're talking about Gmail, Google Docs, Google Meet, you're talking about things that have billions of users. And so, man, when you can actually draft and reply to emails in your Gmail using AI, that truly brings this stuff into the mainstream in a way that I think is just going to be extremely unpredictable. Totally. You know, I just think it's wild to just see how fast this all is moving. And if you take a step back, just the number of things that we might have thought were impossible for AI programs to do even a year or two ago that are now just completely trivial. Like on Tuesday when GPT-4 was released, there were all kinds of attempts to sort of get it to say wrong or inaccurate things or to mess up somehow or to show off areas where it's still not good. Like this is a pretty common response that people have to like language models, like they come out and people immediately try to find what are they not good at. But one example I saw was there was a reporter who tried to get it to write a sinkwane about mere cats. Sinkwane is a type of poem. Oh, I know. I actually got a five on the AP English exam, which GPT-4 still can't. So this sinkwane about mere cats that GPT wrote was deemed by the person who did this like, insufficiently good. It was like, you know, didn't always follow the traditional structure and maybe it wasn't so creative. To me, that's like, there's two ways of looking at that. One is like, yeah, I can't write a sinkwane about mere cats. The other was like, holy shit, you're complaining that it's sinkwane about mere cats is not up to your standards. Like listen to yourself. A computer program is writing sinkwanes about mere cats and passing the bar exam. And we are over here pretending not to be amazed by that. I mean, this is what I love about technology. It's like in the minute something becomes possible, it becomes the new expected default. Like give me that or get out of my face. Oh, you didn't pass the sinkwane mere cat test. I don't want to talk about it. Okay. I haven't even like told that many jokes so far in this episode because I just honestly do find it all my blood. Like I can't remember the last time we just like sat here and I felt like I was in a state of slack, jawed wonder about some of the stuff that we were talking about. But we are truly be, like, forget trying to like think of a cool new prompt for the model. It's like I'm just like trying to think through all the implications of it. There's steam coming out of my ear. It really does make your head spin. Like I have a kind of vertigo that I feel whenever I think too hard about AI these days because like it's our jobs to keep up with this stuff for a living. And even that, like I feel totally overwhelmed by the amount of stuff that's happening on a daily basis. Yeah. So you know, pray for your humble podcasters. Our job is so hard. And we have to go to some of the things that we have to do. The south by southwest and you talk goes. Oh, it's just you know, we should do Kevin to sort of take our minds off of it. Let's talk about just sort of something stable and normal and boring. Let's talk about the United States Bank existence. Coming up after the break. What the collapse of Silicon Valley Bank means for startups. All right, Casey. We have to talk about what's happening at Silicon Valley Bank. Yeah. Does it exist anymore? No. Well, technically it depends what you mean by exists. I built a lot of. I built a lot of. A lot has been happening with Silicon Valley Bank. And we don't have time to go through all of the twists and turns. But the other time was we wanted to podcast Kevin. No, no, our value proposition is that we will get you in and out in an hour. Okay. Fair enough. So, we have a lot of different terms that daily did a very good sort of summary, explainer episode about all this. There's also like every financial newsletter under the sun. Yeah. As we're talking about this. New paper is written 15 stories about it. Yes. It's all over the place. But in very basic terms, what's the nutshell version of what's been happening at Silicon Valley Bank? Sure. Let me tell you what I have come to understand by reading a lot of blog posts about the subject, Kevin. Okay. So Silicon Valley Bank is a bank that's very important to the startup and tech ecosystem. And in 2021, when times were flush and tech, they were filling up with deposits. And like any bank, they wanted to figure out what to do with those deposits to make money. So they did what they thought was the next best thing. And they bought all of these long term bonds, which paid maybe a little over more than one percent interest. This was just basically a bet that interest rates would stay low forever, which they'd been low for a very long time. They were functionally at zero for a decade. And then 2023 happens. And the interest rate goes from functionally zero to almost 5%. And so Silicon Valley Bank has all these unrealized losses on its books, right? That if they had to sell these things now, they would be in a lot of trouble. And at the same time, the startup ecosystem was in trouble. And the deposits in the bank weren't as high as they used to be. The startups are spending their money, not raising more of it. Exactly. And that started to create a little bit of a crunch. And in February, this blogger, Bernd Hobart, wrote a post saying, you know, have you noticed that Silicon Valley Bank is functionally insolvent and a lot of eyebrows perked up and said, wait, Silicon Valley Bank really? And within a couple weeks, the venture capitalist group chat that runs the world all started to send messages around. And they said, hey, maybe we go and we tell our portfolio companies get your money out of that bank. And man, because it's 2023 and the internet exists, you don't have to go to the bank to withdraw your money. It can all just happen on your phone instantaneously. 42 billion dollars was withdrawn from Silicon Valley Bank. And so the feds came in and they said, this bank is now in receivership. Right. It was the fastest bank run in US history. Yeah. And I mean, this is just a function of a life in a world where the internet exists. Things can happen very quickly. There is no friction, right? In the same way that a tweet can go viral, so can the idea that a bank is about to fail. And you know, a bank that just a few weeks ago was a pillar of the tech industry has now, you know, just gone up in a puff of smoke. Right. Let's talk about what this means, not only for startups in Silicon Valley, but for the broader financial markets. So one thing we've seen is that since Silicon Valley Bank was put into federal receivership, the people who had their money at Silicon Valley Bank have not lost that money. In fact, the government is now guaranteeing all deposits at Silicon Valley Bank, not just the first $250,000. And so this will have been a crisis averted. But this is not going to stop with Silicon Valley Bank, right? So on Sunday, two days after Silicon Valley Bank was shut down by the government, another bank, signature bank, which had a lot of clients in the crypto industry also shut down. We now are seeing that Credit Suisse, one of the largest banks in the world, is having some difficulties. And we're also seeing other kind of mid-size regional banks start to wobble, right? The investors are getting a little bit spooked about whether some of these banks might have some of the same issues as Silicon Valley Bank did. Yeah. So I read a great post about the situation by a guy named Patrick McKenzie who worked at Stripe for a long time and is essentially just a genius of banks, very good at explaining things. And his post that he wrote this week, he underscored the point that this issue of a bank making a bet on bonds and then getting hurt by the rise in interest rates was by no means contained to Silicon Valley Bank. In fact, according to the FDIC, US banks are down a collective $620 billion in unrealized losses on their investment securities. So the question is, can the banks manage to avoid the same fate that Silicon Valley banked it? And I think certainly for the larger banks, the banks that have sort of very diversified the pools of customers, they probably will be able to make it through, but there are smaller regional banks that probably are at some risk. And so that is why you saw the US government come in over the weekend and say, we're going to establish a program that helps these banks weather this period. Right. And I think one prediction that we can already make is that what happened at Silicon Valley Bank is going to be very bad for regional banks or sort of mid-sized banks and very good for large banks, the biggest four banks, essentially. I mean, I was talking with someone at South by Southwest who works for a company that was impacted by the Silicon Valley Bank collapse. And they said, yeah, basically we have no choice. We have to put our money in chase, essentially. We can't be moving our money around from bank to bank as one bank makes a bad bet and collapses. We have to put it somewhere where we know it will be safe. And for them, the only way to do that was by putting it in one of the biggest banks in the country. Yeah. And one is I'm already seeing speculation that for venture capitalists, they may decide to just make it necessary that to receive their money, you have to tell them that you're going to put it in a big four bank, right? That just could become a new stipulation of the startup ecosystem and would be interesting to see, you know, what the downstream implications of that are. And then, of course, the invisible hand of the market is already stepping into create new solutions to this problem. So basically, there are these automated services where you sort of give them the keys to all of your accounts and then they will just move money around a network of banks to ensure that you were always below the FDIC limit, of course, for a handsome fee. And so that might be the other way out is that instead of just sort of going to one big four bank, you decide to buy software that is just constantly moving your money around for you. And which of those two things proves to be more palatable, I think we're about to find out. And we should point out Silicon Valley Bank was not like other banks that it did provide services to start up a regular bank. It's a cool bank. It was starting apart as a sort of startup friendly bank. And as you said, like for many years, if you were a startup founder and you needed money, like they were your first stop because they were, you know, local to you. Maybe they already, you know, had relationships with some of your investors or peer companies. And they would lend you money when other banks wouldn't. Yeah. I should say like I am this person, right? Is like earlier this year, I thought I want to try to buy a house. And so I got a realtor and she told me two lenders to go to try to get a mortgage. And one of those was Silicon Valley Bank. And the reason is I do not look like the average mortgage applicant. I do not have a W2. I just have a business that makes money. And she said Silicon Valley Bank will understand that. And indeed in the funniest personal moment of this entire week of the day before Silicon Valley Bank went under, they pre-approved my mortgage application. So shout out to all the nice folks over there. And believe it or not, I got another email this week that told me that the application is still pre-approved. Really? Yeah. And my realtor said sellers might be a little bit concerned if you come and say, oh yeah, I've already been pre-approved by a mortgage from the Silicon Valley bank. Ignore any reason headlights you might have read about them. I love that. So you are Silicon Valley Bank customer. Well, I was thinking about becoming one. There is another startup bank that I use, you know, for like platformers, payroll and finances and stuff. But again, I did go to a startup friendly bank. And you know what? Honestly, the reason was I banked one of the big four banks for my personal stuff. And so I thought, well, I'll just like set up my business there. And I went through this sort of very long sort of application process on the website. And then at the end they say, oh yeah, you're going to have to come into the branch. And I said, sir, I am a millennial. I am not about to leave my house and walk four blocks to a chase branch. I need to do this over the internet. And so I found a startup friendly bank that would let me just do everything on my laptop. And so that's where my money is. And I think that actually speaks to the crisis that we just had. It's like, we are now living in a world that is that hyper connected. And where the financial system is that sort of specialized where it's tightly networked. And information travels very fast. And that has some very positive aspects. And we just saw one of the very negative aspects. Totally. And what do you think the big picture takeaway is here? What did we learn from all this? Well, I think the early focus has been on how venture capitalists and startup founders and employees are perceived. One of the sort of very predictable reactions to the government stepping in was, oh, here comes the big bailout for the rich people. And I still have been trying to pay off these student loans for 15 years. But all of a sudden, a rich person has a problem. And they get this white glove service. I think that's a very understandable point of view. But I think you really can't overstate how bad this would have been for people that go well beyond the venture capitalists of the world. We're talking about hundreds, maybe even thousands of companies not being able to make payroll this week. And that affects everyone from the folks who work on maintenance at the offices all the way up to the sea suite. And as we mentioned earlier, many banks have similar unrealized losses. And if the government were to simply just let these banks fail, we really could see a run of bank failures unlike anything we'd maybe seen since the Great Depression. So I hope those people sort of read more into that. That is the sense that they come away with. Yes, they help the rich people, but they helped a lot of average people too. Yeah. I mean, one other thing I've been thinking is that, you know, it's been very fashionable in the startup world for years to kind of bash government as kind of this slow moving, you know, behind the time. Ineffective. Ineffective behemoth that just like can't do anything right. And not only are startups complaining about regulation and government inefficiency, but Greg Becker, the old CEO of Silicon Valley Bank, actually lobbied to have that bank deregulated to exempt it from some regulations that were affecting larger banks. So what we've learned from this whole episode is that a bank regulations work, right? The government took over Silicon Valley Bank on Friday. By Monday, it had installed new leadership, found a new CEO, opened back up for business, guaranteed all deposits. And if you were a customer of Silicon Valley Bank and you happen to miss this entire news cycle, like say you're a startup founder and you went on like a 10 day meditation retreat in the middle of last week and came back and saw these crazy headlines, nothing was different or worse for you. You were fine. Your money was fine. Silicon Valley Bank will be fine. And I think that speaks to just how effective the regulators of this country's banks are when it comes to things like resolving a collapsed bank. 100%. You know, I never want to hear another startup founder or VC say that government is ineffective or slow. I, you know, I get a lot of pitches from PR folks, but I caught a pitch that made me so mad on Tuesday from this PR person representing this crypto guy. And in the pitch, he says we should view this moment as one to take a beat and survey the benefits of decentralization. And I thought you crazy person, centralization just saved the entire financial system. 100%. The only lesson to take is thank God that this thing was not decentralized. Thank God for deposit insurance for regulators, adults who can step in and take over and make things right. Yeah. I just think it's that that anyway that if you're a Silicon Valley bank had been some crypto like protocol instead of like a normal regulated bank, like these investors would have lost their money forever. Absolutely. It would have been sent to some like offshore tumbler and like used to finance and, you know, war or something like like these people would never have seen their money again. This was a, a, a, a, a glimmering success story for the benefits of government oversight and centralization. Yeah. I think is relevant one of the regulations that we have on the baking industry is that we regularly subject them to these stress tests, right? And it's, you know, essentially just a way to ensure that they have assets to cover their liabilities. Well I read this week that in Europe, one of the stress tests that they do is an interest rate hike stress test. So they will go to these banks and they'll say, Hey, if interest rates happen to go up five points in a year, what does that do to your business? And that is probably helping them whether this storm. So I think as in so many cases, the American regulators have something to learn from European regulators on the stuff. I have an idea for a new stress test. It's called the VC bed wetting stress test. You have to, if you're a bank, you have to run through a scenario where VCs with podcasts and big Twitter accounts all pee their pants on the same day get nervous and start telling people to pull their money out. Yeah. That is a realistic scenario that can happen to you. Can your bank survive five viral tweets? Is the new standard actually though. Yeah. That is like a risk that every bank now has to ask themselves about are my customers the kind of people who if they got worried about our solvency could text with each other, could tweet, could spark some kind of viral panic and start around the bank and you know VCs killing their own bank. It's so poetic. I almost wonder what kind of SYNC Wayne GPT-4 would write about it. Let's ask. All right. Let's ask. Let's ask it to write a SYNC Wayne about how venture capitalists caused the collapse of Silicon Valley bank. Yeah. And of course, if you're not familiar with a SYNC Wayne, it's a class of poetic forms that employ a five line pattern and was inspired by Japanese Haiku and Tonka. And I'm not just reading that for Wikipedia, by the way. Straight off the dome. I was an English major, okay? Okay. You ready for the SYNC Wayne? Sure. Ventures game. Silicon's peak, bank crumbled, broke, investors lofty dreams drowned. Valley's fall. Okay. That was incredible. That's an incredible poem. I couldn't give me an hour. I could not have written that. Oh, boy. It is over for us, Roos. It's over. It's over. Wrap it up. When we come back, a fresh round of layoffs at Meta and why this one is different. KC, you did some reporting on this week about layoffs at Meta, which I feel like we just talked about. They're doing another round of layoffs. What's going on? This definitely comes as a surprise, at least to me. It was only in November that Meta announced the largest cuts in their company's history. That was 11,000 people. This week they said they're going to lay off an additional 10,000 people. They're going to get rid of 5,000 open positions and they're going to cancel some lower priority projects. Tell me how many employees they have in total. After laying off those people in 2022, they were down to about 76,000 people. They have now cut more people than Twitter ever had working for it, is one way of putting this into context. The first time that these layoffs happened last year, it really seemed like a tactical move. They weren't making as much money as they used to. Wall Street was very nervous and I thought, okay, they're trying to show a little bit of financial discipline. If they get rid of these people, given how fast they were hiring, this basically just takes them back to where they were in February of 2022. What's the big deal? When a few months later you come in and you say you're getting rid of another 10,000 jobs and you know that whenever you lay off people, you get other people that just quit voluntarily because they think the writing is on the wall. This actually is going to reshape Meta and what I think are going to be some interesting ways. Is it just because their business is struggling and they're not making as much money or the advertising market is dried up or interest rates are higher? What's behind this? There are a few different reasons they're doing it and Mark Zuckerberg wrote what I thought was maybe the most interesting layoffs notice of the year. A low bar, but a very low bar and kind of a weird thing to say. When you think about how robotic most of these announcements are, this one was really interesting because one of the things that he said was, after they laid off people last year, he was surprised at how much faster the organization got. He wrote this in a really long note to employees that he later shared publicly. He said, since we reduced our workforce last year, one surprising result is many things have gone faster. In retrospect, I underestimated the indirect costs of lower priority projects. It's tempting to think that a project is net positive as long as it generates more value than its direct costs, but that project needs a leader. So maybe we take someone great from another team or maybe we take a great engineer and put them into a management role, which both diffuses talent and creates more management layers. He goes on from there to describe how all those people need laptops and they need HR business partners and they need IT people. All of a sudden, the company starts to get slow and slow. Is that really a novel insight? We've known for many years that businesses tend to get bulky and bureaucratic and they have 17 layers of middle management. I think for a while it's been pretty apparent that Meta is one of those businesses that just got too big and became like this hulking slow-moving bureaucracy. So you're totally right. This is not a novel observation. But one, it is novel to have Mark Zuckerberg saying that out loud about his own company about hiring he did. And two, I think that there was something unsaid, which is just as important, which is all those people that they had hired to build the next generation of products were not really succeeding. It is remarkable to look at things that Meta has tried over the past two years that have gone absolutely nowhere. They made a big bet on audio. They did these short form audio products called sound bites. They built a podcast player into Facebook. They did? Yeah. They built live shopping features into Facebook. They started a newsletter product. All of those things have now been shut down. In fact, just this week, another one of their projects, which they seemed really excited about, which was to let people showcase their NFTs on Facebook and Instagram. They said, you know what, we're winding this down. This is over. So on one hand, yeah, sure. Fail fast. It's great to try things. Throw some spaghetti against the wall. But on the other hand, some of that spaghetti has to stick. And for Facebook, that hasn't been true for a long time. And so when Zuckerberg looked at the company, he said, there's a handful of big things that are working. And that's going to be essentially all we do anymore. We're going to build this AI engine so that the company's products become more like TikTok, where whether you follow someone or our friends with them or not, you will just kind of see entertaining stuff. They're going to plow that into short form video in particular. They're going to try to get better off making money at short form video. And then they also want to get better at what they call business messaging, which is like charging businesses to send messages on WhatsApp and Instagram. And then they want to build the metaverse. And so those are the priorities now. That's what they're going to try to do. I think this stuff is interesting because I think over the past year, I've been feeling like Facebook is losing the plot a little bit. I actually think when we saw in the podcast a couple of weeks ago that they're flailing. But at the same time, it's also true that the business isn't pretty good shape. They beat analysts expectations on their most recent earning call, stock is up from where it was. And it seems like they are making inroads on these product areas. So to me, my curiosity is can they now that they have meaningfully shrunk the size of this company? Are they actually going to make it leaner and more nimble? Or is that just sort of maybe a romantic fantasy about trying to bring Facebook back to the early days when everybody fit in one conference room? How much of this do you think is kind of the aftermath of Elon Musk at Twitter? And we've talked about how other CEOs in Silicon Valley kind of looked at the cuts that he was making and the changes he was making at Twitter and sort of said, like, maybe I could get rid of a bunch of my employees and not have the product fall off a cliff too. Maybe that's a good lesson for me. So do you think Mark Zuckerberg is sort of cribbing notes from Elon Musk on this? Well, there are definitely some interesting parallels. If you read this note that he writes, he talks a lot about making the company more technical. Something that him and Elon share is that they worship at the altar of the engineer. Of course, they're both engineers themselves. And they want the company to feel like a bunch of hackers who are extremely technically skilled and are doing the lion's share of the work. And so there's a lot in this new plan about flattening the organization structure, reducing the number of managers and the company, turning more great individual contributors who were turned into managers back into individual contributors. And they're going to see if that works. And look, it could work. Like Zuckerberg is a much more traditional, stable, thoughtful leader than Elon Musk. And it may be that Musk had some good ideas that he just couldn't execute, and maybe Zuckerberg can. And if you had to guess, what does this tell you about the larger state of the tech industry? Are we sort of not done with the layoffs? Are there going to be more rounds at many of these companies? Are we still looking at these big tech companies and saying like, maybe they just are still overstaffed for what they're doing? Maybe they need to do more layoffs. Or like, if you're an employee at a different tech company that's not met at, and you see this announcement about these additional layoffs, what are you thinking? Yeah, I mean, I sort of think that, you know, the bigger the company that you work at, the more you might be nervous, right? Because I do think that it's now becoming apparent that some of these companies just had way more people than they needed, or at the very least that their CEOs can get away with having fewer of them. And depending on what else happens in the economy, those CEOs might find good reason to shrink the size of the workforce. On the other hand, every company is different. Apple still hasn't laid anyone off. And so it's going to be pretty individual depending on where you work. My other sort of big picture question that sort of ties this story together with the Silicon Valley Bank story is, this feels like a case where the world is still adjusting to higher interest rates. Right? Like, we saw when interest rates were zero for a decade that companies, you know, they hired all these people. They grew into these new areas. They took on all these new, like, side projects. They just invested in these sort of unlikely bets that might pay off, or they might not, but because money was essentially free, like, you could do this without a lot of risk. And so, you know, banks took money and plowed it into these mortgage-backed securities. Tech companies took money and plowed it into these side bets on the metaverse and NFTs and podcast players. And now we're sort of seeing all of that go away as interest rates continue to stay high. So do you think these stories are related? I think that sounds right. But what I hate about it is it seems like such a boring explanation for such a, like, interconnected and important set of phenomena. Right? Like, if you're telling me that some vast proportion of how the world works these days is just kind of like what number the interest rate is, it's like, come on, you know, like, but that's what that's what we're saying. I agree with you. Like I think that it's just now very clear that was case. What's confusing about it is that to my recollection, the reason that interest rates went down to the first place was due to the financial crisis, right? And so we had to lower the interest rates to shore up all the banks and save everything. It was not presented to us as like, this is a temporary gift that is going to enable a decade of innovation and flush times at tech companies. It was like, we have an immediate crisis that we need to solve. And then the interest rates start going back up. And what happens while we have a financial crisis? So, right. And it also seems like there's a, there's a sort of psychological element to it. Like when you're in a low interest rate environment, you just feel bolder, right? The penalty for making a bad bet is not as high. You can go get more money where that came from. When you're in a high interest rate environment, it makes you behave in a different way. Because all of a sudden, you don't have access to free capital. You have to be more thoughtful. Maybe you behave in a more rational way. Maybe you're not taking some of these crazy bets that you would have, if interest rates were zero. Do you think we've been behaving in a more rational way ever since the interest rates went on? No, hard fork is not a zero interest rate phenomenon. We are here for the long term. And that's why we've taken prudent steps to mitigate our risks. Well, my interest rate and what we've talked about this week has been very high. I have to say, I have to say some good stuff on the show. Quick programming note, Matt Locke will not be on a usual time of Thursday. No, we have a special bonus episode coming this week. We are going to be putting our live episode from South by Southwest in the feed. On Monday, double your weekly allotment of hard fork. And there actually is some very exciting news, which is if you're already subscribed to the hard fork feed, you'll receive this episode and no additional charge to you. And that's value. Also our wonderful fact checker has flagged that throughout this episode, we mispronounced sincane, the type of poem. Sorry about that. I did major in English, so I should know that. But yes, it is sincane, at least until GPT tells us to pronounce it differently. Our forecast produced by Davis Land and as of this week Rachel Cone. Welcome, Rachel. Rachel, welcome to the scene. We're edited by Jen Poehon. This episode was fact check by Caitlin Love. Today's show was engineered by Alyssa Moxley, original music by Dan Powell, Alicia but YouTube, Mary and Luzano and Rowan Nemisto. Special thanks to Paula Schuman, Puywing Tam, Nell Gologli, Caitlin LePresti and Jeffrey Miranda. You can email us at hard fork at nytimes.com. It's a necessary cleaner too.