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Chapter 1: What is the main topic discussed in this episode?
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Hello and welcome to another episode of the Odd Thoughts Podcast. I'm Tracy Alloway.
And I'm Joe Wadsenthal.
Joe, we like to talk a lot about physical constraints on this show, right? And this is one reason why AI is a really fascinating area for us right now, because there are a lot of physical constraints on what is ultimately the sort of ephemeral technology. And I think that the tension between those two things is really interesting, right? Like you type a prompt.
into ChatGPT or Claude or whatever, and it's this disembodied digital platform. You don't necessarily think about the power usage, the real resources, the transformers that have to go into data centers to get compute.
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Chapter 2: How does Anjney Midha define the current compute market?
I said yes, then we tried to get another 25 VCs to say yes, and I failed. It was a harrowing experience. It was a bit of a wake-up call. It was late 2020. I had just sold my last business. It was called Ubiquity 6. It was a 3D mapping business. It was an AI business that we had founded in 2017. And I felt like a failure at the time because I was in San Francisco.
Just as big picture, my life stories. I was born in India. I went to high school in Singapore. And I came out of college to the United States at Stanford for my undergraduate degree. And when I arrived at campus in 2011, deep learning had just started taking over the world in Silicon Valley.
Andre Karpathy was a computer science TA to Andrew Ng, who was one of the, I would say, modern sort of founding fathers of deep learning. This idea that you can teach machines to think and without having to give them prescriptive rules. I got swept up in that moment and started studying. A lot of my coursework was in machine learning.
My primary department at Stanford was in bioinformatics, which was machine learning applied to healthcare. I got sidetracked to a venture firm called Kleine Perkins for about four and a half years where I got the chance to work for some of the great investors like John Doerr and Mary Meeker. Then I left and started my own company.
And as is the case in Silicon Valley, when you start, I mean, I was 25. I went and raised about $47 or so million from some of the usual suspects, like benchmark and index and so on. And I thought I was the coolest kid in town. And I got the beat out of me because we built this incredible technology, which is this AI system that could map any location in 3D, and then the pandemic hit.
And so location-based mapping, 3D mapping, The only thing you can control is how you react to what happens. I did feel for a moment like it was bad luck, and then you just have to pick up the pieces and make the best of it. So I did with my co-founder. We figured it out. It was a tough... few years where we had to pivot the business. But we landed the plane.
Essentially, a lot of the distributed systems we'd built on the backend side ended up being quite valuable. We sold that to a company called Discord, which is a chat app for gamers.
We have an online Discord. Time to plug that. We chat with our fans on there.
Awesome. Our listeners. So, you know, about a month after I sold the business, I got a call from some friends who were running research at OpenAI. And we'd all been, you know, friends in the machine learning community in the Bay Area. And they said, Anj, you know, we've trained a little model called GPT-3 and we think it's the best since. Just a little model. Yeah. Nobody really paid attention.
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Chapter 3: What challenges do small labs face in compute utilization?
We tried to raise $500 million. We couldn't. We instead scraped together about $100 million, which I know sounds like a lot, but at the time it was a rounding error compared to how much Google has spent on the same kind of systems. And it was all angels in that first round, a bunch of cats and dogs, all of us who believed in the mission.
And then over the next 18 months, Dario, Tom, and team put together a plan that we kind of workshopped on getting Amazon involved as a strategic. And that resulted in a $4 billion compute and capital partnership that made me realize infrastructure, especially, you know, compute infrastructure was just a key requirement to create any kind of modern AI lab.
And so since then, I've spent the past five, six years figuring out how to unblock that compute bottleneck for research teams.
Amazing. Well, obviously, an incredibly well-timed.
It just, like, emphasizes how much things have changed, right? Where, like, people are literally throwing money at, like, almost any model now versus, like, a few years ago going, like, ah, AGI, like, I don't really know.
Right, right. Well, let me ask you this question because this is a very top of mind question for me. And we can skip around on the timeline here. But there are three labs that are seen as like genuinely at the frontier right now. And that is obviously DeepMind within Google, OpenAI and Anthropic.
And then, of course, you know, a lot of people say that the Chinese labs are very close, if not quite there. Maybe they're a few months behind. Is this, is there, you know, when we think about like part of your mission is like you say, okay, a new lab should be able to get access to compute. If you're really bright, like that shouldn't be the bottleneck.
Does that imply, therefore, that you expect more labs to be able to, were they to have access to the compute, also reach the frontier and that there is something inherent about like this sort of seeming stability or parity that we see among frontier models?
So the answer to your first question is yes, there are many frontiers to be conquered and pioneered. And it's not just one frontier. I think that's a fundamental misunderstanding people have about the frontier.
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Chapter 4: How does AMP PBC aim to standardize compute resources?
I mean, there were so many new businesses founded in the industrial revolution. And I think that's the reality is the software engineering frontier, which is where Anthropic is clearly leader, is one frontier. I think the chat frontier, the sort of consumer chat frontier is another frontier where OpenAI has been a leader.
Arguably, ByteDance is at the video frontier with SeedDance, right? Absolutely, yeah.
And so I think there's just many, many frontiers to be conquered, or pioneered, rather. I think Anthropic is clearly a role model for the rest of the community on how to do it in an efficient way. There are, I think, fewer than 5,000 people, and they've been able to put out state-of-the-art models that teams like Google, which have 60,000 people, are close to, but not yet quite there.
So actually, I don't really agree with your assessment that they're all at parity. If you use the models day in and day out, they're quite remarkably different in meaningful ways to the person with hands on the keyboard doing the engineering work. And I think those differences reflect the focus of the teams, right?
What is the actual mission that the team working on that domain cares about day after day after day? So in the Stanford class I teach, the first lecture was a breakdown of how frontier models are even created. And it's actually quite simple. The recipe is super simple. There's basically four steps.
There's pre-training, mid-training, post-training, and then what we call the continuous feedback loop. So pre-training just says, hey, you collect a bunch of data from the internet and train a model to be a generally good pattern recognition machine. You then do mid-training, which is to say, in a particular domain that you really care about, you inject more capabilities.
So if you want this model to reason about science or math or physics, then you give it science or math or physics data. And then you get a pretty good model that's specialized in that domain. And then you deploy it to the real world where you have people using it. And...
The context feedback, which is when the model is able to do a task well or not and you can verify whether that task was done correctly, gives the model the data it needs to keep improving on that task, on that distribution.
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Chapter 5: What is the significance of compute grid technology?
In the case of another lab I incubated called Periodic Labs, which we started a year ago, and you should come by sometime. We've got 40,000 square feet in Menlo Park where we've got AI models that are predicting new... The goal is to try to find a room temperature superconductor. And so these models predict... The summer of room temperature superconductors. I forgot about that.
That was a fun summer.
Yes. This time we will verify. If we ever put something out, you will know it's not real. That's not going to be us. But the AI system predicts new materials candidates. Then we have robots that synthesize the new material in the lab and then use X-ray diffraction machines to test whether the material has the properties the AI said it would.
And that's verifiable feedback from reality, from physics. And then we pipe that data back into the training loop over and over again. That context feedback is very factually verifiable. And that's where progress is the fastest today, because that feedback doesn't result in the kind of hallucinations that you often experience with these models on more subjective tasks.
It's also, by the way, why the models are terrible at subjective tasks, like creative writing. And sometimes it can get quite toxic, to be honest, if you get them down the wrong loop. I don't know if you've been using it as a therapy bot and so on.
I have not, just for the record.
That's great.
It did ask me to defy the laws of gravity at one point because I was trying to create something in my backyard and I was asking it how to do it. And it was like, then just set this up like the following way. And I was like, that's not within the laws of physics. Yeah, yeah. Whatever.
No, go ahead.
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Chapter 6: How does the software solution improve compute efficiency?
And consequently, wherever that progress, the workflows are not verifiable, is actually where humans are going to shine. And I think that's where parts of the economy, you're going to see extraordinary gains in the wages of humans who have creativity and craft that are not typically verifiable through traditional objective means. Does that make sense?
Yeah, it does. And it dovetails with a lot of what we've been talking about on the show recently. Just going back to verifiable feedback. So, okay, the model spits out something and you can check whether it's right or wrong. Is it important to understand how the model actually got to that answer?
Because we have discussions with like big bank CEOs who are using more AI and their response to this question is always like, well, if we can put restrictions around the AI, if we make sure that it's like released into a sandbox before it's released into the wider world,
We're all set from a regulatory perspective, and regulators don't actually need to know what's in the black box model and how it's working. But this seems a bit concerning to me.
Yeah. No, I am quite strongly opinionated about this one, which is that technical literacy should be non-negotiable. It's the reason I spend so much time teaching this class at Stanford, putting it up online. And the idea of the Frontier Systems class is that end-to-end, it's a full...
simple, but first principles breakdown of how these AI systems are built from scratch, from land, power, shell, like the energy, where do we get them, the data centers, then how do we train the models? And the final project, the class with the kids was actually the one person frontier lab, which is at the end, they're creating their own models and so on.
Because the idea is that a person with the right tools today can scale themselves infinitely, but they need to know how to use the tools, what the limitations are, when to lean on them versus not. And I think this is a generalizable piece of technical literacy that all leaders should have.
It's like saying, you know, I, in the 90s, I imagine if you knew, you could use the internet without really knowing how it worked. But, you know, on the margins when like the page doesn't like refresh or you're like this, this cookie thing is annoying me. Like over time, people who are more technically literate just realized sometimes you got to debug something. you know, the browser.
And those of us who've learned over time to do knowledge work are more adept at leaning on them versus not. Like just now when I was trying to get onto the internet, I realized, okay, there's this, you know, wifi password, whatever. And then you don't end up Relying on them in ways that they can't fulfill your need anyway.
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Chapter 7: What are the economic implications of the current compute model?
Does that make sense?
Absolutely. Let's talk about AMP. Yes. And because you're never going to get the frontier in anything unless you have access to compute. It seems pretty obvious. And there are various arrangements for acquiring compute. You have companies building their own data centers. You have smaller labs, and maybe they use someone else's data centers or a NeoCloud, et cetera.
What are you building at AMP such that at least as part of this story is trying to solve the compute bottleneck specifically?
Right. Yeah. It's very simple what we're doing at AMP. We're doing two things. We are trying to standardize the format for compute, which today is super fragmented. So in the history of infrastructure, if you look at whether it was the industrial revolution, the internet, streaming, there were usually formats of inputs that were quite heterogeneous. They were fragmented. And then to unlock
productivity, you had to standardize a format. So in the case of electricity, until ACDC was standardized, megawatts would just sit in stranded pockets around the United States being unused. And then once we standardized the format to ACDC, Then the question was, okay, great. Now we turned all these stranded pockets of electricity into one sort of interoperable universal format.
Now how do we distribute it to everybody who needs it? And we came up with this distribution layer in the United States called the grid. That's all we're doing. Yeah.
You're building a grid for compute.
Correct. We're trying to standardize the compute layer today. Different chip types, different manufacturers, different clouds. I mean, it's a complete mess. Go ahead.
Say more about how we plan to do this, because we've talked before about, you know, there are various people out there that want to create indices of compute, futures potentially on compute. And the issue that always comes up is fungibility.
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Chapter 8: What future trends can we expect in the AI compute landscape?
Today, the average data center in the industry, in the ecosystem, in the independent ecosystem, is running at less than 70% utilization. The Colossus 2, which is running in Memphis, Elon's 500,000 GB, 500,000 GB 300s was running at less than 60% node utilization and less than 11% MFU, model flop utilization is how much of the chip is actually being used.
So there's two kinds of utilization people care about in the data center. First is how many chips are being used? That's just the most naive measure. If that number is not at 90 plus percent, no excuses. So you have the chips. They should at least be doing something. And then within the chip, how much of the chip is being used within a workload, that number is usually much lower.
I'm very intrigued by this latter point about that even like the chip itself may not be even used at full capacity. Because I see these numbers and you say like a lab has like a It was like, we have 200 chips. We've acquired 800 GPUs, et cetera.
And when I see these headlines, I assumed that optimal utilization techniques must be so good that you can infer someone's capabilities simply by how many NVIDIA GPUs they've acquired. No. What you're saying is that there is actually quite a bit of heterogeneity about the techniques and approaches to getting the most juice out of ad chip.
Yes. You have to measure what matters. And what matters is output. OK. Any time I start a new lab, in the case of Periodic Labs, we started with Liam Fetis, who was the co-creator of ChatGPT, and Dovish Chubok, who led the physics teams at DeepMind. And when we sat down and we planned out the company's roadmap, the most important thing to us to measure was not the number of chips we had.
Yeah. It's the eval, what we call- So all this chip bragging, they're like, oh, we acquired, it's just a sort of-
It's a lot of bravado.
Yeah. All right. This is helpful.
You don't measure the inputs. You should be measuring the outputs.
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