Speaker 1: I want to start by talking about a dream, the
Speaker 1: dream of building an artificial mind. It's something people have
Speaker 1: imagined and written about for decades, humans working together to
Speaker 1: construct a new entity more powerful than ourselves, and some
Speaker 1: researchers think this dream may be within reach.
Speaker 2: The day will come when the digital brains that live
Speaker 2: inside our computers will become as good and even better
Speaker 2: than our own biological brains. Computers will become smarter than us.
Speaker 2: Vicoll such ANAI and AGI artificial general intelligence.
Speaker 1: That's Ilia set Skiver, one of the co founders of
Speaker 1: open ai and at ted talk he gave last year,
Speaker 1: and he often sounds like a religious mystic when he
Speaker 1: talks about the future of artificial intelligence. But right now
Speaker 1: he's just talking about this quest to build artificial general intelligence,
Speaker 1: an AI that can think and solve a variety of problems.
Speaker 1: Like a person. It could switch between playing games, solving
Speaker 1: science problems, creating beautiful art, and driving a car. Open
Speaker 1: AI's goal is to build AGI. It's a pretty out
Speaker 1: there idea in the AI world, or at least it
Speaker 1: used to be. Ilia frames AGI as this almost mystical,
Speaker 1: momentous leap forward, like Prometheus channeling fire and the consequences
Speaker 1: will be huge. It will usher us into technological glory
Speaker 1: and at the same time into chaos. In this tape
Speaker 1: from the documentary film I Human, he sounds certain of
Speaker 1: the tidal waves that will come now.
Speaker 3: AI is a great thing because AI will solve all
Speaker 3: the problems that you have today. If you solve employment,
Speaker 3: if you solve disease, it will solve poverty. But it
Speaker 3: will also create new problems. The problem fake news is
Speaker 3: going to be a million times worse. Cyber attacts will
Speaker 3: become much more extreme if people have totally automated AI weapons.
Speaker 1: Ilia is an incredibly accomplished AI researcher. Before open Ai,
Speaker 1: he worked at Google, and he has several passions that
Speaker 1: I see as a celebration of being human. He plays
Speaker 1: the piano, he draws and paints. One of his paintings
Speaker 1: hangs in the Open Ai office. It's a flower in
Speaker 1: the shape of the company's logo. At the same time,
Speaker 1: he's also hyper focused on his AI research. He told
Speaker 1: a reporter once, I lead a very simple life. I
Speaker 1: go to work, then I go home. I don't do
Speaker 1: much else. There are a lot of social activities one
Speaker 1: can engage in lots of events one could go to,
Speaker 1: which I don't. He spends a lot of time looking
Speaker 1: at the current trajectory of AI and extrapolating to try
Speaker 1: to predict the future. In particular, Ilia is worried about
Speaker 1: what happens if AGI gets its own desires and its
Speaker 1: own goals. You can hear this dreamy equality in his voice.
Speaker 3: It's not said it's going to actively hate humans and
Speaker 3: want to harm them, but it is going to be
Speaker 3: too powerful. And I think a good analogy would be
Speaker 3: the way humans treat animals. It loves to be hate animals,
Speaker 3: but when the time comes to build a highway between
Speaker 3: two cities, we are not asking the animals for permission.
Speaker 4: Imagining it this huge.
Speaker 3: Unstoppable force, and I think it's pretty likely the entire
Speaker 3: surface of the areas will be covered with solid panels
Speaker 3: and data centers.
Speaker 1: I want to pause here for a minute. This is
Speaker 1: a really intense, powerful image that we are creating some
Speaker 1: new kind of being that would view us with interest,
Speaker 1: but ultimately with indiffer friends, like the way we look
Speaker 1: at deer. What strikes me most in this audio is
Speaker 1: Ilia's tone of voice isn't one of fear. It sounds
Speaker 1: more like awe Ilia imagines an AGI that we create
Speaker 1: that would be likely to bulldoze over us in order
Speaker 1: to reach its own desires. It's a dramatic vision, hard
Speaker 1: to really grasp, and it has a religious quality in
Speaker 1: its conception of a supernatural, all powerful entity. I should
Speaker 1: mention this is all totally theoretical. We are still nowhere
Speaker 1: close to AGI. Open AI's best efforts are statistical models
Speaker 1: that convincingly mimic humans, and mimicry is a far cry
Speaker 1: from AI that can think for itself. Still, open Ai
Speaker 1: wants to do this, and do it right, and do
Speaker 1: it first. Here's Sam Altman testifying in front of Congress
Speaker 1: in twenty twenty three.
Speaker 4: My worst fears are that we cause significant We feel
Speaker 4: the technology the industry caused significant harm to the world.
Speaker 4: It's why we started the company. I think if this
Speaker 4: technology goes wrong, it can go quite wrong.
Speaker 1: You're listening to Foundering. I'm your host, Ellen Hewitt, and
Speaker 1: in this episode will take you inside the messy and
Speaker 1: idealistic early years of open Ai. We'll discuss this dream
Speaker 1: of building all powerful agi. It's important because this is
Speaker 1: the destination that open ai is speeding toward. It's this
Speaker 1: generation's race to the moon. We'll discuss how ai technology
Speaker 1: changed dramatically and quickly, and how that change made this
Speaker 1: dream of AGI feel closer than ever before. In just
Speaker 1: a few years, it went from an eccentric idea that
Speaker 1: people were scoffing at to a milestone some experts think
Speaker 1: could happen within a few years. Sam Altman has even
Speaker 1: suggested twenty twenty eight. And we'll examine the compromises open
Speaker 1: Ai made in its pursuit of this dream. At first,
Speaker 1: the company made promises to share its research widely and
Speaker 1: to not be corrupted by for profit incentives. But once
Speaker 1: their technology began to advance and it looked like there
Speaker 1: was serious power to be had, they.
Speaker 3: Made a u turn.
Speaker 1: Then this pivotal moment, kareemed into a power struggle at
Speaker 1: open Ai, and Sam Altman took charge.
Speaker 4: We'll be right back.
Speaker 1: We'll start In twenty fifteen, open Ai had just been founded.
Speaker 1: It had a commitment from Elon Musk for a billion
Speaker 1: dollars in funding, plus some money from other donors as well.
Speaker 1: It was this small, scrappy research lab. Sam and Elon
Speaker 1: weren't around much in the early days. At the time,
Speaker 1: Sam was actually still running y Combinator, the startup accelerator,
Speaker 1: but he was beginning to position himself as a thought
Speaker 1: leader in the AI space. In particular, he was talking
Speaker 1: about AI doomsday scenarios. In twenty fifteen, he declared on
Speaker 1: his blog development of superhuman machine intelligence is probably the
Speaker 1: greatest threat to the continued existence of humanity. He also
Speaker 1: wrote that AI could destroy every human in the universe.
Speaker 1: Here he is at a tech event the same year,
Speaker 1: referencing the founding of open AI.
Speaker 4: I actually just agreed to fund a company doing AI
Speaker 4: safety research. You know, I think AI will probably, like
Speaker 4: most likely sort of lead to the end of the world.
Speaker 4: But in the meantime there will be great companies created
Speaker 4: with serious machine learning.
Speaker 1: I want to talk about that comment for a second.
Speaker 1: He's saying AI might kill us all, and he's asking
Speaker 1: us to trust his conclusions as an expert, but he's
Speaker 1: also being glib about making money along the way. In
Speaker 1: the beginning of open AA, Sam and Elon weren't around
Speaker 1: for the day to day. They were out glad handing, recruiting,
Speaker 1: and talking to journalists. They would pop in once a
Speaker 1: week or so to get progress updates. In those days,
Speaker 1: Sam would swoop into a conversation then leave.
Speaker 5: So he struck me as very very sharp, incisive, and
Speaker 5: also superficient with this time. When the conversation is done,
Speaker 5: it's done.
Speaker 1: That's Peter Abiel, a researcher who worked at open ai
Speaker 1: in its first two years. He says, in the early
Speaker 1: days open ai looked like a typical startup. They didn't
Speaker 1: even have an office for a while. They met in
Speaker 1: the home of one of the co founders.
Speaker 5: When we started out late twenty fifteen early twenty sixteen,
Speaker 5: it was in Greg Brockman's apartment in San Francisco Mission District.
Speaker 5: It was you know, we're sitting essentially on a couch,
Speaker 5: at a kitchen counter and on a bed, and that's
Speaker 5: pretty much that's where the work is getting done. It's
Speaker 5: it's kind of crazy to think that, you know, that's
Speaker 5: where something big got startup. We just had twenty of
Speaker 5: the world's best AI researchers together, really focused on trying
Speaker 5: to get some things done and I've never been done before.
Speaker 1: In the absence of Sam and Elon, the main leaders
Speaker 1: were two people who aren't famous but who will play
Speaker 1: a mammoth role later on. There's Greg whose apartment served
Speaker 1: as their office, and Ilia, the research scientist we heard
Speaker 1: from at the beginning of the episode. You can think
Speaker 1: of Greg as the workhorse in charge of business operations,
Speaker 1: and Ilia as the AI genius, and together they ran
Speaker 1: open AI. Peter remembers going on weekly walks with Ilia
Speaker 1: around the neighborhood in San Francisco, talking about big picture stuff,
Speaker 1: asking themselves, are we working on the right problems?
Speaker 5: I feel like he he just kind of saw AI
Speaker 5: what it could be doing, could be capable of, more
Speaker 5: clearly and earlier than anybody else. He's seeing it more
Speaker 5: optimistic than everybody else. He would come up with analogies like, okay,
Speaker 5: and the ONAL network is just a computer program. It's
Speaker 5: just a circuit. We're just programming it differently.
Speaker 1: Greg meanwhile, was grinding away.
Speaker 5: Greg is somebody who can just apply himself. He can
Speaker 5: just you know, keep working and keep working and keep working.
Speaker 5: I've seen some people like that, but very few.
Speaker 1: Even after open AI moved out of Greg's apartment, he
Speaker 1: still practically lived at the office. One former employee said
Speaker 1: he would be hunched over his laptop when they showed
Speaker 1: up to work in the morning and was still tapping
Speaker 1: away when they went home at night. Years later, when
Speaker 1: Greg got married, he even held a civil ceremony in
Speaker 1: the office with a big backdrop made of flowers, again
Speaker 1: in the shape of the open ai logo. The ring
Speaker 1: bear was a robot hand and the officiant was Ilia.
Speaker 1: When Greg and Ilia joined open ai, they didn't need money.
Speaker 1: Ilia had sold a company to Google, and Greg owned
Speaker 1: a lot of shares of stripe, and that company was
Speaker 1: worth tens of billions of dollars. In Silicon Valley, people
Speaker 1: usually create startups because they think they can build a
Speaker 1: lucrative business, but open ai was a nonprofit. Greg and
Speaker 1: Ilia were motivated by this dream. Here's Reid Hoffman, one
Speaker 1: of the earliest donors to open ai.
Speaker 6: There was no equity upside for that. Initial crew was like, look,
Speaker 6: we're doing this for humanity.
Speaker 1: Doing it for humanity. Open ai talks like this all
Speaker 1: the time. Their website says, our mission is to ensure
Speaker 1: that artificial general intelligence benefits all of humanity. Okay, So
Speaker 1: it's well known that Silicon Valley loves grandiose mission statements.
Speaker 1: We work wanted to elevate the world's consciousness, but open
Speaker 1: AI's mission statement is even more sweeping. And it has
Speaker 1: this overtone of altruism. When Sam talks about the company's work,
Speaker 1: he often discusses potential disasters. Here he is with Rebecca
Speaker 1: Jarvis on ABC. His voice sounds grave. Again, he's positioning
Speaker 1: himself as a thought leader in this space.
Speaker 7: So what is the worst possible outcome?
Speaker 4: There's like a set of very bad outcomes. One thing
Speaker 4: I'm particularly worried about is that these models could be
Speaker 4: used for large scale disinformation. I am worried that these systems,
Speaker 4: now that they're getting better at writing computer code, could
Speaker 4: be used for offensive cyber attacks.
Speaker 7: But you raise an important point, which is the humans
Speaker 7: who are in control of the machine right now also
Speaker 7: have a huge amount of power.
Speaker 4: We do worry a lot about authoritarian governments developing this.
Speaker 7: Putin has himself said, whoever wins this artificial intelligence race
Speaker 7: is essentially the controller of humankind.
Speaker 5: Do you agree with that?
Speaker 4: So that was a chilling.
Speaker 1: Statement for sure, Sam saying this stuff is so valuable
Speaker 1: that global superpowers are going to fight over it. The
Speaker 1: cynical take is that if you make what you're working
Speaker 1: on sound really important, you attract a lot more attention
Speaker 1: and money. We'll talk more about this dynamic in the
Speaker 1: next episode. In open AI's early years, their humanity saving
Speaker 1: plan wasn't that clear. Their strategy was a bit scattered.
Speaker 1: Here's Peter again.
Speaker 5: We looked at robotics, did some work there. We looked
Speaker 5: at simulated robotics, did a bunch of work there. We
Speaker 5: looked at digital agents that navigate the web and do
Speaker 5: all kinds of tasks online, like booking flights. We looked
Speaker 5: at video games.
Speaker 1: Open Ai said one of its first goals would be
Speaker 1: to build a robot butler which could set and clear
Speaker 1: a table, kind of like the maid on the Jetsons.
Speaker 7: Coming Sir, Hey William Sir.
Speaker 1: The company also built a robot arm that could solve
Speaker 1: a Rubik's Cube single handedly, and they put a lot
Speaker 1: of effort into building bots that could play Doda two,
Speaker 1: a massively popular multiplayer video game. They imagined that the
Speaker 1: complexity of the game environment could lead to an AI
Speaker 1: that could better navigate the real world. Here's someone testing
Speaker 1: the bot.
Speaker 6: The bot is good. The bot is better than I
Speaker 6: could have ever imagined.
Speaker 1: Those Doda bots even competed against professional players.
Speaker 7: Already up stop the actions kicking off sopen Ai will
Speaker 7: claim first blood.
Speaker 5: No tawn is to be caught out here.
Speaker 1: A bot that could play Doda was technically impressive, but
Speaker 1: it didn't look very impressive to the average person, and
Speaker 1: the commercial applications for these products were not immediately clear.
Speaker 1: Here's how one former employee put it. We were doing
Speaker 1: random stuff and seeing what would happen. There were not
Speaker 1: really defined goals. Sometimes it felt like there was a
Speaker 1: big gap between what was being built and what was
Speaker 1: being imagined. People would spend their days programming bots that
Speaker 1: played video games. Then they would sit around the lunch
Speaker 1: table and talk about saving humanity. The prevailing wisdom in
Speaker 1: the AI world was that in order to make something powerful,
Speaker 1: you sometimes have to start with something trivial. Video games
Speaker 1: and robot maids would pave the way to self driving
Speaker 1: cars and cancer curing AI. Internally, at open AI, they
Speaker 1: sometimes compared themselves to the Manhattan Project, the team given
Speaker 1: the mission to create the first atomic bomb, and they
Speaker 1: meant it as a good thing, ambitious and important. Here's
Speaker 1: how one former employee described it to me. It's an
Speaker 1: arms race. They all want to make the first AGI.
Speaker 1: They believe they can do it best. I didn't see
Speaker 1: a lot of fear of AI itself. I just saw
Speaker 1: excitement to build AI. Back in twenty fifteen, AI looked
Speaker 1: pretty different from today. It was weaker and harder to train.
Speaker 1: At the time, the major breakthrough was that a bot
Speaker 1: had been able to beat the world's best player and Go,
Speaker 1: a complex strategy board game from China, but that AI
Speaker 1: could only play Go, it couldn't do anything else. Here's
Speaker 1: Orn Etzioni, a computer science professor and the former research
Speaker 1: director for an AI institute.
Speaker 8: The thing about these is these were narrow systems, very
Speaker 8: highly targeted. So the system that played Go couldn't even
Speaker 8: play chess, certainly could not cross the street or understand language.
Speaker 8: And the system that understood airfare fluctuations and predicted very
Speaker 8: well whether airfares were going up or down could not
Speaker 8: handle text either. Right, So basically, every time you had
Speaker 8: an application, you'd have to train up a new system.
Speaker 8: And this took a long time, took a lot of
Speaker 8: labeled data, etc.
Speaker 1: But then came a major breakthrough in AI technology. In
Speaker 1: twenty seventeen, a group of researchers from Google Brain published
Speaker 1: a paper called Attention Is All You Need, and in
Speaker 1: it they describe a new kind of AI architecture called
Speaker 1: the transformer, and the transformer did something huge. At the time,
Speaker 1: AI systems needed to be fed very specific data. Each
Speaker 1: piece of data had to be labeled this is correct,
Speaker 1: this is incorrect. Spam not spam, cancer not cancer. But
Speaker 1: the transformer allowed AI to take in messy, unlabeled data,
Speaker 1: and it could actually do so even more efficiently than expected,
Speaker 1: using less computing power than before. Now these transformer based
Speaker 1: models could just teach themselves in a way. It was
Speaker 1: like if you wanted to teach a kid to read,
Speaker 1: and you used to have to hire a tutor to
Speaker 1: sit there with flashcards, and now instead you could just
Speaker 1: let the kid run through a library and they would
Speaker 1: emerge knowing how to read and write. This was, as
Speaker 1: one investor described to me, a surprising and bitter realization
Speaker 1: that the best AI would come not from the most
Speaker 1: specialized training techniques, but from whichever had the most data. Peter,
Speaker 1: the early open AI employee, says Ilia immediately saw its promise.
Speaker 5: Ilia's reaction was pretty affirmative right away. It's like, this
Speaker 5: is something special we need to be we need to
Speaker 5: be looking at this. This seems a big breakthrough.
Speaker 1: Even in the early days of open AI, Ilia had
Speaker 1: always had this hunch that big advances in AI wouldn't
Speaker 1: come from some specific tweak or new invention, but just
Speaker 1: from more data pouring more and more fuel into the engine.
Speaker 1: And now Ilia had the research that backed up his hypothesis.
Speaker 8: Here's Oren again, Ilia from open ai is known as
Speaker 8: the person who said, it's the data, and it's the
Speaker 8: amount of data, and if we just scale that up
Speaker 8: tremendously of magnitude much much more, we're going to achieve
Speaker 8: what we need. That was not the common perception and
Speaker 8: some very smart and very famous people. I don't want
Speaker 8: to cast aspersions, but certainly I'm I'm not that smarter
Speaker 8: with that famous, but I'm one of the AI experts
Speaker 8: who did not see that coming.
Speaker 1: Because of Ilia, open Ai started experimenting with the transformer.
Speaker 1: They were one of the earliest companies to do so.
Speaker 1: They made models with the now familiar acronym GPT Generative
Speaker 1: pre Trained Transformer, and in particular, they started experimenting with
Speaker 1: how the transformer performed with written words, because they could
Speaker 1: basically feed the model anything written any book, newspaper, article,
Speaker 1: reddit posts, blogs. Humans have spent a lot of time
Speaker 1: writing things down, and those words now had another purpose,
Speaker 1: training data. The Internet wasn't created to train, but in
Speaker 1: the end that may become its legacy. Open aiyes, models
Speaker 1: got better and better at generating text, and they weren't
Speaker 1: limited to just one field of knowledge.
Speaker 8: The amazing thing about these GPT systems is that they're
Speaker 8: very broad. They are actually generalists. You can ask them
Speaker 8: about virtually any topic and they'll produce surprisingly good answers.
Speaker 8: And that's because they've been trained on effectively the entire
Speaker 8: or at least an approximation of the entire corpus of
Speaker 8: text that's available to humanity, billions of billions of sentences,
Speaker 8: all the books you've read, all the documents, the memos,
Speaker 8: the silliness, Harry Potter fan fiction. It's all grist for
Speaker 8: the mill. And then once it's read all that, it's
Speaker 8: remarkably general. So for the first time we would have
Speaker 8: a system that you could ask it about anything and
Speaker 8: it would give you a surprisingly intelligent answer. So we
Speaker 8: went from narrow AI to a kind of general or
Speaker 8: broad AI.
Speaker 1: Through the massive amount of writing that they were feeding
Speaker 1: into their models, open ai found they could create AI
Speaker 1: that was much much better at forming convincing sounding responses
Speaker 1: to questions. In fact, at some point they started to
Speaker 1: worry it was maybe too good. When OpenAI announced its
Speaker 1: language model GBT two, they initially decided not to share
Speaker 1: the model more openly because they were concerned it could
Speaker 1: be dangerous. Here's Peter. He had by that time left
Speaker 1: open ai to start his own company, but he remembers
Speaker 1: the day of the release.
Speaker 5: It was just obvious that it had a much better
Speaker 5: understanding of language than anything that had been trained before.
Speaker 5: Its release was indeed accompanied by a lot of I
Speaker 5: guess great marketing or caution or combination of both. It
Speaker 5: was headline that's too dangerous to be released, and so
Speaker 5: I think it was probably one of the first projects
Speaker 5: where open i decided to not release some of the
Speaker 5: work because all of a sudden, the thinking had become, well,
Speaker 5: what if it's something is so powerful that people could
Speaker 5: go misuse it in ways that we can't control.
Speaker 1: As soon as open ai had a product that was
Speaker 1: actually powerful, they started rethinking their openness.
Speaker 5: Open Ai started with that name where to open really
Speaker 5: stood for everything's going to be open sourced built, you know,
Speaker 5: anybody else can build on it.
Speaker 1: Openness was a crucial part of the company's brand when
Speaker 1: they were founded, Sam told the journalist Stephen Levy, it
Speaker 1: will just be open source and usable by everyone. He
Speaker 1: also told him that their AI would be freely owned
Speaker 1: by the world. Open source software in its broadest sense,
Speaker 1: means that the source code is made available to the
Speaker 1: public freely, and that anyone can tweak the code and
Speaker 1: distribute it themselves. But the company soon started walking back
Speaker 1: those commitments.
Speaker 5: Obviously that evolved over time into something that is not
Speaker 5: so open source, if open source at all for anything.
Speaker 5: I mean, it's definitely not open sourcing. It's hits work.
Speaker 1: Right now, that open source ethos seemed to fade away.
Speaker 1: Here's Sam giving a talk in Munich in twenty twenty three.
Speaker 4: I'm curious, if we stay on the same like GPT
Speaker 4: two to three to four trajectory for five and six,
Speaker 4: how many of you would like us to open source
Speaker 4: GPT six the day we finished training it. Wow, Well
Speaker 4: we're not going to do that. But that's interesting that.
Speaker 1: Honestly, Sam sounds pretty arrogant here. He knows open AI
Speaker 1: started off with promises of being open source. Now he's
Speaker 1: pulling the audience about open sourcing models and immediately dismissing
Speaker 1: their response. Over the years, Sam has subtly changed the
Speaker 1: meaning of openness. It's become fuzzier here he is at
Speaker 1: a VC firm.
Speaker 4: So I think that is that's what we call open ai,
Speaker 4: open AI. We want this to be open technology made
Speaker 4: available to.
Speaker 1: Everyone, open technology made available to everyone. He says it
Speaker 1: so plainly, as though that's obvious what open means, But
Speaker 1: his definition strikes me as so vague that it's essentially meaningless.
Speaker 1: I mean, Google Search is available to everyone. It seems
Speaker 1: like open ai was happy to let people guess what
Speaker 1: they meant by open. In an internal email just months
Speaker 1: after it was founded, Ilia wrote, as we get closer
Speaker 1: to building AI, it will make sense to start being
Speaker 1: less open. The open in open ai means that everyone
Speaker 1: should benefit from the fruits of AI after it's built.
Speaker 1: But it's totally okay to not share the science, even
Speaker 1: though sharing everything is definitely the right strategy in the
Speaker 1: short and possibly medium term for recruitment purposes. This email
Speaker 1: was really interesting because it shows that from the beginning
Speaker 1: open ai had planned not to freely share their se science.
Speaker 1: They didn't want to be open source, as they claimed,
Speaker 1: but they wanted to keep up the public appearance of
Speaker 1: openness because it gave them a recruiting advantage like, don't
Speaker 1: go build AI for the bad guys, come work for us,
Speaker 1: the open virtuous choice. When we asked about their changing
Speaker 1: definition of openness, a company spokesperson said, our mission has
Speaker 1: remained the same, but our tactics have had to change. Okay,
Speaker 1: So let me bring us back to twenty seventeen. It's
Speaker 1: two years after the company's founding, and another problem was
Speaker 1: brewing an Open AI a power struggle. Elon wanted to
Speaker 1: take over. He's someone who's used to being in charge.
Speaker 1: According to open AI, he wanted to move the company
Speaker 1: under Tesla, and he wanted to be CEO, and he
Speaker 1: wanted majority equity, and if it couldn't be done his way,
Speaker 1: he was out.
Speaker 9: Like with everything Elon, as time goes on, he wants
Speaker 9: to assert more and more control and make sure the
Speaker 9: company is operating in exactly the image and way that
Speaker 9: Elon wants it to operate. And so this is going
Speaker 9: to create tension.
Speaker 1: That's Ashley Vance, my colleague who has written a biography
Speaker 1: of Elon.
Speaker 9: Elon's preferred role in anything is to be the CEO
Speaker 9: and the dominant force and the one who controls what's
Speaker 9: going on day to day and.
Speaker 1: The guys actually running the day to day. Greg Brockman
Speaker 1: and Ilias Aitzkiv were wary because Elon was reckless, impulsive,
Speaker 1: and difficult, but he was also their main source of money.
Speaker 1: He had pledged them almost a billion dollars. Open Ai
Speaker 1: had other donors, but nothing close. One option was to
Speaker 1: go with Elon and keep the money.
Speaker 9: The employees weren't all on board with that idea and
Speaker 9: had some concerns, and so you get to this, you
Speaker 9: get to this decision point where it's kind of like,
Speaker 9: you know, are we going to go on with Elon
Speaker 9: or without him? Almost always in recent years people have
Speaker 9: kind of put up with Elon and his demands.
Speaker 1: Or another option was to split with Elon and figure
Speaker 1: out how to get a different source of cash, you
Speaker 1: know who would probably be good at raising money. Sam Altman.
Speaker 9: Reached's point where Elon wanted the company to go one
Speaker 9: way and then the employees wanted it to go another,
Speaker 9: and Sam was picked as the person to lead open
Speaker 9: Ai forward.
Speaker 1: Sam hadn't been that involved in open Ai for the
Speaker 1: first few years. He was still president of YC actually,
Speaker 1: but in this jostling for power, Sam beat out Elon,
Speaker 1: and that's a big deal. Elon was much more famous
Speaker 1: and experienced, and notably, he hates losing well.
Speaker 9: In most conflicts, Elon reacts by trying to win at
Speaker 9: all costs, and whatever whatever which earth you know, may
Speaker 9: may arise from that, Elon doesn't. He doesn't lose too
Speaker 9: many battles. Usually he either. If it's not within a company,
Speaker 9: he usually sues somebody into submission. If it is within
Speaker 9: a company, he throws his weight around in politics until
Speaker 9: he gets what he wants. It's hard to find too
Speaker 9: many examples in recent years where he did not get
Speaker 9: what he wants, and so the turmoil inside of the
Speaker 9: company must have been quite drastic in order for this
Speaker 9: not to happen.
Speaker 1: So in twenty eighteen, Elon walked away in a huff
Speaker 1: and took his money with him. Years later, he'll actually
Speaker 1: end up suing Sam and open Ai, claiming they broke
Speaker 1: their original commitment about remaining nonprofit and open source. Soon
Speaker 1: after Elon left, Sam became CEO of open Ai. There
Speaker 1: hadn't been a CEO before, but this power struggle crystallized
Speaker 1: Sam's new dominance over this company. Remember what the founder
Speaker 1: of YC once said, Sam is extremely good at becoming powerful.
Speaker 1: Sam's excitement about open Ai kept growing, his attention started
Speaker 1: drifting away from his job running YC. Sure, running a
Speaker 1: world famous startup accelerator is a position of a lot
Speaker 1: of influence, but the race for building AGI was heating up,
Speaker 1: and if OpenAI succeeded in creating AGI before anyone else,
Speaker 1: it's hard to imagine a position in the world with
Speaker 1: more power than being its CEO. But Sam didn't give
Speaker 1: up his job at YC right away. This situation made
Speaker 1: some of the people running the accelerator grumble. They felt
Speaker 1: like Sam was spread too thin, pushing to expand too fast,
Speaker 1: and prioritizing his own interests above those of YC. It
Speaker 1: earned him some enemies within his own ranks. In fact,
Speaker 1: according to a source, Sam's mentor, Paul Graham, the guy
Speaker 1: who put him in the job in the first place,
Speaker 1: flew in from the UK to ask Sam in person
Speaker 1: to step down. Paul had lost confidence in his former protege,
Speaker 1: but he also didn't want to create public drama, so
Speaker 1: Sam was ushered out and they kept the backstory quiet.
Speaker 1: Now focused only on open Ai, Sam had one big
Speaker 1: goal to raise money to train open Aiy's models. They
Speaker 1: needed a lot of computing power, and computing power is expensive.
Speaker 1: Sam tried to raise money but wasn't getting traction. Here
Speaker 1: he is on the Lex Friedman podcast.
Speaker 4: We started as a nonprofit. We learned early on that
Speaker 4: we were going to need far more capital than we
Speaker 4: were able to raise as a nonprofit to do what
Speaker 4: we needed to go do. We had tried and failed
Speaker 4: enough to raise the money as a nonprofit. We didn't
Speaker 4: see a path forward there. So we needed some of
Speaker 4: the benefits of capitalism, but not too much. I remember
Speaker 4: at the time someone said, you know, as a nonprofit,
Speaker 4: not enough will happen. As a for profit, too much
Speaker 4: will happen.
Speaker 1: They needed something in the middle, and honestly, Sam doesn't
Speaker 1: sound that hung up about leaving nonprofit life behind. He
Speaker 1: Frankenstein something together. Basically, he created a for profit entity
Speaker 1: that lived under the umbrella of the original nonprofit. The
Speaker 1: for profit could do all the things normal companies do,
Speaker 1: like raise investment and offer equity to employees, but its investors'
Speaker 1: returns were capped, whereas at other companies they'd be unlimited.
Speaker 1: This corporate structure was grafted together. Open Ai was essentially
Speaker 1: now a for profit controlled by the board of the nonprofit,
Speaker 1: which sounds a little unstable. Open Ai had spent years
Speaker 1: saying they would be a non profit. Now they had
Speaker 1: come up with this for profit workaround. After that change,
Speaker 1: a lot of people were upset, but open Ai was
Speaker 1: more focused on their end goal. They wanted to build
Speaker 1: Agi and they needed to raise money to do it.
Speaker 1: And then in twenty nineteen, Sam the deal maker made
Speaker 1: a big, hugely important deal. He raised a billion dollars
Speaker 1: from Microsoft. Here's Microsoft CEO Satya Nandela after they signed
Speaker 1: the deal.
Speaker 4: Hi, I'm here with Sam Altman's CEO of open Ai.
Speaker 2: Today.
Speaker 4: We are very excited to announce a strategic partnership with
Speaker 4: open Ai.
Speaker 1: And I thought one important thing Microsoft had was lots
Speaker 1: of raw computing power and open Ai could now use it.
Speaker 1: Remember open Ai had originally been conceived to be an
Speaker 1: antidote to Google. They presented themselves as fundamentally different from
Speaker 1: profit hungry tech giants, and then overnight they became intimately
Speaker 1: enmeshed with a tech company worth more than a trillion dollars.
Speaker 1: Now open Ai was in many ways an arm of Microsoft.
Speaker 1: This was a remarkable about phase. Reid Hoffman was on
Speaker 1: the board of open Ai and on the board of
Speaker 1: Microsoft at the time of the deal. He didn't see
Speaker 1: this as an abdication of open AI's initial premise.
Speaker 6: There were parties who worried about with this corrupt the mission.
Speaker 6: But you know, I think that's a little bit of
Speaker 6: like kind of a modern naivete is to say corporation
Speaker 6: equals bad or corrupt, and it's just naive because there's
Speaker 6: lots of ways that companies are are collaborative with humanity anxiety.
Speaker 6: They try to serve the customer as well, they hire employees,
Speaker 6: they have shareholders, they exist within societies.
Speaker 1: Okay, So Reid's perspective is that just because you want
Speaker 1: to make money doesn't mean you're bad, which is on
Speaker 1: brand for a billionaire venture capitalist. And I guess one
Speaker 1: way to look at it is that the Microsoft deal
Speaker 1: may have been the most practical way for open ai
Speaker 1: to continue its mission of creating safe agi for all
Speaker 1: of humanity, but it also highlighted an important pattern so
Speaker 1: that OpenAI often walked back its promises when it was
Speaker 1: convenient to do so. And amid all this, people started
Speaker 1: to doubt Sam's integrity both inside and outside the company,
Speaker 1: and that would lead to a major rift. That's next
Speaker 1: time on Foundering. Foundering is hosted by me Ellen Hewitt.
Speaker 1: Sean Wen is our executive producer. Rachel Metz contributed reporting
Speaker 1: to this episode. Molly Nugent is our associate producer. Blake
Speaker 1: Maples is our audio engineer. Mark Million and Vandermay Seth Fiegerman,
Speaker 1: Tom Giles and Molly Schutz are our story editors. We
Speaker 1: had production help from Jessica Nix and Antonia Mufferetch. Thanks
Speaker 1: for listening. If you like our show, leave a view,
Speaker 1: and most importantly, tell your friends. See you next time.