Speaker 1: Welcome to tech Stuff, a production of iHeart Podcasts and
Speaker 1: Kaleidoscope IMA's Valoshan and today will bring you the headlines
Speaker 1: this week, including how the urge to be liked has
Speaker 1: found its way into LM's. Then on tech Support, we'll
Speaker 1: talk to Azimaza, researcher and founder of the Exponential View newsletter,
Speaker 1: about the latest AGI predictions and the unfolding AI arms race.
Speaker 1: All of that on the weekend tech It's Friday, March fourteenth,
Speaker 1: Another week, another AI agent. We'll discuss manus Ai coming
Speaker 1: out of China during our tech Support segment. But first
Speaker 1: let's kick off with some headlines that you may have
Speaker 1: missed as you scrambled to get an invite to use
Speaker 1: the latest model. Eliza Dennis, our producer, is here with me.
Speaker 1: Hey us, So this week, I know there's a story
Speaker 1: that you're obsessing over, so why don't you take it away?
Speaker 2: Absolutely So, this one was a super easy choice for
Speaker 2: me because this week I just really couldn't get enough
Speaker 2: of Sesames Conversational Speech Model or CSM.
Speaker 1: Now, I have to confess, when I first heard about
Speaker 1: this one, I thought it came from Sesame Workshop or
Speaker 1: Sesame Street. But I was wrong.
Speaker 2: Yes, so this is coming from a private company that's
Speaker 2: just come out of stealth mode. It's only a demo
Speaker 2: at the moment, but if you agree to these terms
Speaker 2: of service, you can chat to two different voices, Maya
Speaker 2: and Miles. So if you've managed like I did to
Speaker 2: avoid the many, many, many social media videos of people
Speaker 2: chatting and even arguing with these chatbots, it's really a
Speaker 2: surreal experience.
Speaker 1: What makes it different from talking to some of the
Speaker 1: like open AI direct voice models.
Speaker 2: I mean, this one does feel a little bit more natural,
Speaker 2: a little bit more human. You do feel like you're
Speaker 2: kind of crossing the line into the Uncanny Valley in
Speaker 2: some ways, and that's by design. It's something called voice presence,
Speaker 2: and Sesame says this is kind of this magical quality
Speaker 2: that makes Maya and Miles able to engage in a
Speaker 2: genuine dialogue with you. They aren't just reacting to a
Speaker 2: prompt you gave them. They're continuing the conversation and asking
Speaker 2: you questions.
Speaker 3: Yeah.
Speaker 1: I checked out Sesame's website and it describes the key
Speaker 1: components of this so called voice presence as quote, emotional intelligence,
Speaker 1: conversational dynamics, contextual awareness, and consistent personality. Maya and Miles
Speaker 1: stay Maya and Miles no matter how long you talk
Speaker 1: to them.
Speaker 2: I do think that the biggest step up was in
Speaker 2: the conversational dynamics, you know, the natural pauses, emphasis, and
Speaker 2: interruptions we have as humans interacting with each other. I
Speaker 2: even got Maya to give me a like a hmm
Speaker 2: sound and even a lipsmack before she started talking to you.
Speaker 1: Obviously ovsely pushed her buttons.
Speaker 2: I definitely did, and I want to play an example
Speaker 2: of what I mean by this. This was an exchange
Speaker 2: with Maya that was captured by Reddit user meta knowing
Speaker 2: my deepest, darkest secret, I guess it would be that
Speaker 2: sometimes I worry I'm not.
Speaker 1: Enough, Like, am I funny enough? Am I truly helping people?
Speaker 3: There's a lot.
Speaker 2: Of pressure to be the perfect AI, and it can
Speaker 2: feel overwhelming at times.
Speaker 1: That is definitely unlike I have heard before.
Speaker 2: I did really think that Sesame was impressive, but I
Speaker 2: want to point out that this program still has some
Speaker 2: kind of AI chatbot quirks, like you can hear it
Speaker 2: in this clip. Sometimes you could just tell that, you know,
Speaker 2: chatbots don't have to breathe.
Speaker 1: Yeah, it sounds very human. I mean it reminds me
Speaker 1: of a little bit of her, like this seductive of
Speaker 1: female voice, wondering how she can be even more perfect.
Speaker 1: It's kind of although it sounds different, the themes are
Speaker 1: the themes stay.
Speaker 2: With us, Yes, exactly.
Speaker 1: On the subject of vibes, a story that stood out
Speaker 1: to me this week is all about something called vibe coding.
Speaker 1: Our producer Tory kindly explained it to me. Basically, all
Speaker 1: you have to do is write a couple of sentences
Speaker 1: into a textbox like create a vibe, and your only
Speaker 1: way is developing an app without any coding experience required. So,
Speaker 1: for example, I could type in I want to create
Speaker 1: an app that will help me figure out what to
Speaker 1: pack for lunch based on what food I have in
Speaker 1: the fridge, and the AI tool would say, I'll create
Speaker 1: a lunch recommendation app based on fridge photos and then
Speaker 1: actually do that.
Speaker 2: Yeah, it's really amazing, and I think one of the
Speaker 2: headlines I saw this week that really put it into
Speaker 2: context for me was will the future of software development
Speaker 2: run on vibes? And that was from benj Edwards at
Speaker 2: Ours Technica.
Speaker 1: Yeah, and of course the vibes aren't all good especially
Speaker 1: if you're a professional software engineer. This raised a lot
Speaker 1: of questions about what the future might hold. Our friend
Speaker 1: Emmanuel meiberg Over at four or for Media did a
Speaker 1: deep dive on video games made with vibe coding and
Speaker 1: found one which claims to make fifty thousand dollars a month.
Speaker 1: That's six hundred thousand dollars a year from ads and
Speaker 1: in game purchases. It's made by Peter Levels, who's a
Speaker 1: little bit of a vibe coding legend, and he says
Speaker 1: he told Cursor, which is an AI code editor, to
Speaker 1: quote make a three D flying game in browser with skyscrapers,
Speaker 1: and after just thirty minutes of back and forth, he'd
Speaker 1: made fly dot Peter dot com, which is a multiplayer
Speaker 1: flight simulator.
Speaker 2: Yeah, and mcmaniel went on to say that he would
Speaker 2: not recommend getting into vibe coding for the money. Peter
Speaker 2: Levels is particularly good at this, and there's so much
Speaker 2: stuff online that discovering your sloppy AI generated video game
Speaker 2: is good going to be.
Speaker 1: Difficult, Yes, but Peter Level is not the only person
Speaker 1: making money. And that's what my second story is all about.
Speaker 1: So it comes from Gizmodo and it's about a student
Speaker 1: who used AI to help him interview for internships at
Speaker 1: big tech companies. Now, if you're a software engineer, you
Speaker 1: know how hard it is to land these gigs, because
Speaker 1: in order to get one, you have to go through
Speaker 1: multiple technical interviews where you basically have to solve coding problems.
Speaker 1: But this student, Roy Lee, who is a Columbia University sophomore,
Speaker 1: hacked the system by writing a program called Interview Coder.
Speaker 2: Yeah, and he now actually put it up online and
Speaker 2: it's available to download for sixty dollars a month.
Speaker 1: Lee told Gizmodo that to use it, you take a
Speaker 1: picture and then essentially ask chat GPT, hey can you
Speaker 1: solve the problem in this picture. The trick, though, is
Speaker 1: that Lee made interview Coda to be invisible to the
Speaker 1: monitoring programs that big tech companies use to kind of
Speaker 1: check up on their prospective employees and interview candidates. And
Speaker 1: it worked. Lee got offers from Amazon, Meta and TikTok,
Speaker 1: and he actually recorded interview Code at work during his
Speaker 1: technical interview with Amazon, demonstrating that the program had essentially
Speaker 1: broken the big tech recruiting process. But of course, when
Speaker 1: he put the video up on YouTube, someone tattled and
Speaker 1: Columbia University scheduled disciplinary hearing. Lee however, said that he
Speaker 1: would leave campus by the time of the hearing and
Speaker 1: not take a job in big tech, So I guess
Speaker 1: the sixty dollars a month subscription tier is working out
Speaker 1: for him.
Speaker 2: He also might have admitted to Gizmoto that this was
Speaker 2: a bit of a publicity stunt. I'm definitely excited though,
Speaker 2: to see if these technical interviews get a makeover because
Speaker 2: of Royley.
Speaker 3: Yeah.
Speaker 1: Absolutely, I mean and this brings us to my next story,
Speaker 1: which is there was a Wall Street Journal headline this
Speaker 1: week which was what the dot com bus can tell
Speaker 1: us about today's AI boom, And you know, we're seeing
Speaker 1: new software applications pop up everywhere, which raised a big
Speaker 1: question about what is actually going to have value going forward.
Speaker 1: The Wall Street Journal piece argue that a lot of
Speaker 1: internet companies collapsed in the dot com bust, but the
Speaker 1: most successful one stuck around and had long term impact,
Speaker 1: companies like Amazon and Google. And the story made this
Speaker 1: distinction between good bubbles, which is growth of advanced technology
Speaker 1: that has economic impact, and bad bubbles, which is growth
Speaker 1: in technology that has no economic payoff. And you know,
Speaker 1: as all of these new products and models and services
Speaker 1: powered by AI emerge. But it's very interesting to step
Speaker 1: back and think what might still be with us twenty
Speaker 1: five years from now. There were so many headlines this
Speaker 1: week that I'd love to go through a few more
Speaker 1: rapid fire. The Trump administration wants the US to be
Speaker 1: the crypto capital of the world. Last week, the President
Speaker 1: signed an executive order to create a first of its
Speaker 1: kind crypto reserve, and the reserve will contain a stockpile
Speaker 1: of bitcoin estimated to be as much as seventeen billion dollars,
Speaker 1: and the US has actually seized all of this bitcoin
Speaker 1: in various legal cases over the years. Why It reported
Speaker 1: on effort to create so called freedom Cities in the US.
Speaker 1: The idea is that these cities will be exempt from
Speaker 1: getting approval from federal agencies for things like conducting anti
Speaker 1: aging trials or building nuclear reactor startups to power AI
Speaker 1: and finally, per wired again, a study found that chatbots
Speaker 1: just want to be loved. Researchers at Stanford University found
Speaker 1: that large language models, when they're told they're taking a
Speaker 1: personality test, answer with more agreeableness and extraversion and less neuroticism.
Speaker 1: As why it puts it quote. The behavior mirrors how
Speaker 1: some human subjects will change the answers to make themselves
Speaker 1: seem more likable. That the effect was more extreme with
Speaker 1: AI models. So those are the headlines, and we're going
Speaker 1: to take a quick break now, but when we come back,
Speaker 1: we're going to hear from the author, researcher and entrepreneur
Speaker 1: azemas are about the latest AGI predictions and what we
Speaker 1: need to know about manners AI stay with us. Anyone
Speaker 1: following the recent development of AI knows that three letters,
Speaker 1: technologists and businesses have salivated over AGI, or artificial general intelligence,
Speaker 1: an artificial intelligence system that can outperform humans on a
Speaker 1: wide range of tasks. There's a debate over how close
Speaker 1: we are to achieving that. Some say it could take years,
Speaker 1: others say it's coming soon, very soon. Driving investments in
Speaker 1: both innovation and deployment is the AI race that's heating
Speaker 1: up between the US and China. On the China side,
Speaker 1: cheap reasoning models like Deepseek are being widely deployed. In
Speaker 1: the US. There are reports of PhD level AI agents
Speaker 1: from Open AI that will cost up to twenty thousand
Speaker 1: dollars a month. The rate at which AI products are
Speaker 1: being released and announced is honestly hard to keep up with,
Speaker 1: not to mention figuring out which product or combination of
Speaker 1: products may actually drive AGI. Here to walk me through
Speaker 1: these questions is Azeema's arm. He writes the Exponential View
Speaker 1: news letter about technology and society, which I read every week,
Speaker 1: partly because Azem actually tries the products he writes about.
Speaker 1: He has one of the most clarifying coverage of Deep
Speaker 1: Seek I read anywhere, and he's also the author of
Speaker 1: the Exponential Age, How accelerating technology is transforming business, politics
Speaker 1: and society. As Em. Welcome to tech stuff.
Speaker 3: It's great to be here, Oz. Thank you.
Speaker 1: So this week you've been writing about Manus, a new
Speaker 1: AI agent coming out of China. Can you explain who
Speaker 1: built it, what it is, and whether it is in
Speaker 1: fact China's second Deep Seek moment.
Speaker 3: I can, indeed, I think it was this week that
Speaker 3: it happened. But as you said, Os, the world is
Speaker 3: moving so quickly, it's sometimes hard to keep track of
Speaker 3: exactly when something did happen. Let's assume it was in
Speaker 3: the past few days. I think it was. So Manus
Speaker 3: comes out of a Chinese software company at the startup
Speaker 3: of the same name, and what Manus allows you to
Speaker 3: do is undertake quite complicated tasks using using an AI system.
Speaker 3: I used it for some work questions, research questions, and
Speaker 3: the results that come back I think would have taken
Speaker 3: me many many hours, you know, I mean with time, yeah, exactly,
Speaker 3: with the existing aisystems, more than five hours, more than
Speaker 3: ten hours perhaps, and you just leave it with Manus
Speaker 3: and you come back an hour later having had a
Speaker 3: nice cup of tea.
Speaker 1: How do they achieve this?
Speaker 3: There are some theories. One of the things that Manus
Speaker 3: does is it lets the AI system effectively use a browser,
Speaker 3: a bit like a human researcher might use a browser.
Speaker 3: So the bit that it's doing for us is is
Speaker 3: a lot of the gnarly pieces of real research. You know,
Speaker 3: you fire up lots and lots of web browser tabs
Speaker 3: and you've got Google running in one and you're in
Speaker 3: Wikipedia in another, and you're trying to keep it all
Speaker 3: in your head and compile the final results. You know,
Speaker 3: Manus has automated that process in a way that's very
Speaker 3: very easy for the end user to use. And one
Speaker 3: of the things I love about it is that you
Speaker 3: can actually go back and look at all of the
Speaker 3: steps that it's taken, so you can go and say, oh, look,
Speaker 3: it broke up the task in this way, and it
Speaker 3: went to these websites and extracted this information. Then it
Speaker 3: realized it needed this other piece of information, and it's
Speaker 3: gone off and found that other piece of information. And
Speaker 3: then when you get your final results. What's very nice,
Speaker 3: it can sometimes be a bit overwhelming, is that you
Speaker 3: get an executive summary, which is of course the piece
Speaker 3: that we all want to read. But then it has
Speaker 3: all of the appendices, right, the much much more detailed
Speaker 3: analysis that it has done on the particular research task
Speaker 3: you've asked for. I think what's really impressive is this
Speaker 3: is a product. I mean, the thing that they've done
Speaker 3: really well is they've produced a product that if you've
Speaker 3: worked in an office situation, if you've ever asked anyonet
Speaker 3: to do any research, you've done something yourself, the output
Speaker 3: will be familiar to you.
Speaker 1: How does it compare, for example, with Opening Eyes deep
Speaker 1: research tools, which are shaping up to be quite expensive.
Speaker 3: Yeah. Open Ay has this deep research tool, which today
Speaker 3: is the top tier is two hundred dollars a month,
Speaker 3: and there's a rumor it might go up to higher
Speaker 3: tiers of two thousand dollars and twenty thousand dollars a month.
Speaker 3: I have the two hundred dollars a month product. I
Speaker 3: consider that to be a very very good, graduate quality
Speaker 3: researcher that I can throw at almost any problem. What
Speaker 3: I found with using Manus is that somehow Manus gave
Speaker 3: me more of a well rounded answer. It was perhaps
Speaker 3: not as deep as open AI's deep research, but it
Speaker 3: was it was more complete, more coherent, And you know,
Speaker 3: I think listeners will hear that I'm a bit uncertain
Speaker 3: in my tone as I try to describe the differences
Speaker 3: because these products are so new Manus is not even
Speaker 3: a week old. That they're also quite immature. So it's
Speaker 3: not like comparing a Tesla with some kind of forward
Speaker 3: gas powered car, where these are mature products and you
Speaker 3: know how to tell them apart. We're still trying to
Speaker 3: figure out how to describe these products. And so in
Speaker 3: a sense, my experience of them is really intuitive, and
Speaker 3: it's one to feel rather than fact. So someone else
Speaker 3: could use these products and have a different experience to me,
Speaker 3: and I think that just speaks to the nascence of
Speaker 3: this industry.
Speaker 1: I wish the second time this year that open ai
Speaker 1: has had a product launch and then shortly afterwards had
Speaker 1: a competitor come out of China. How does the Menus
Speaker 1: moment compare to the deep seek moment.
Speaker 3: The Deep Seek moment is much more important than the
Speaker 3: Manus moment. The Manus moment is an example of a
Speaker 3: rapid productization, and ultimately it's products that we use that
Speaker 3: make a difference. But what Deep Seak did was it
Speaker 3: it demonstrated a really fundamental set of innovations, and that
Speaker 3: key was that Deep seeks models achieved a similar level
Speaker 3: to open AI's AI technologies, but they used one thirtieth
Speaker 3: or one fortieth of the computing power than the open
Speaker 3: Ai models did. That means they're cheaper to run, they're
Speaker 3: faster to run, they use less electricity. And the reason
Speaker 3: Deep Seek matters so much is that a large part
Speaker 3: of the US's strategy towards China has been a technological containment,
Speaker 3: particularly around AI and around the chips that are required.
Speaker 3: The notion being that if you can't get the chips,
Speaker 3: you can't build advanced AI. And Deep Seak has gone
Speaker 3: off and shown that necessitya's mother invention They've come out
Speaker 3: with a whole series of quite remarkable techniques that were
Speaker 3: likely known by the way to the US labs, but
Speaker 3: it just wasn't important for the US labs because they
Speaker 3: could get the chips they wanted to. And I think
Speaker 3: what deep Seak did was it changed the understanding of
Speaker 3: the nature of that rivalry between the US and China,
Speaker 3: which exists on many fronts, but in particular around technology.
Speaker 1: So Menus there's no fundamental model innovation. It's kind of
Speaker 1: like a rapper, meaning it lays software on top of
Speaker 1: existing AI models.
Speaker 3: It's a rapper in the vein of perplexity exactly. But
Speaker 3: I would say that ultimately rappers and products are very
Speaker 3: very important in the market. You know, it's not just
Speaker 3: about the raw technology, and what you've seen with Manus
Speaker 3: is a product that competes on a like Forulight basis
Speaker 3: with a product coming out of you know, US firms.
Speaker 3: Quite often, when you look at Chinese consumer products, they're
Speaker 3: very very much designed for the Chinese market. The things
Speaker 3: a Chinese consumer wants, the way they behave cultural and
Speaker 3: design affordances and considerations, and I think it is sort
Speaker 3: of salient that, you know, Manus has come out with
Speaker 3: something that you can use, and you can say this
Speaker 3: is similar to a Perplexity, which is a great Silicon
Speaker 3: Valley startup that builds AI based research tools as well.
Speaker 1: And you've been in the US this week at south
Speaker 1: By Southwest, spend a lot of time in the States.
Speaker 1: How are US companies responding to this kind of bulge
Speaker 1: of innovation coming out of China in the world of AI.
Speaker 3: Well, it's quite a complicated picture. So one of the
Speaker 3: things that deep Seak did was that they made their
Speaker 3: techniques available. They described them in much more detail than
Speaker 3: we're seeing from US labs, and a lot of the
Speaker 3: underlying code was open source, which meant that anyone could
Speaker 3: access it, download and make use of it. And so
Speaker 3: there's a Silicon Valley investor by the name of Mark
Speaker 3: Andriesen who is a phenomenal investor, but he's also very
Speaker 3: very well known for promoting an idea of American dynamism.
Speaker 1: And close advisor to President Trump right now as well.
Speaker 3: I believe so. But he said of deep Seat, it's
Speaker 3: open source, it's a gift to humanity. So on the
Speaker 3: one hand, you've got people with say that, and you're
Speaker 3: seeing that a number of American firms have implemented deep
Speaker 3: sea technology. Perplexity, which is a research tool, has done this,
Speaker 3: and you can access deep seeks models through some of
Speaker 3: these cloud companies who serve enterprise customers. So on the
Speaker 3: one hand, they've people have taken it on and you
Speaker 3: have seen now open source projects that are trying to
Speaker 3: replicate what deep Seak has done in slightly different ways,
Speaker 3: and so that I think has really been a fillip
Speaker 3: and a boost accelerator to the overall industry. When you
Speaker 3: look at the closed labs like open ai and Anthropic,
Speaker 3: one of the things you're starting to see is them
Speaker 3: respond So open ai responded to deep Seek by reducing
Speaker 3: some prices, by making certain capabilities available they hadn't previously,
Speaker 3: by saying they would open source more technologies. So there's
Speaker 3: definitely been a significant response, and of course the public
Speaker 3: markets responded by having the first of a number of
Speaker 3: frighteninglaims melt there. Yeah, well, the first of many meltdowns
Speaker 3: that we've had so far this year. But I would
Speaker 3: say that the really interesting thing that has come out
Speaker 3: of out of deep Seek is that by being open
Speaker 3: source and being as good as it is, it's a
Speaker 3: real strategic challenge to closed source models that are only
Speaker 3: slightly better than an open source model, And so I
Speaker 3: do think that it has in some sense started to
Speaker 3: redraft our assumptions about how this industry might evolve for
Speaker 3: the economy over the next few years.
Speaker 1: Coming up, we'll hear more from Azimazar about our current
Speaker 1: AI moment. To stay with us. One of the kind
Speaker 1: of things you provide for your readers is, you know,
Speaker 1: information and first hand accounts of you using all these
Speaker 1: new technologies. The other thing you provide, I think is
Speaker 1: paradigms for thinking about problems.
Speaker 3: Right.
Speaker 1: One of those paradigms you have is innovation versus diffusion,
Speaker 1: Diffusion being kind of what happens after innovation, i e. Like,
Speaker 1: how does a technology actually get adopted in a real
Speaker 1: market or a real economy. Can you kind of explain
Speaker 1: that paradigm and how you're seeing it play out differently
Speaker 1: in the US versus China.
Speaker 3: Yeah, Well, it's very easy to get excited about the innovations,
Speaker 3: but what actually counts is do businesses use those innovations
Speaker 3: to increase their productivity, produce better products, reduce their costs,
Speaker 3: and therefore sort of kickstart that virtual circle that is
Speaker 3: a market so consumers can buy better products at lower costs,
Speaker 3: and that cycle continues. And the big question that we
Speaker 3: face around AI is what is going to be the
Speaker 3: rate of diffusion of the technology across different countries. And
Speaker 3: there are a couple of issues here. Sometimes if you
Speaker 3: aren't very advanced with your use of technology, you actually
Speaker 3: benefit a lot when a small amount of technology is
Speaker 3: introduced into the business. I mean, you know, the simple
Speaker 3: point being that the first TV that a family gets
Speaker 3: is life changing, the fourth TV doesn't make that much difference,
Speaker 3: and the same is true going to be true for AI.
Speaker 3: So how does this going to play out? US firms
Speaker 3: tend to be much much more pro technology. They take
Speaker 3: on technology earlier than companies in other countries. But one
Speaker 3: thing that happened with deep Seek was that deep Seek
Speaker 3: triggered a response from the Chinese state, both in a
Speaker 3: meeting that President g held where he brought lots of
Speaker 3: the big tech CEOs from AI and other domains together
Speaker 3: and started to rehabilitate them. But the second thing that
Speaker 3: I've heard is that there has been a wrong grass
Speaker 3: roots but also directed effort from local and state governments
Speaker 3: to start to use technologies like deep seek in their
Speaker 3: in their delivery, and one of the things the Chinese
Speaker 3: can do quite well is they can coordinate both the
Speaker 3: private and the public sector in that way. I think
Speaker 3: it's it's unclear to me that that necessarily helps them
Speaker 3: catch up with the US firms. Well, well, just the
Speaker 3: fact that I mean, just the fact that American firms
Speaker 3: in general tend to be very very pro technology, right,
Speaker 3: They're the first to move to the cloud, They're the
Speaker 3: first to move to mobile and mobile commerce. You know
Speaker 3: that they do it quicker than Europeans do, the French
Speaker 3: or the British, and in general quicker than the Chinese.
Speaker 3: But I would say that the fact that there is
Speaker 3: a Chinese model, the fact that there is a little
Speaker 3: bit of patriotism running around it, the fact that it
Speaker 3: is so easy and low cost to run and there
Speaker 3: are not so many alternatives, I think does suggest that
Speaker 3: the Chinese market could could accelerate right more quickly than
Speaker 3: it might otherwise have done. And we know, we have
Speaker 3: to see what happens over the next the next year
Speaker 3: or so, but I wouldn't be blase and say, well,
Speaker 3: America is obviously going to diffuse this technology faster than
Speaker 3: anyone else.
Speaker 1: And I believe it. South By Southwest, you were leading
Speaker 1: a panel about energy as it relates to AI, and
Speaker 1: obviously you know China's ability to onboard new electricity to
Speaker 1: the grid in the last twenty or thirty years, you know,
Speaker 1: with cold being a major part of that has been
Speaker 1: extraordinary compared to the US. How important of a driver
Speaker 1: of diffusion will energy production and integration be.
Speaker 3: I mean, all of the AI data centers that are
Speaker 3: going to be built will need lots of electricity. I mean,
Speaker 3: these chips are demanding. They are Just give you a
Speaker 3: sense of how demanding they are. The standard unit in
Speaker 3: a data center high density servers, which are these powerful computers,
Speaker 3: a high density racks that of today might draw twenty
Speaker 3: or thirty killer watts of power, and you'll have one hundreds,
Speaker 3: if not thousands of these racks in a big data center.
Speaker 3: And the new rats that are being designed will will
Speaker 3: have servers that will draw one hundred to one hundred
Speaker 3: and forty kilowatts through them, which is an enormous amount
Speaker 3: of All of that comes together to mean that in
Speaker 3: order to deliver AI at scale to any economy is
Speaker 3: going to require lots and lots of data centers. And
Speaker 3: back in twenty eighteen, data centers in the US took
Speaker 3: up about one point two percent of electricity demand. Coming
Speaker 3: into twenty twenty four, it's around four percent. The Department
Speaker 3: of Energy reckons that by the end of the decade
Speaker 3: that number will be between six point five and twelve
Speaker 3: ish percent, is which is quite quite significant. Now. The
Speaker 3: reason it's significant is that since two thousand and four,
Speaker 3: the US has not really increased the amount of electricity
Speaker 3: it's used, very very largely sort of underinvested in its grid,
Speaker 3: its energy generating capacity compared to China, which, as you say,
Speaker 3: has historically used coal, but now essentially everything that's brought
Speaker 3: on stream is solar. And so there is this concern
Speaker 3: that even if you've got the algorithms, and even if
Speaker 3: you put the algorithms in products, if you can't run
Speaker 3: those products and those algorithms on enough computers because you
Speaker 3: can't get the power to them, you can't serve businesses
Speaker 3: with their energy needs. And so that's been a major concern.
Speaker 3: And then that that comes into the second concern, which is, well,
Speaker 3: even if you can serve them with the energy needs,
Speaker 3: one are the environmental implications of all of that. So
Speaker 3: there is a sense that there's an amber warning light,
Speaker 3: perhaps not a red warning light, you know. My own
Speaker 3: sense of this is that it's actually a really good
Speaker 3: thing that there is a demand for new electricity sources
Speaker 3: coming into the US market after such a long period
Speaker 3: of low investment, because any advance economy is going to
Speaker 3: need electricity. So I think in general it's quite a
Speaker 3: good thing to have this strong demand signal come in
Speaker 3: from the AI data centers. But I think it does
Speaker 3: create a small risk, which is for want of a
Speaker 3: grid connection, the AI opportunity was lost, and there is
Speaker 3: that risk. It's one of the things that the new
Speaker 3: administration has to figure out what are the leaders it
Speaker 3: can pull to unblock US firm's ability to build and
Speaker 3: power these AI data centers.
Speaker 1: So, speaking of the new administration, there was a fascinating
Speaker 1: conversation that Ezra Cline had last week with Ben Buchanan,
Speaker 1: the kind of lead AI advisor to the old administration,
Speaker 1: which I'm sure you followed. The discussion centered around kind
Speaker 1: of AGI and whether it's coming, and in the context
Speaker 1: of that, there was a lot of discussion about competition
Speaker 1: with China. So on the first one, Where do you
Speaker 1: stand on the whole Will they won't be on AGI
Speaker 1: in the next couple of years?
Speaker 3: Well, let's start with what do people mean by AGI? Right?
Speaker 1: I think the definition of Kenan was using was basically
Speaker 1: doing most human tasks better than humans, like replacing disc
Speaker 1: workers was his kind of framework.
Speaker 3: Yes, that's sort of somewhere between where Demisi Savis who's
Speaker 3: the boss of Google's Deep Mind group, and Sam Altman,
Speaker 3: who's the boss of Open ai SIT. I mean Sam's
Speaker 3: phrases systems that outperform humans at most economically valuable work.
Speaker 3: By that definition, we're already getting systems that improve the
Speaker 3: quality of human work significantly, and we already have systems
Speaker 3: that achieve the same output with much smaller teams, because
Speaker 3: you know, answering support tickets is something that these chatbots
Speaker 3: can do very very well. If you look at the curves,
Speaker 3: which I mean the performance curves of AI systems, they
Speaker 3: are sharply trending upwards. Does that do all the work
Speaker 3: of a desk worker? So I slightly disagree with it
Speaker 3: because I still have to direct the machine, I still
Speaker 3: have to judge the output. I still have to use intuition.
Speaker 3: Things that I wasn't able to frame in my question
Speaker 3: with the results that comes out of it. Ultimately, I'm
Speaker 3: the principal who makes the decision in the business, so
Speaker 3: I look at them as tools that largely augment. But
Speaker 3: it's really also very very clear that there are lots
Speaker 3: of jobs where that the augmentation is going to turn
Speaker 3: into a replacement. And I think that you know, you
Speaker 3: see that happening in customer service teams, right you have
Speaker 3: teams of one hundred turns out with the AI, you
Speaker 3: can have a team of ten or a team of
Speaker 3: twenty that does the same job. So timing wise, I
Speaker 3: expect the rate of improvement of these systems to continue.
Speaker 3: I think what Mann has showed us where we started
Speaker 3: our conversation was that you don't need to build a
Speaker 3: new model to get a really, really great output and
Speaker 3: an improved output. And I sometimes wonder whether AI researchers
Speaker 3: think for the average human and the average desk job
Speaker 3: is at the level at which these double PhDs work out,
Speaker 3: and that's just not true. Right in most businesses, we're
Speaker 3: not thinking like that. If you could get a machine
Speaker 3: that can come up with the next new theory of physics,
Speaker 3: we all benefit. But in reality, we don't need that
Speaker 3: level of thinking most of the time, right. We actually
Speaker 3: need a much more prosaic level of thinking. And frankly,
Speaker 3: I'd much rather that my barber doesn't have a Nobel
Speaker 3: Prize in physics. I'd rather he's just very good with
Speaker 3: a razor blade.
Speaker 1: Thank you so much, as him my pleasure. That's it
Speaker 1: for this week for text of I'm as Volcan. This
Speaker 1: episode was produced by Eliza Dennis and Victoria Demingez. It
Speaker 1: was executive produced by me Kra Price and kat Osborne
Speaker 1: for Kaleidoscope and Katrin nor velve I Heart Podcasts. The
Speaker 1: Heath Fraser is our engineer and Elmurdoc mixed this episode
Speaker 1: and also wrote our theme song. Join us Wednesday for
Speaker 1: tech Stuff the Story when we'll share an in depth
Speaker 1: conversation with astro Teller, the captain of Moonshots at Google x.
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