YouSaid · the spoken record

Anjney Midha

lines on the record: 59
first: 2026-06-13
most recent: 2026-06-13
sittings or episodes: 1
sources: podcast

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“So now you don't need to use a third party spreadsheet. And so then the harness gets updated to say don't go out and use a third party spreadsheet, which, by the way, collapses the time required to do that task by sometimes a minute to two minutes. Now suddenly I've improved the user experience. And that's when things really sing. It's when both of those parts, the model and the harness, are co-designed to create a symphony. Does that make sense? Yeah.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Once you have that visibility, you go aha, we specifically want to improve the capabilities on this type of task. It's going to take us about three months to get there. Start designing the harness for that improvement. By the time they show up, then you can have the harness assume that the model will be able to do x, y, z on its own, whereas ABC, it's going to need third-party tools. So then the harness says, remember that three months ago you were terrible at understanding a spreadsheet. So then we had to go right a third party tool to use a spreadsheet. In the last three months, what we've done is added the ability to actually reason about a spreadsheet in the model.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“I'm sorry, I have to correct you there. It was not just a harness innovation. Those two things go hand in hand. It's a symphony of improvement between it's a dialectic between the model capability and the harness. That harness was designed specifically for the capabilities that the new model was going to have. And so when you design these things in the industry, we call this co-design. So you have the harness designed side by side with the researcher who's designing the next generation capabilities in the model. And you get a little bit of visibility in where the model is going to be good because as I described earlier, the pipeline is actually quite predictable. Pre-training, mid-training, continuous feedback loop.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“If you look at the compute prices of long term rentals over the last six months So we started, for example, for 2026, we started securing our capacity in January at these long-term rates. We could resell that at a 2x markup if we wanted to.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“I'm a big fan of efficient markets and I'm trying to actively invest in and help entrepreneurs out and teams out who are trying to drive more efficiency in the service of more productivity in science and engineering. I'm not that thrilled about the financialization of these products if it ultimately results in more speculation. Does that make sense?”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“I hope not because when you had speculation to production goods, it creates scarcity of a different kind, right? Because then you have financial traders and markets trying to trade the speculative value of the asset. And that's going to hurt a lot of our research teams in technology. On the other hand, I think that creates a need for innovation inside of the research teams. And so one of the core operating functions we have inside of our business is a forecasting capability where we have a team that's very similar to actually the kind of forecasting team you'd have inside of a hedge fund. We're constantly predicting demand and supply. And then we're actually procuring capacity in advance through call options on compute clusters. But our needs are similar to the kind of internal trading desk you'd have inside of a large steel company, right? Where they need to lock up iron ore and so on for their production needs.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“But that's the bifurcation leaders who are actually trying the tools out. They realize they're extraordinary at some things and not at others. And so depending on whether you get it or not or you're actually getting your hands dirty or not, I find the questions completely different.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Trying to get at it's the summer, right? We have two nieces in London and we call it Camp Mirashen. My last name is Mira. My wife's name is Shen. And so you're welcome to send them to Camp Mirashen anytime. That's amazing.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“That just might be a generalizable, that's really the only point I'm trying to make. I see. Okay. Well, you can send them over to me anytime. I'm happy to be the fun uncle. Yeah.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so that's the other thing, right? So the kids are super smart and they're using the tools and they're like, it's good at this thing, but not at that. So they understand the jagged frontier part. Actually, you don't.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, I think it's a barbell distribution. So there's two types of CEOs, broadly speaking. The first is the CEOs who are using the tools themselves. And those folks are going, aha, I understand the jagged frontier. When they understand the Jagged Frontier we talked about, their strategies, their questions they ask me are completely different from the CEOs who are outsourcing their understanding. They're not trying the tools. They're mostly asking their kids like, hey kiddo, this chat GPT thing, it's good, right? And your kid is like. Yeah, it's pretty good, Dad. And then you're going. Kids”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Can you guys please figure out how to get the work done in the cheapest way, in the most efficient way, in the most secure and trusted way? And increasingly what you'll find is that which particular model is helping you out in a particular task will just be abstracted. You won't even think about that. It'll just be a companion. You're just going to talk to it. It'll be a companion provided by a brand you trust. And under the hood, they might be using 200 different models to orchestrate your task. And over time, that efficiency will get better and better and better and better. And that's why I just don't think there's only three frontier models that are going to win. It's going to be an ecosystem.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Which Oh, yeah, we're absolutely in the medieval ages of this technology. I think what will happen is increasingly, based on my conversations with corporate American leaders and corporate leaders across the world, they don't really care about the models. They don't care about the underlying model, the technologies. They just don't care. It's like too much complexity. We just want the work done”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Except when, like. There's a heat wave, exactly. So, some of that infrastructure we are having to reboot. But you can think about AMP in the broadest sense as a utility company, where what's called an independent system operator of the grid. So we don't own our own data centers. We don't own our own labs, but we coordinate the capacity needs across different parties. And at sufficient scale, those usage patterns actually just gets evened out. Does that make sense? Yeah.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Or something like that Okay, so there's an embedded assumption I think I should tease apart in your question. Usage is different from the production of the model. So what's happening, right, in terms of the pipeline is you use the grid to produce these systems, the model, and then the model produces tokens. If the end user is only using tokens, then as long as everybody, we have enough diversity in the end user base using models hosted on the grid, things actually even out. That cyclicality in the same way electricity in America evens out. If you have enough scale at scale, basically.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“That's not what I'm saying. What I'm saying is in inference, for example Satcha would like more control over his unit economics, so he's making an inference chip. Because if you are dependent on a third party to give you the inference chip. And if you don't have an inference chip, you can't sell more product. You want more”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“On the technical side, the primary reason is you want control over your supply chain. Because today, in a compute, well, we've been in a compute constrained world now for at least four or five years. But if you can't get the chips you need, you're not in control of your own supply chain. So your dependent on compute allocations that the compute manufacturer thinks is optimal, right? By the way, that's how it works at the foundry level. Today, TSMC gets to decide which compute provider's business grows or not. Because they only have so much production capacity. And so the technological reason is you want supply chain independence. And so when you want economic independence, unit economic independence, anyone's supply chain independence, you want as much control over your own chip.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I think that the better our software gets, the more that margin should flow actually to the researcher, because that's where the value will be captured”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, there's two technological reasons and two economic reasons. The first is from an economic perspective, about 80 cents of every dollar our lab spends today on their R&D close to a chip provider like Nvidia. And so as a result, your margins are just super, super rough. So from a unit economic perspective, you want more control over your margins. And therefore, when you look at your unit economics, you're going, wait, wait a minute, for every dollar we make, there's this massive chunk that's going to somebody else.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Or is it both? Both. Yeah. So the beauty about having diverse types of compute. On our grid is that once you make the resource fungible, you can do any workload. You just fill all the unutilized pockets with inference and then all the reservations with training. Got it.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“No matter what format it is, it might be NVIDIA, it might be MD, we love AMD, it might be some other chip, and we turned it into one fungible resource. And we standardize the format on something we call grid credits. So researchers don't even need to think about what chip type is under the hood. They're just paying what they need or what they use. And so from a fiduciary perspective, I'm on seven boards. As an investor, I get very excited when teams switch from this sort of long-term lease model where they're paying $25, $26 per GPU hour. So now they're actually only paying the $2.50 that was marketed because everything they're not using gets reallocated to the grid and other research labs can use that resource.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“So that spread due to wastage is just insane. So from an economic perspective, that's the wastage. That's the deadweight loss. Okay. So now how do we, from a technological perspective, how do we utilize that opportunity? Literally, all we do is from a software perspective, we take all of that unutilized compute.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“It's hard to forecast. So you over provision for your beak, not your baseload, because what happens, you're researching on these algorithms and the minute one is working, you go, guys, let's scale. We want to ship this thing. So let's improve, throw as many chips at it, and then once we ship it, the needs go down. So between these spikes, there's just huge pockets of unused compute. As a result, the effective price per hour that you're paying is closer to $25 to $28. Whereas the marketed rate that you think you're paying is $2.50.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Now, what happens with that compute, whether it's used or not, is the researchers problem. They've outsourced that problem. A result of this wastage that we're talking about. And I'm happy to go into why it's hard for individual teams to utilize most of the capacity. The primary reason it's because research is spiky.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. Yeah, so let me give you the technological answer and the economic answer. The economic answer actually is a simpler one to reason about. The way the compute business works today is primarily on the construct of the atomic unit of long-term leases. So I'm a researcher. I need some compute. I show up to a compute provider and say, hello, I would like some compute, please. And the compute provider says, no problem. Here's 500 AMD chips or NVIDIA chips that you can lease from me on various time scales. And you've got to pay for it 24-7. It's like leasing an apartment. And whether you use it or not, that's your problem, but it's $2.50 per hour, $3 an hour. So instead, you take a long-term lease. And now the cloud provider, the compute provider said, great, I just booked revenue for the next two years that this guy rented from.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Anytime I start a new lab, in the case of periodic labs, we started with Liam Fettis, who is the co-creator of ChatGPT and Doge Chubuk, 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. The eval, what we call”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Today, the average data center in the industry, in the ecosystem, in the independent ecosystem, is running at less than 70% utilization, Colossus 2, which is running in Memphis, Elon's 500,000 GB300s, was running at less than 60% node utilization and less than 11% MFU. MF model flop utilization is how much of the chip is actually being used. So there's two kinds of utilization people care about in a data center. First is how many chips are being used The most naive measure. If that number is not a 90 plus percent, no excuses. So you have the chips. They should at least be doing something. And then within the chip during how much of the chip is being used within a workload, that number is usually much lower.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“And it's a pretty gnarly challenge, but today we're able to do that in ways that improve utilization sometimes from 50, 60% at labs that we have incubated on the grid to close to 95, 96%. At Google, the utilization is roughly 99%. When Sebastian arrived at Google, it was about 62%. By the time he left, it was roughly at 99%. At Google, if utilization is at 96%, that's considered a major outage.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, exactly. So we've got a couple ways we solve the fungibility problem. This is a pretty thorny challenge. We solve it in two or three ways. The first is we have a system called the grid, which actually makes the compute fungible at a consumption layer. So under the hood, we have a bunch of different chip types. We support various different manufacturers. And there's a system that was built to do this already inside a little company called Google. And one of the technical leads on that project was called Borg, internally at Google, is my co-founder, Sebastian Lobo. He was my roommate at Stanford 14 years ago. He's my engineering co-founder. And we're building Borg for everybody else, which is essentially a translation layer that says no matter what the underlying chip type is, the machine learning researcher who's using the chip just has to worry about the workload and we handle everything else underneath the hood.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Correct. We're standardizing. We're trying to standardize the compute layer. Today, different chip types, different manufacturers, different clouds. I mean, it's a complete mess. And if you're going.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Megawatts would just sit in stranded pockets around the United States being unused. And then once we standardize 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. So for competition.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“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”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Or relying too much on it. It's the You can't outsource your understanding to a model. You can outsource your thinking. You can outsource part of the tedious workflows, but you can't outsource your understanding. And if you keep saying, if you create these simplistic frameworks of, oh, here's a sandbox and this is safe, you have to use that sandbox in the right way. Because if you say, well, now everything that happens in the sandbox is totally fine. If the model says use the spreadsheet, the spreadsheet is good. It's deployed in our servers, but you didn't actually check the spreadsheet and what went into the spreadsheet. And did the model actually understand the particular structure of the business, the physics of the business that you're trying to model out, then you've outsourced your understanding to it. Does that make sense?”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Kind of breathtaking. So I think that technical literacy should always, for leaders, be a basic requirement. And then if you're deploying these systems at Goldman Sachs, you won't oversimplify and get tripped up later when two years later you realize half your employee base has been leaning on this sandbox framing, when in reality inside the sandbox, they were doing all kinds of, they were using the tools in ways that were prone to hallucination, prone to risks, prompt injection. They were leaning on it in ways that were not informed in the appropriate ways. Is this making sense? Yes, like Eddie.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Without the technical literacy that I wish all leaders had about reasoning about how these systems were built, what you end up doing is projecting out in your mind what the capabilities are in ways that are inaccurate. You project out their impact on society that are not accurate. You project out their business models in a way that are not accurate. I mean, the very fact that when you started this conversation, I don't blame you for it. You're like, there's three models at the frontier. I'm like, well, which frontier? And which models? Because from where I'm sitting, there's like 17 different frontiers right now and there's four different players in each one. And the businesses of all of them are”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“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 the page doesn't refresh or you're like this cookie thing is annoying me, like over time people who are more technically literate just realized sometimes you got to debug 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 Wi-Fi password, whatever. And then you don't end up relying on them in”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“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 them models? And the final project of 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,”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Fact checking absolutely. So, fact checking that's an example where I think we should be leaning on these tools and you should expect more progress. And the parts then that will be more to borrow your jagged frontier framing there, we will be in a regime of jagged frontier progress wherever parts of workflows that are Verifiable factually will essentially you'll see progress there very predictably over the next few years. 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 are, 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.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, exactly. And in journalism, I think it's the same thing. There's so much craft that goes into the verification of a story before it goes out that's not legible to the world. I've had a chance to spend some time with some of the journalistic institutions of the barrier like Kate Metz or Brad Olsen at the journal. And as you spend time with them, you realize, I mean, they're verifying every sentence that goes into each.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“This is a great point. So, where progress will be made most predictably is in parts of knowledge work where the task is essentially a workflow that's fairly structured. And so somebody who spends most of their day inputting cells into an Excel spreadsheet, well, that part of the job will get automated pretty fast because that's actually verifiable. And you know what? That's frankly often the most tedious part of the job anyway. And so I'm quite excited to see that progress because I'm terrible at spreadsheets. And I think if we could free up more of my time and hopefully other people's time to focus on the art of the spreadsheet, not the tedious part of it.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Before that piece of code usually gets deployed to a production system, you have unit tests, and those are quite objective tests of is this code performing the function we need it to? And if it passes both those tests, it's a verifiable piece of code that accomplished the goal. So in software engineering, the reason we've seen such a dramatic improvement in capabilities is that a lot of these labs are using feedback from that verification loop 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.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a great question. So let's take reason by example in two or three cases. In the case of software engineering, in the way software engineer is actually code is you write a piece of code and then you submit it to the main code base and then you usually have a peer on your team review the code and approve it or reject it. And if it gets approved, that's the first step. That's called a PR, a pull request. And if another human on your team that you trust approved it, that's one kind of verification of quality. And then two,”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Correct. So when I say feedback, I'm in a very specific kind of feedback, which I call verifiable feedback. So when you say that wasn't right or that was wrong, that's an opinion.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“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, you know, 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.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely, yeah, you know, 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. 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”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly, jacket intelligence, right? In a poetic sense and historical sense, if you think about the Wild West or the Western frontier, it wasn't just one frontier. There was a frontier of gold and there was a frontier of genes. It turns out Levi's, you know, turned out to be a new modern behemoth of a company. 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 way 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.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source
“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 your frontier. They talk about the jagged front.”
2026-06-13 · Odd Lots · Anjney Midha's Plan to Radically Lower the Price of Compute · IDENTIFIED FROM THE TRANSCRIPT · source