Travis Good: Machine Intelligence as a new world currency: facing down OpenAI with Ambient, a hyperscaled decentralized PoW-powered alternative

Join Tom Shaughnessy as he hosts Travis Good, CEO and co-founder of Ambient, for a deep dive into the world's first useful proof-of-work blockchain powered by AI. Fresh out of stealth, Ambient reimagines the intersection of crypto and AI by creating a decentralized network where mining secures the chain through verified AI inference on a 600B+ parameter model.

🎯 Key Highlights

▸ Ambient's vision for a decentralized currency representing a unit of machine intelligence

▸ How mining incentivizes global participation in a single model's continuous improvement

▸ The power of reasoning traces and synthetic data to create a "world's RL gym"

▸ Verified inference as the foundation for trustless AI composability

▸ The philosophical importance of open, decentralized AI vs. corporate controlled models

▸ How the economics of ambient create new possibilities for an AI-driven world

▸ Deep analysis of the geopolitical shifts in AI development with Chinese open source models

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🧠 Follow the Alpha

▸ Tom's Twitter: @Shaughnessy119

▸ Travis's Twitter: @IridiumEagle

▸ Ambient's Twitter: @ambient_xyz

🔗 Connect with Delphi

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Spotify: https://open.spotify.com/show/62PR1RigLG2YN5Pelq6UY9?si=18ac7ccf36ab4753

Apple Podcasts: https://podcasts.apple.com/us/podcast/the-delphi-podcast/id1438148082

Youtube: https://www.youtube.com/channel/UC9Yy99ZlQIX9-PdG_xHj43Q

Timestamps

00:00 - Introduction to Ambient

02:00 - The Vision: Currency for Machine Intelligence

05:00 - Convergence of AI Models and Democratizing Access

08:45 - Mining with GPUs to Improve the Network's Model

11:30 - The Power of Reasoning Traces and Synthetic Data

16:45 - Size Matters: High IQ Models and Fine-Tuning

23:15 - Speed and Performance: Building on Solana's Foundation

27:30 - Supporting Different Hardware Configurations

34:20 - Network Governance and Model Upgrades

38:00 - The Importance of Verified Inference

43:40 - The Problems with Centralized AI Models

53:00 - The Vision for an Ambient-Powered Economy

59:30 - The Future of Open Source Models

01:04:15 - Response to Leopold's "Situational Awareness"

01:13:30 - Privacy and Transaction Design

01:19:00 - Denominating Real Business in Ambient

01:25:00 - The Path Forward for Decentralized AI

Disclaimer

This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token.

Transcript

SPEAKER_00 0:00

You're now plugged into the Delphi podcast.

SPEAKER_01 0:04

Hey everyone, it's Tommy from Delphi Ventures. Welcome back to the show. Today I'm extremely excited to host Travis from Ambient yet again. It's Travis's third time on the show. Travis, how are you?

SPEAKER_00 0:16

I am great, uh Tommy. Thank you. Uh just uh out here in sunny San Francisco uh doing the uh crypto startup accelerator uh with uh A16Z. Uh so intense and a lot of a lot of fun.

SPEAKER_01 0:31

Travis, I'm so glad that we could finally chat now that Ambient is out of stealth. Um we're happy to to have co-led your your pre-seed, and at the time we couldn't talk about it. Um I need the one-liner on Ambient. It is so good. Tell me what tell everyone what Ambient is.

SPEAKER_00 0:48

Uh well, uh Ambient is a useful proof of work uh L1, uh, where the proof of work is verified inference, fine-tuning, and pre-training on a single large language model that runs on every node uh of its network. It is a fork of Solana that converts Solana uh from proof of state to proof of work and operates at full Solana speeds.

SPEAKER_01 1:14

It's insane. We're gonna get into all of this, but before let's let's walk backward. What is the end goal of Ambient? Are you building an are you building an L1 to build an L1? Are you building an AI model to build an AI model? What's the end result?

SPEAKER_00 1:28

Yeah, uh well, I think this uh this gets into the nature of uh crypto and currencies. Uh so uh, you know, if you look at uh at Bitcoin, you know, uh Bitcoin is a magnificent project uh that achieved one of its goals really well and did not achieve another one of its goals. Uh so Bitcoin's original goal uh was to be a currency. You know, it's something that we wanted to be able to uh pay for pizza for, pay uh, you know, uh pay for pizza with and uh transact with. And uh, you know, if we had achieved that goal, I think it would have reordered the world in a really healthy way. Uh it would have decentralized uh a lot of the power structures uh that I think are uh grinding against each other uh today. Uh and uh it would have been wonderful. Uh but uh you know, in the face of its success as a store of value, um Bitcoin pivoted away uh from being a currency. And today it is denominated in dollars, uh, which I which I think is really sad, actually. Uh you know, it's an abstraction on top of an abstraction. Uh and uh, you know, ambient we want to go back uh to the beginning, and we think that we have an opportunity to do that because the world economy is changing so profoundly. You know, we're moving towards a world where there's going to be more AI agents doing work uh than people. And uh, you know, those uh AI agents are powered uh by inference, um, by machine learning. Uh and that's their cost basis. And so in that world, uh it makes sense uh to have a currency uh that represents a unit of machine intelligence, and that is what Ambient is seeking uh to provide uh to the world. We want to be this unit of intelligence that can uh be usefully applied in any sector of the economy, in any nation of the world, uh, because every nation has adopted AI and AI agents to do useful work. And we think that uh, you know, if uh AI is the work that we're gonna do in the future economy, uh it makes sense to use AI to secure a blockchain. If we're devoting all of the world's resources towards running more and more models uh faster, uh, you know, and they're not that many models, but uh, you know, the big ones uh are gonna do a lot of the real work, uh, then uh it makes sense to take advantage of that supply of hardware uh to secure a blockchain. Uh and that's the the goal of ambient, it's to provide this currency, but also a store of value that reflects what's going on in the world, uh, which is that the AI-dominated economy, the AI portion of the economy is growing and growing and growing. Uh and that's why you know something like Ambient becomes more valuable uh over time, uh, because uh you know that uh that supply is becoming uh more and more value valuable and more and more prevalent.

SPEAKER_01 5:05

Travis, that's it's awesome to hear. It is sad when you bring up that we denominate Bitcoin the way we do uh after all these years, but focusing a bit on Ambient, what exactly does that mean? Right, like I hold your token. What you know, with Bitcoin, I have there's many different ways to splice it. The easiest is, you know, I have $82,500 uh this horrible night because Trump likes to be unhinged. But um, what does it mean to hold Ambient's token? I have access to a model or a blockchain or inference. Like, like what do I have access to?

SPEAKER_00 5:37

That's a great question. And I I want to take a step back and uh you know talk about something that's been happening in the world of models, and I'm gonna relate it to your question. Uh and uh what's been happening in the world of models is a convergence of capabilities. Uh so we've seen closed source models and open weights models uh start to achieve a very similar outcomes in most meaningful problem-solving domains. And our thesis is that that standardization and convergence is going to continue because the economic incentives to do that are simply too strong. Uh, like all these players uh need to try and balance each other out or they're gonna be left behind. Uh and I think China has discovered, maybe to the US economy's detriment, uh, that they can achieve a sort of geopolitical balancing of the scales uh by releasing open weights models. And I think other nations are gonna do that. Um but what that means is when you have these uh convergent capabilities, uh, you can pick one large model and get most of the benefit of machine intelligence. Uh and uh you know, in Ambient's case, going back to your question, uh what we do is we say uh we are offering uh this large model to you. Uh it's going to be the highest scale delivery of this uh such that uh you're getting the cheapest possible cost uh for verified inference at the fastest possible speed. Uh and that value proposition is represented by a token of ambient. It is the world's commonly defined capability for machine intelligence uh that's manifested in a token.

SPEAKER_01 7:29

It's it's crazy to hear out loud because if I own open AI stock, I have no access to the models of the inference. If I own if I own ambient, it's totally different.

SPEAKER_00 7:40

That's right. And I think that it has the power to be a hugely democratizing force. Um because you know, our belief is that um people should have ownership uh in this phenomenon, and it shouldn't be through uh proxies. Like you should be able to own it directly. Uh you know, uh OpenAI and all of these other uh closed source providers are very dangerous choke points uh for a lot of uh uh you know phenomena. Uh authoritarianism, uh censorship, uh propaganda, uh and uh we think that uh no one should have that kind of power over the world. Uh that it's uh it's like handing, you know, no matter what you think of Sam Altman, uh it's like uh handing him uh the the one ring, uh giving him that kind of power. Sort of yeah, it's just it's just corrupting.

SPEAKER_01 8:50

It it's not even him, right? It's it I don't know of a person that you can give that to that it wouldn't corrupt, right? That's right.

SPEAKER_00 8:57

It could be me, you, it could be anyone. And so, you know, with it's exactly right. And so with ambient, uh, you know, we want to leverage uh the design, uh the decentralized design of crypto to create a natural balance of powers uh that keep bad things from happening uh and keep abuse uh from happening. Uh and that's you know, that's the beauty of crypto. Like you can you can do that. That was the wonderful technical innovation of Bitcoin uh that uh you could motivate uh the creation of strong emergent communities just with the proper decentralized incentivization mechanisms. Uh and uh we want to be a counterbalance uh to these other sort of, I guess you could call them oligarchical forces in the world that I think are slowing humanity down.

SPEAKER_01 9:55

So, Travis, I know we're gonna jump around a lot from mining to inference to the models. I want to spend a little bit more time on the mining side. So uh John Gurl, one of my colleagues who you know very well, he had a tweet about your announcement, and one of his bullets was Ambient will incentivize miners from all over the world to power a single model showcasing the power of crypto and coordinating the much needed compute that AI demands. There's a lot to go into there, but maybe one of my more specific questions is if you are successful in convincing GPUs around the world to mine and secure ambient, it's it's become so nuanced how DeepSeek versus OpenAI versus others train these models and make them better, right? Like why will getting more GPUs make your end product, this model, uh, better?

SPEAKER_00 10:43

Yes. Uh so there's a lot to unpack there.

SPEAKER_01 10:46

Um I may have asked the question wrong too. So feel free to take it.

SPEAKER_00 10:50

No, no, it's totally fine. So let's start there, and then there are a number of other related things to talk about. And so I think you make a great point, uh, which is that uh, you know, Ambient is starting with something that has been provided in the world. Uh, you know, we might start with Deep Seek R1, uh, or it might be R2, or it might be Llama. It's whatever the state-of-the-art open weights model is at the point that we do our test net and our mainnet, that's gonna be the model. Uh and we think that model is gonna be you know 600 billion parameters plus uh because that's what's providing uh the really high intelligence and value uh for people. And we could talk about other things. But you know, the question is like uh what what's going on um you know with with Ambient in the meantime? Like what's uh and what value is Ambient itself adding? Uh and I I would start with uh you know something simple, uh, which is that a distinction between you know just an open weights model release and a model that's really kept up to date. Uh so you know, you as a Power AI user, you've experienced like it's really nice when GPT-4.0 comes out with a new release. Yeah, the knowledge is fresh. Uh like it doesn't give you three-year-old Python library references. Um, you know, uh like you're probably questions you've asked on the internet have made their way into the training set and all of a sudden it has answers for them. Uh, you know, uh there's a combination of uh memorization and genuine innovation that happens uh that you miss actually uh with a lot of the open weights models because they don't have a steady uh release cadence. Uh and so one of the things that Ambient as a network is designed to do is to take that base model and steadily improve it in a few different ways. Uh so one way is to continuously fine-tune it uh with up-to-date knowledge and provide those fine-tunes on the network. Uh, so that, hey, like if you just want to do logic problems on the base model, that's totally fine. Uh, but it's pretty easy to flip a switch, uh, have a hundred uh megabyte LoRa update uh that gets you into a fine-tune with all the financial news uh on the market, uh, for example. And because this is a proof-of-work blockchain, like we can build into the program of the system uh activities that keep the system's knowledge up to date. Uh so that's one thing. Uh, but then another like really exciting thing is uh related to the Deep Seek paradigm. Uh so you know, for those who who don't know, uh Deep Seek uh pioneered this virtuous uh circle, I like to call it, where uh you use reinforcement learning on an already good base uh model, and then it produces a bunch of synthetic data uh that is really useful for training the next base model. Uh and so uh Ambient, because it is a proof-of-work network and is continuously churning, uh, can devote a portion of the network capacity to generating huge synthetic data sets with reasoning traces uh that can be used to increase the intelligence of the model. Uh and uh so that is some of the value uh that Ambient provides. It's really changing the paradigm from having this increasingly stale snapshot of an open weights uh model release uh to something uh that is uh cultivated carefully uh by the network uh to maximize utility uh to the communities that it serves.

SPEAKER_01 14:43

That is uh it's I read uh Sam Lehman's post, say the world's RL Gym. I I think I saw you mentioned as a as a contributor, and it really didn't click for me how important this synthetic data reasoning trace flywheel is for models, right? I always just assume this outdated mental model of you know, data trains model, output, go back to train the model. Let's double-click on that for a second. So, what is a reasoning trace? Like walk us through a reasoning trace, how the network produces that, and why it makes the network smarter. And if you could, Travis, maybe just parlay that with how it's so different for just updating the zeros and ones of a model.

SPEAKER_00 15:25

Yeah, absolutely. So uh when you're talking about reasoning traces, there are a few different ways of going about it, but I'm probably gonna focus on two. Uh so uh one is where you have very well-defined uh problem domains uh that you're training uh a large language model uh to deal with based on kind of automated feedback. Uh so imagine uh you're trying to get a model to write a Python program, uh, and you give it access to a Python debugger. Uh and so uh what you do is you have the model output a bunch of reasoning traces, and the ones that don't uh trigger Python debugger errors are the ones that get rewarded. Uh and uh you know if you're looking at harder problems, uh the ones where uh there were all the problem solving approaches uh kind of shared similar problem solving steps, we reward the similar problem solving steps because we we have this idea uh that like statistically we're kind of honing in on the correct problem solving approach. And there's sort of a majority of uh you know voters in that uh approach who reflect a certain viewpoint and we sort of reward them uh for agreeing with each other. Uh so that that's sort of so reasoning trace to answer your question, actually, is a series of problem solving steps uh that gets rewarded in different ways. Uh and of course we're trying to reward correctness uh with those uh reasoning steps. Uh and so uh was your next question like what do we do with those?

SPEAKER_01 17:03

Or or which yeah, maybe maybe both, right? Like how do we take those reasoning traces? And my mental model is just sort of like a lot of different pathways the model takes until it's successful, but yes, like how do we take those and make Ambient's model better? Like what's the how do we go back?

SPEAKER_00 17:21

Yeah, um, so we we take those reasoning traces uh and we do more pre-training on the model. Uh so that's the other part uh of what uh Ambient is designed to do uh is uh you know, when the time comes, when we've built up sufficient reasoning traces on the network, uh the whole network takes a portion of its compute power. We can talk about how that works economically, and is devoted to upgrading the capabilities of the model um by uh pre-training it with these reasoning traces.

SPEAKER_01 17:56

That is I have to ask another stupid question, but so you're you're taking the reasoning traces and you update the model. How is it why is it so much better for the model to update with reasoning traces than it is to update with just raw data, a new encyclopedia, a new manual, a new textbook, like pure raw data versus the reasoning traces? Why is it so much better to have the reasoning traces?

SPEAKER_00 18:21

It's a great question. And it almost gets philosophical, but I'm going to try and give you a pragmatic answer here. So uh high quality data is incredibly important uh to these models. You know, what we discover is that uh smaller curated data sets are better uh for pre-training, uh, for fine-tuning uh than bigger, noisy data sets. Uh and the reason is that uh you know, you could think of these models as uh pattern recognition engines. And so the more clearly that you can spell out uh good patterns, uh the better it is for the model's learning rate. Uh and the more it's going to converge on something that you know humanity finds useful. Uh to give a little bit of a silly example, uh, like if you throw a bunch of romance novels at these things and you mix it in with a bunch of logic problems, you know, you're you're giving some incoherent uh like value system here. Not a good Hallmark movie. Yeah, exactly. Um and so that's why you know these reading reasoning traces, you could think of them as sort of the purest distillation of patterning. Uh they are logic patterns that in some strange way, as you introduce more and more of them to the model, universalize the model's intelligence.

SPEAKER_01 19:40

That is so cool. And I I gotta keep going. You have the best answers and takes here. So when I read the Worlds RL gym, when I've when I've talked to like the news research folks and all these others, there's always this recurring theme of these uh, you know, there's always domain-specific data, domain-specific models, and now there's like these domain-specific reinforcement learning uh gyms, as Sam calls them, and other areas where you know, if you're a doctor, you want specific reasoning traces to train a specific model, right? But in your in your situation, it's one giant model. So that's right. I how do you view the trade-offs of like having many ambient models with specific reasoning traces versus the one global?

SPEAKER_00 20:23

It's a it's a great question. And uh the first thing that I would say is that the IQ of a given base model is kind of fixed, like once you've once you've baked it. Um and that is that can be a detriment if the IQ is low. Uh, and it can be uh an asset if the IQ is high. Because what happens then, you know, ambient supports a model and its fine-tunes. Uh, and so uh, you know, on our network, you'll be able to go like train, pay to train a fine-tune of Ambient using your own gym. Uh, and the validators will check that that was a valid uh training process. Uh and uh you know that can uh be something that is like perfectly obscure to your area, and you can just load that uh fine-tune on the network and call it as much as you want uh and achieve good results. And the reason that we're confident you're going to achieve good results is because uh you're building off of the base IQ of the model. And as long as we can pick something with a high enough base IQ, uh, we're very confident that it's going to learn the right patterns. Uh, you know, I I do uh like probably way too much fine-tuning uh in my very limited uh spare time. Uh I I I occasionally I occasionally uh uh tweet about it. Uh but um you know one of the things that is really funny to me is how much better uh bigger models do with smaller data sets. It's kind of a weird counterintuitive. Thing. But if you feed a really high quality data set to Llama 8 billion, it kind of converges, it kind of gets better at answering the problems, and then it kind of plateaus. And if you feed the same data set to like a 27 billion parameter model, the convergence happens really fast. It's just smarter. And so it's figuring it out faster. And you have to stop a lot faster in some cases. Because, like, for example, the GEMA models that were released recently. They're really good for fine-tuning. And I'm ending my training jobs early because the model has already learned everything it could possibly learn from the data set. But that you know that gives us a strong indication that if you have a really big model and you're fine-tuning that, uh, you're going to get a lot of benefit from those fine-tunes.

SPEAKER_01 22:54

That is really cool. Yeah, I I I I guess I go back and forth. The smaller model, I would assume it's like less people in the room to convince, but then more. It's hard to go back and forth on that one.

SPEAKER_00 23:06

Yeah, the um, you know, there's some really interesting trade-offs that are going on in the space. I don't want to go on too much of a tangent, but I could talk to you briefly about my testing of QWQ. Um, so this is actually, you know, it's a 30 billion parameter model, roughly. Um, maybe 32, I don't remember. And uh it's really up there in terms of its benchmarks. Uh, like it's right on par if you just look at the benchmarks uh with uh Deep Seek. Uh and um when you use it, uh you start to feel a great deal of disappointment. Uh and uh the disappointment comes in uh you know, maybe it's a form of the bitter lesson, I don't know what it is, but the uh the disappointment comes in the fact that there is like a time space trade-off. Uh so uh it turns out that in order to get an intelligent answer out of QWQ, you have to output like 60,000 reasoning tokens. Um, you know, I actually almost burned my Mac down the other day uh running this thing locally. Uh you know, I gave it a very basic math problem, and my God, that thing was thinking so hard about that math problem. And it was asking itself rhetorical questions, like, am I really sure about this? You know, and it was doing it, but wait, but wait, that's the classic uh uh phrase that these models give. Wait, wait. Uh, and then it goes back round um and it spun for a solid 16 minutes. Uh and uh, you know, Deepseek, uh, which I was running on some H100s, uh, just went, yeah, it was like 15 seconds. It had a very succinct reasoning trace. Uh, the answer was immediately clear. They got to the same answer. Uh, it's just that the cost of shrinking that model down uh so much uh was that it took an incredibly long time for it to emulate the level of intelligence of DeepSeq. And that's not something they discussed in their benchmarks for obvious reasons. Uh, but really, when I see these now, I think that they should just normalize them based on the tokens of output, uh like intelligence per token of output, uh, which strangely enough is the same premise of ambient. It's like, well, it's like we're really measuring intelligence in a standard way, sort of per token of output. If we think that the intelligence of these models converges at a certain scale, uh then that's the value that you're getting.

SPEAKER_01 25:40

Um The the output intelligence, does that include so like two plus two equals four, it's 15 minutes on one model, 15 seconds on the other? I I'm greatly simplifying the math problem, you probably did. But the intelligence per token, does that include all those reasoning traces, or is it just the answer?

SPEAKER_00 25:59

It includes all those reasoning traces. Uh because that's you know, you can think of that as a proxy for the amount of compute that it's spending.

SPEAKER_01 26:07

Okay, so if you both arrive at 15 and one uses 15,000 tokens, the other uses 100, the one model is much more intelligent. Oh, because it includes the time dynamic and the correct answer.

SPEAKER_00 26:20

That's right.

SPEAKER_01 26:21

Okay, this brings us to another interesting question. Um how will Ambient be as fast on the inference side as the hyperscaler, an open AI clawed anthropic? How do I get my answers fast, if not faster, than the centralized players?

SPEAKER_00 26:37

Well, I I think uh there are a few different answers to that. The first obvious answer is that you have to be operating on a fast blockchain substrate. And so uh, you know, we're very uh happy and proud to be building off of something that has proven to be a speed king, uh, which is Solana. Uh and we're not doing anything to that blockchain in our fork of it uh that is impacting its speed. Uh and it provides, if you have that fast finality, um, that uh allows you to present a similar user experience uh to what people are expecting, uh, which is that they pop the transaction in. Um, you know, models can even be streaming in our protocol and have streaming verified inference. Uh, but these transactions are happening very quickly. Uh and uh, you know, I think uh the future is bright uh for Solana, and we've set ourselves up to uh be downstream from the performance improvements uh that they make. Uh, but they are going to push the limits of TPS that we think is possible. Uh, you know, we love the engineering uh team uh over there. Uh we we know some of the members. Uh we've also uh met with Anza, and we think they're absolutely crapped in terms of their engineering talent. And uh, you know, their contributions represent the first sort of pillar of uh ambient, uh, you know, which is absolutely like nailing the core blockchain uh tech. Um but then uh you know the second thing uh is uh really enabling efficiency on two sorts of uh use cases uh for uh LLM inference. Uh so the first of those is what you're talking about, uh yeah, which is the uh Bitcoin uh hyperscaler, you know, I just got into GPUs now um uh sort of uh profile. Uh and you know to do that, uh what we enable is patching of uh open source uh highly competitive uh inference engines. Uh so things like uh VLLM and SGLing. Uh you know, these projects are spending you know really uh huge numbers of cycles on achieving bleeding edge performance. Uh and we want to leverage that uh and also raise their profile as well because we think they're great products. And so what we do uh for our verified inference uh is we patch them uh to make them trustless uh without really impacting their performance much at all. Uh and uh so they support high-scale batch inference. Uh, you can configure them to do routing around different machines, uh, and uh it's a really good experience. Uh and you know the benefit of that, you know, building on top of successful platforms uh like Solana, like VLLM, uh, is that you sort of for free inherit all the innovations that they implement. Uh and in the case of like VLLM, you know, you can be sure that they're pouring over the deep seek uh code releases and are starting to include some of these routing uh optimizations and things uh in their engine. Uh and uh they also are very configurable uh to different situations. And so if you're a provider, it means you have a lot of documentation and support like just out of the gate because you know we're not some like completely custom thing. It's like, well, you just apply the patch uh to VLLM, and it basically operates exactly the same as vanilla VLLM, except it gives you verified inference instead of unverified inference. Uh so sort of these are two of the approaches, and the other one I kind of already mentioned, uh, which is that we support uh streaming uh verified inference uh you know to some degree. Uh there are there are limits uh you know on how many tokens you can get down to before that starts to become a poor uh experience, but you could think like 10 to 20 tokens at a time, like if you have a really uh long answer, you're kind of approximating the performance of these reasoning models anyway. Um, because like O3 mini high, you know, it's spinning, it's saying, I'm thinking about this, I'm thinking about that. And you know, we can very much do the same thing uh just out of the box.

SPEAKER_01 30:59

That is those are awesome answers. Um and I guess the reason to fork Solana would be you don't want non-ambient related transactions on your network. You sort of want to keep the fees low for what you're doing.

SPEAKER_00 31:13

Well, uh it's actually there there are a few reasons. Um so, first of all, uh like philosophically, uh ambient is designed to uh serve up uh machine intelligence at scale to the world. And so in order to do that, you have to create a very specific economic system. And what you specifically need to do is be the best place for miners. Uh so uh, because you know, miners provide your quality of service on your network. And you know, let's be honest, they deserve the rewards. Uh, you know, they're they're doing the work. Uh and so uh in order to have a network uh in crypto economic terms that rewards miners of inference, miners of inference actually need to secure the network uh because that's how crypto works. Uh, you know, those who secure the network get the rewards. Uh and you know, so in Solana's economic design, it's a different thing. It's you know, it's optimized to be like a stock market, uh, and it's not optimized to be like a minor reward platform. Um and those are you know, these are different objectives. Uh, they're both very useful. We'd like stock markets uh, you know, in crypto to be super functional, and we'd like to be able to run all sorts of complex financial engineering problems uh you know on a crypto network. It's an awesome use case. And we'd also like to have a high-scale inference provider. It's just that the high-scale inference provider needs to be you know catering uh to the compute that is really uh bringing it uh you know, bringing it the quality. Uh and so uh you know, specifically what we change about uh Solana is that uh we we change the definition of stake. Uh so uh stake goes from uh you know rewards for locking your capital up uh to uh validated problem-solving contribution to the ambient network on two different timescales, uh sort of a short timescale uh and a longer timescale. Uh so what that means in uh Solana terms is if you are a big miner on our network, uh you are more likely to be selected as a leader uh or a validator uh in ambient terms. Uh and if you're a big miner, you are more likely to be selected as a member of a committee uh that verifies inference. Uh so you know it puts miners in the driver's seat. Uh, it gives them the rewards, uh, it gives them uh the decision-making power, uh, and it preserves that sort of tower Byzantine uh consensus model uh that Solana has and the power and speed of that, uh, but makes it into a proof-of-work network by just tweaking one little thing.

SPEAKER_01 34:15

Travis, maybe a basic question for you, but in finance, in crypto, there's always this notion of you want to be co-located. You know, the hedge fund wants to be close to the New York Stock Exchange. That's I understand. Well, I don't really understand all the technical reasons on the I, but I but they track. Um, is there a notion of me being closer to an ambient node than I am to an open AI node at a data center? Or is that not I might have missed that.

SPEAKER_00 34:42

Yeah, so actually, you know, it's helpful to talk about um like the other use case that we want to enable also. Uh so you know, there's the the hyperscaler uh use case, um, but then we're doing something a bit more uh custom uh to support uh the OG Bitcoin uh user use case, uh, which is that you want to join like a mining cluster. Uh you know, you want to take your uh Disco Lite GPU and uh get on board uh and just you know show up and mine something. Uh and you know traditionally that has been very difficult uh because it's hard to spread large language models across uh many GPUs and have any sort of level of performance. But you know, recently there have been uh some papers and uh research uh that has really uh I think uh opened the door uh for really performant uh inference uh in a distributed setting. Uh and so that is our uh next challenge that we've undertaken to make that type of inference uh competitive and cost competitive and uh timing competitive uh with uh some of the uh scaled up uh inference. Uh and so then that relates back to your question because uh yes, you have the the hyperscalers who are in data centers all over the world, uh potentially who are in bunkers, you know, uh who are in like my my I my dear father, uh I I love him. Uh he was really into Bitcoin and he rented out a police bunker uh that was not used in St. Louis and was running a Bitcoin mining operation.

SPEAKER_01 36:22

That family dynasty is nuts. I I we could do a whole other episode.

SPEAKER_00 36:26

This is I have this picture with my uh my brother and me in an elevator shaft of the police command center. So he had GPUs just in the bottom of that bunker? Yeah, like they were bare, like it was just open, and he had it like all these wires. It was it was pretty great, actually. Does he still have it? Is it still no, no? Well, we could talk about this, but the Bitcoin mining at that scale actually became unprofitable, which is a whole older yeah. Um but uh at least with GPUs, right? Like everyone went to ASICs. Um but you know, the relating it back to the data center concept, there are two key things there. Uh first of all, yes, absolutely. We expect that there would be closer nodes to you. And so there could be some uh latency optimization that goes on. Um but also uh like if you are in a part of the world where there are no GPUs and there are no data centers, uh, you know, you are not uh at the mercy of data centers uh anymore because there's a whole distributed network that wants to take your business. Uh and uh you know that that should give you like if the whole economy depends on machine intelligence, like that should give you a little peace of mind, is that we're gonna build sort of a safety net for you.

SPEAKER_01 37:50

Travis, one thing that went over my head, I just want to clarify. So you are we are focused on one model, but you you mentioned that there will be specific fine tunes. Is that like per domain, per application, or per user? And apologies I missed it, but I want to make sure we clarify.

SPEAKER_00 38:06

Well, you know, it depends on what the network comes up with. Uh, you know, we're going to actually seed uh the network with some really interesting uh fine tunes that we've been cooking up, uh, which we think are a lot of fun. Uh, you know, I kind of uh share a view that I think you expressed on X, which is that uh large language models can be very boring to deal with. The AI slop is a real thing. Uh and so uh one of the things we're doing is uh putting together some very opinionated and bossy models uh that will disagree with you uh and give you very specific perspectives and feedback on things. And they will disagree with each other. Um and we we think that you know it's it's funny on the one hand, but it also gives people uh you know a little bit of a canvas to paint on. If we publish those techniques, uh show how uh models can be tuned in a different way, uh, that it will open up uh sort of a creative uh license uh for others to uh create uh stuff that's interesting. Um to like to get a little bit more uh specific if you're talking about useful domains, uh you know, I think that uh a lot of like current jobs that require lots of manual effort are going to be automated. Uh you know, different types of uh, you know, I'm gonna call it coding jobs. And a good a good example would be insurance coding. Uh so not like coding coding, but where I'm looking at this thing and I'm like, what category does this fall into? And you know, somebody is spending like an hour like pouring over documentation and photos and trying to say, is this, you know, does this qualify for catastrophic flood insurance or something? Um and like I hope, and I think it'll be to uh you know to the uh great benefit of everyone that all of those jobs go away and that people are able to do higher value work than that. Uh but you can imagine it's great to get rid of the menial work, right?

SPEAKER_01 40:08

Yeah. I don't know why people are against that. It's way better quality of life.

SPEAKER_00 40:12

Yeah, and you know, this actually, you know, this kind of goes to the world that Ambient uh thinks about, uh, that we want to support, that you know, we think our network can help bridge to, uh, which is a world of abundance that runs like a Swiss watch. Uh so to give you uh an example uh of what I mean by that, uh, you know, I am a pretty modest traveler. Uh I think probably a lot of us are, you know, I book economy flights, uh fairly mediocre hotels, uh and you know, I feel like a slight pride when I do this because uh, you know, I I'm being I'm being frugal, I'm I'm spending on the things that are really important. Uh but I'll I'll tell you, like occasionally in my life, uh people have put me up in really fancy hotels. You know, it's usually for a job interview. Uh and uh, you know, this uh it's a swank place that I would never pay for. Yeah, I I I just I feel uh like this tremendous sense of luxury, but then the thing that I always notice when I go into the hotels is that the elevator is scuffed. There are cracks in the mirror, in the corners, uh, there's dust, uh, there are fingerprints in places that probably should have been wiped down. And it always makes me think like there's not actually, even in places of abundance, enough human labor to make things perfect. You know? Uh there's always more polish that could be done, but they're not enough hours in the day for people to do that. Um, but imagine a world where we had enough productive capacity uh where uh robots could be polishing the heck out of all the elevators. Uh, like the customer service could be perfectly tuned on every dimension. Uh, someone could be thinking about solving your problem in the deepest possible way and working with others in a cooperative fashion to solve collective problems uh in a smart way that no person could have ever thought of. And if you build that kind of world, and I think that that world is best supported by ambient intelligence, and intelligence that lives everywhere and is not influenciable by special interests or different parties. Like if you build that world, uh everything just becomes so much better. You know, uh, you can uh you could uh really create that uh sort of Star Trek the next generation uh feeling, I think, for everybody, just by focusing in on the details of every decision, of uh every little action uh that was taken in the world in a way that like people on their own could never afford to do, companies could never afford to do. But in a world of abundance, like properly directed, that becomes possible. And that's that's what we we want to build.

SPEAKER_01 43:23

That's such a great example. It's uh I can't understand tons of robots in AI working so hard on every little tiny thing to make life perfect. I don't think I can understand that until I'll experience it. You probably can, but it it's uh yeah, that's tough.

SPEAKER_00 43:40

Yeah, it's like uh it's kind of like uh what if we could turn every place into Disneyland? You know, Disneyland is like a place that every corner is scrubbed, you know, they're very particular. Um, but like what if we could turn every corner of every street into Something like that, and what if we could uh think at a level where the contours of our national discourse and our communications among each other uh were pristine and at a level uh of perfection that yeah is almost unimaginable. Like that's you know.

SPEAKER_01 44:21

Travis, the first hotels to get this are gonna be the rich ones. So you're gonna have to break your rule and spend uh no, it's a it's a great example. So maybe just going back, I have a couple of nuanced questions for you before we get into the future, but um in Ambience, you know, bio, marketing materials, et cetera, there's a focus on the 600 billion parameter model, right? And in crypto, we deal with so many different governance votes, DAO decisions, way back when it was the maker-DAO interest rates, now it's select improvement protocols, take it back. For you, how does the network decide? You know, hey, I want to focus on a new type of foundational model, right? Or I want to expand the parameter count. Like, how do how are we gonna make all these decisions for the base model?

SPEAKER_00 45:08

It's a great question. And I think that the answer has to be in a properly decentralized fashion. Um, and you know, the drivers of this network are the miners. Uh, they're the ones who have the most rational economic interest in the network uh being used and in being highly secure. Uh and so uh we're gonna give them the ability to vote on uh what model to adopt. Uh you know, initially our thought is that that's gonna be like a multiple choice vote uh you know that'll kind of come up like Solana had this economic change vote, you know, it was it was voted down. But you know, that's a good example, right? Like uh you've got two options, yes or no, uh, and uh someone can vote on that. And so initially, you know, we will curate the options. We'll say, all right, there are only you know, maybe four open weights models in the world that we could all adopt uh that would be competitive uh with the state of the art. Uh like here are the options. What would you like to do? Uh and people will vote. And then, you know, just as when you have a validator upgrade in Solana, uh, there's a scheduled downtime. Uh there'll be a scheduled downtime, uh, the nodes will go down, uh, the model will be changed, the nodes will come back up, uh, and we'll be in a slightly different world.

SPEAKER_01 46:25

That is pretty cool. It it's um it'll be interesting to see if these hardware guys agree with the AI experts, right? That'll be interesting because there has to be some communication between hey, look, this is a literal leading edge model. We want to change something in ambient, but I know this might impact incentives short term. I don't know how it'll play out, but it'll it'll be these are the ethical.

SPEAKER_00 46:52

No, I think you have to um you have to really be candid in your communications about the pros and cons. And uh, you know, probably there's an element of the whole community expressing its preferences as well, uh, just so that the miners get grounded, you know, when they ultimately vote uh in what the community's interest really is. I think those are really important points.

SPEAKER_01 47:13

So, Travis, two more quick questions to make sure we cover as much as we can about Ambient before the fun future stuff. You've mentioned verified inference a lot through the podcast, and I haven't asked you why that's important. Why is verified inference such a core part of Ambient?

SPEAKER_00 47:28

Because composability is fundamental to crypto. If I want to uh build useful things on crypto, I need to have a trusted way of putting the pieces together, a trustless way of putting the pieces together. And if uh large language model inference is going to be one of those big pieces uh in the future of crypto AI, uh then I need to make it trustless, uh, which means that the inference must be verified. Uh because if you use unverified inference, uh then you know, there are all sorts of shenanigans uh that can be pulled. And we've we've seen them in non-LLM world with logic exploits, uh, but you you know, it's uh almost unimaginable what will be accomplished uh in unverified LLM model world.

SPEAKER_01 48:18

Do you think that there has been um weird or funky things with inference behind the scenes of the Web2 labs that we cannot view or understand or see? And like what would they be?

SPEAKER_00 48:31

Uh so uh I do, and I think it's a huge problem. And I'm gonna relate this to software deployments because I did I've done different types of software deployments my entire career. Uh and uh you know, when you deploy a piece of software, you normally do particularly one that's safety critical. You know, I used to do like large-scale safety critical system deployments. And so when you when you deploy those things, um you want to do unit tests and integration tests. Uh you want to make sure that the individual little pieces of uh functionality are checking all the boxes that you want, and you want to make sure that the different architectural components are communicating with each other in the expected way and producing the expected results. Uh and uh when you insert an LLM uh into the middle of something like this, uh you immediately run into some severe problems. Uh and what you know, one of those is related to the non-determinism inherently of the output. Uh, you know, there's just a fair amount of randomness baked in, if you even if you set the temperature uh to uh zero. Uh but another uh problem is uh that uh it's very hard uh to predict uh what will happen when you give a novel series of inputs uh to the LLM itself. Uh and so you know it's uh because of this phenomenon, uh because like that's already very hard, um closed source model providers are absolutely causing chaos when they deprecate models really fast. So, you know, GPT-4, it lasts like the version that we care about lasts like a year. Like as a software shop, like I'm trying to figure out all the behaviors of these models in different conditions. And if I get it wrong, my car dealership might sell someone a Chevy for one dollar. Oh, and there's just no way in hell that I can test all this stuff at the cadence it's being released. And so, and they're not being transparent with us about the change in the behavior of the models either. They're just like, oh, it's smarter now. And it turns out like it doesn't answer any political questions anymore. Uh or like, oh, like the model is better at problem solving, you know, except for in your coding use case where it absolutely faceplants every time now. Uh, you know? And uh so on a very like visceral level, like they're absolutely changing everything all the time without being properly transparent. Um and I think that that is really dishonest and also dangerous uh when your economy uh depends on these models because people feel a certain amount of pressure to keep up with the Joneses and to switch. Uh and uh the moment they switch, uh they're not incurring risk for open AI or Anthropic, uh, they're incurring risk for their business. Uh so I think that's one whole category where yes, some shenanigans are being pulled. Uh, but then there are some subtler things that people over time have really picked up on. And those include the fact that uh the models are being dumbed down uh silently uh for no good reason other than the provider's infrastructure is melting down, right? Um, if you've used Claude, uh you have probably gotten the responses temporarily abbreviated due to high load uh message, uh which is honestly better than it used to be, because you used to just get stupid responses and wonder why. Uh and and now you get stupid responses, uh, and you know that it's not just that uh the model is under heavy load and they're shortening what's going on, but actually, if there's thinking going on in that model, they're shortening the reasoning traces associated with that model as well. Uh and that is causing the model to be noticeably dumber.

SPEAKER_01 52:37

So you could you could just get be way smarter if you use the models at the right time of day. I mean, I don't know when that day time of day is, but well, you know, I'm I'm a strange night owl.

SPEAKER_00 52:48

Like I'll stay up until two and three in the morning, and boy, those models are smart at like 2 a.m.

SPEAKER_01 52:54

They're dumb at 10 a.m. Eastern. Yeah, they are yeah. I I was trying to fix our Delphi bot I built to just handle inbound plays, and it was hallucinating on the number of plays it reviewed, which I made a tweet weekly, a very basic thing. And I asked XJDR how to fix it, and he's like, just lower the temperature and such an easy fix for the yeah, just such a it's less creative, but yeah.

SPEAKER_00 53:18

Well, so yeah, I mean that's a major problem is this dumbing down of models, like without being transparent about that. But there's actually a final class of problem, which is related to all the censorship uh that they have put into these models and the way that they silently change the censorship, uh, which actually breaks use cases. Uh so you get these patch notes on versions of claw that are like, oh yeah, like more responsive to queries now. Um it's like, oh, like uh what actually changed in the behavior? Like which queries are is it more responsive to? Is it going to respond in what you and I would think of as a truthful way now? Uh or is it going to hedge in what we might think of as a deceitful way?

SPEAKER_01 54:01

Uh like what is a it's crazy because I always think of censorship as that system prompt when you ask it a question that lives in the shadow in the invisible realm that that tells what the can and cannot do. I also think of censorship in the data that goes into training the zeros and ones. You're talking about a totally different level of censorship.

SPEAKER_00 54:22

Yeah, it's uh the kind of censorship where the model is actively trying to prevent you from getting into whole spaces of thought. Uh where like Claude has a it has a morality built into it. And if you try to lead it along certain uh perfectly valid uh philosophical lines, uh it will try and steer you into another zone. It's kind of like uh in those in the old video games, uh like if you would walk to the edge of the map, uh they would like turn you around. Um it's it's I like that's the best analogy I can think for it. Um but it is But why though?

SPEAKER_01 55:06

Like why wouldn't they let you go off? Like, what is the downside for Anthropic saying, hey, go explore the matrix? Like, why don't they let you?

SPEAKER_00 55:14

Uh that is a great, great question. I I think there are probably a couple of reasons. Um and I don't want to get myself into too much trouble, um, but I think that the world has embraced a culture of safetyism uh where uh it is dangerous for people to even encounter certain ideas. Uh so uh you know, we used to think that uh you sh you know shine the light of truth on bad ideas and they evaporate. Uh and there are a lot of yeah, it it it disinfects. Uh and you can argue against them, like it's very easy if you expose them. Uh but I think that uh you know the culture has shifted in a way where uh these companies have this almost paternalistic um uh contempt uh for their users. Uh they think I need to protect you from dangerous thoughts. Uh and uh you know that actually makes us all stupider because then we can't we can't reason about dangerous things if we never encounter them. And you know, the reality is yeah, life is full of dangerous things. Uh and like you wouldn't want to uh try to learn to ride uh a bicycle, you know, without uh like uh or go down a highway on a bicycle uh without ever having fallen on a bicycle in your neighborhood.

SPEAKER_01 56:48

I mean imagine never seeing a gun and then seeing it for the first time. Like you don't that's life or death.

SPEAKER_00 56:53

Yeah, and like if you're just using some of these image gen models, you might never see a gun. You know, like that's true. Uh and so the I think that there is this patronizing moral attitude that uh we have a better idea of what morality is than you do, uh, and we are going to protect you uh from all the bad things in the world. And I think it's one of the insidious dangers of uh corporately controlled LLMs because uh that attitude, while extremely frustrating right now, can morph into totalitarianism very quickly. You know, all of a sudden, dangerous ideas start to include uh, you know, opposing the existing political order or the economic interests that are sponsoring the LLM today. Uh, you know, you can imagine like Microsoft Azure Chat GPT sponsored by Pfizer. Like, it's gonna do deep research, but is the deep research gonna return uh like any results about adverse reactions to the drug that you're taking? Uh like maybe not. Uh that sure would be helpful to know.

SPEAKER_01 58:06

Don't don't research a new drug if it conflicts with one of our main selling drugs. I mean, that's right.

SPEAKER_00 58:12

Uh we've seen we've seen the commercial incentives uh like skew our news media, uh, we've seen them bend all sorts of institutions uh in really unpleasant and unhealthy ways uh for the populace. And I think that uh the same thing could happen uh with uh LLMs in in really scary ways. And so that's why you know I would uh just offer an appeal uh to the people who are out there, you know, thinking about something like ambient. Like, please come on board with us. You know, advocate, advocate for this kind of decentralized model uh so that we can make a better world uh together.

SPEAKER_01 58:59

Well, one of the one of the reasons I'm so happy for you is because you're you're building at a time when the world isn't yet, all the apps aren't yet powered by these AI models, right? There's still a chance that we can build them all on ambient, right? Um be a lot safer. Um well not safer. Uh yeah, it's now now we're getting in a weird vocab. I guess uh more democratic or open.

SPEAKER_00 59:24

It's uh it's more free. And you know, that comes with that comes with upsides and downsides, but uh at the end, like honesty is really important in society. And if you can't trust anything uh that is presented to you, uh you start to live in a low-trust society, and that has really negative impacts on your quality of life and on your civilization. And so, you know, I think that creating a network like Ambient, which is censorship resistant and which is designed to be economically uh competitive uh with the players that could dominate, is what creates a wedge alternative uh that prevents us from getting into the totalitarian dark futures uh that are possible. Uh but you there always has to be an alternative, or uh those players are going to exploit and try to control as much as they possibly can. Uh and yeah.

SPEAKER_01 1:00:23

No, no, I I preaching to the choir, I totally, totally agree. One um one question I have for you is taking the behemoth open AI. Um we can talk a lot about a lot of different ways we can take this, but open AI is, I think Sam uh mentioned they may open finally open source some model, right? Right. Um if is there let's like play the other side. Is there a risk that open AI ever goes full tilt open source? And if they do, do you think that would negatively impact AMP?

SPEAKER_00 1:01:00

Um, so uh that's a great question. And you know, I I think I have a few different answers to it. Uh like I we should play the Steelman version of this where they do, and we can talk about it. Um but let us look at the history of it. Uh so if you look at the emails uh that they sent back and forth, uh, you know, I I think there was a conversation between Sam and Ilya at one point where it's like we're going to use open source as a recruiting tool, uh, but we're never going to actually give the technology to the public. You know, if I'm badly paraphrasing this, maybe. But there's one of these emails that feels like a smoking gun to me, uh, where they were just um, I think, abusing uh the community, uh, to be quite honest, uh, to generate enthusiasm for their product in a very cynical way. Uh and that's baked into their DNA. Uh, you know, that level of cynicism, uh, like it or not, exists at the founding point uh of the company, and the company still has the same leader. So if you're at if you were to ask me, like, do I believe that you know Sam Altman has had a like a wave of benevolence like wash over him? And uh, you know, um, like uh I think he's uh you know an excellent capitalist. Uh and he will do some things for temporary advantage, um, but he will never uh give uh true economic advantage to other parties. Uh, because for someone like that, that would be to cede the field uh to the competition. Uh he will always maintain the strongest possible edge uh for open AI while trying to create a narrative that is palatable uh to the public.

SPEAKER_01 1:02:48

I think that's a great way to put it. And this is a great a great segue if you have time. But like when we first did a podcast, you wrote a phenomenal long-form response to Leopold's situational awareness post called Situational Blindness. I'll link it in the show notes. And Leopold argued a bunch of things that um increases in computational power and um unhobbling and algorithmic improvements would increase the power of these AI models, yada yada. Talked about web 2 and scaling and data and hardware and everything like that. Um, since then, you've made considerable progress with Ambient. OpenAI has made insane progress with everything they've done, but we also have open source, we have Deep Seek, we have Quen, we have MANIS, we have take your pick. Have your views changed at all from your long former post on situational blindness? And then I know we can't go into the whole post here, but I'm curious if anything specifically changed in your view since you wrote that post.

SPEAKER_00 1:03:47

It's a it's a great question, Tom. And I mean, I appreciate it. Um, you know, I let me let me go over some of the assertions that uh Leopold made. Uh so uh he said we live in a bipolar world. Uh it's the US and China. Uh and uh they are in, they're locked in economic competition. Uh he said the only expedient thing to do is to lock down all AI models, all AI research, and form a Manhattan project uh that is devoted to defeating China. Uh and uh we should clear the way uh for everyone regulatorily. Uh we should uh favor the incumbents, uh, we should declare open AI specifically. They might have had a little bias there. Uh we should declare them the winner of the AI race. And we should essentially give them the keys to the kingdom along with Microsoft and say, you know, go build as many coal-fired or nuclear power plants as you possibly need uh to uh compete with the Chinese. Um and uh he very strangely uh justified this uh in the name of democracy. Uh and uh you know it it's a it's a bizarre way to look at the world uh that you have to empower the most secretive and undemocratic institutions in a society in order to achieve democracy.

SPEAKER_01 1:05:17

With the totalitarian lock-in that comes from a single model winner.

SPEAKER_00 1:05:22

That's right. Um and uh it is been immensely frustrating for me uh to watch uh people you know who have nominally democratic uh you know principles uh support this stupidity uh because it is just plainly stupid. Uh and anyone who thinks about it for 10 minutes should be able to come to the conclusion like this guy is a very smart idiot. Uh you know, he's he's right about certain things, he's right about the scaling curve uh to some degree, although we can talk about that. Uh he's he's right about some of the unhobbling things, although he doesn't do a very good job of explaining why. Which I think is kind of crucial when you come in with strong claims. You need to sort of justify those. But he's very stupid about the implications of what he's doing because if we followed his plan, we would end up in a totalitarian hellscape. And you just have to follow his own assertions to their logical conclusions, which is what that essay does to come to that. But then let's sort of take that and let's reflect on that what has changed in the world since that essay was written. And I think that what is maybe becoming clearer is that secrecy is not the biggest weapon. Openness is actually the biggest weapon. Because all of a sudden people thought, Nvidia's not going to be selling as many data center cards now. And you know, like I personally think that's that's probably healthy. Like I don't think that one company should uh you know dominate the entire sector of the economy. Uh uh and uh but you know uh people were aghast about this. And you know, uh so it turns out that uh Leopold's fundamental assumption was that the uh form of economic attack was going to be secrecy and uh competition in secret. Uh but actually uh it turned out uh to be nullifying advantage uh through open source releases. Like that would that was the power move. Um and that China pulled. Yeah, that China pulled. And I think what that did uh is it changed the patterning for the world. Because now you know different groups in the world, like you know, to give an example, uh Mistral uh stopped kind of releasing models for a while. Uh uh, you know, they juiced their stock price, uh their yeah, whatever their internal stock price was, so the valuation. They juiced their internal valuation, they got a lot of funds from the European entities, and then they kind of shut down their open source program for a little while there. Um and uh then they came roaring back recently with like uh this Mysteral Small model, which is awesome. Uh and I think what they saw is that uh you can lead through inspiration. Uh and uh if you inspire the most people and if you give the most, uh then uh the world might come along with you. Uh and you know, it's something that uh I think would be predictable if you think about how business relationships go. Like you might see you might meet someone at a conference. The person you stay in touch with is the person who's offering you some value in that business relationship. You know, maybe they have some crucial perspective or insight uh that they're providing to you for free, essentially. Uh and when they do that, like you feel positively uh about them. Uh and like they may influence you, they might change your mind about some things because they're giving you a gift, and they might change your mind about some unrelated things uh that uh you know you hadn't really deeply considered before. And uh I think that what we're seeing is hard power versus soft power. So in a world where the US is abandoning its soft power, uh uh China is embracing a different kind of soft power. It's the soft power of open model release. Uh and Leopold's premise is based on hard power. Um, but I think he got it wrong. Like, the the most powerful thing is soft power uh when exercised carefully. Uh and to be clear, like I think that uh you know that's not typically how a lot of countries act. Like I don't think that countries play the soft power game uh very well. Uh but I think that they've discovered in this arena that model releases are soft power. Uh and I think that that really screws up Ashen Brenner's calculus. Uh, because now you're gonna have uh countries jockeying for soft power via model releases. Um and I think that's awesome. Like I think that's really healthy. Um but you know, if you were to ask me if I changed my opinion, uh I would say that uh I don't think his argument has gotten any less wrong. I don't think the implications of his argument uh have gotten any less dire if people were to follow his assumptions. Um but I do think actually that the technology picture um that I was thinking about has gotten significantly less bleaker, uh less bleak um, you know, in the interval. Because, you know, at the time that I wrote that, it was very unclear if anyone was ever gonna catch up with OpenAI.

SPEAKER_01 1:11:24

China didn't even have a did they have a great open source model when this was written? No, right?

SPEAKER_00 1:11:29

So much. And uh, you know, I think um one of the big funds had just released a piece about how uh you know models were going to be like mainframes forever and we would all just have to rent from them. I don't know if you remember that. I forgot, yeah.

SPEAKER_01 1:11:48

Um the crazy part um also with Leopold's argument is that um he argued, I think, that we're racing for like this trillion dollar cluster. But the reality is like DeepSeek, like they really innovated, right? Like they use old generation hardware, they only turn on parts of the model mixture of experts. They did this absolutely nuts if you read Sam Lehman's post, innovation on how to train their new reasoning model. So, like to think that we're just competing in this just direct linear fashion with one model type and architecture, it just it that like just doesn't make sense.

SPEAKER_00 1:12:25

I I totally agree with that. And I think that there's going to be a lot of um innovation in the field, you know, both in software and in hardware. And you know, one of the really important things for for Ambient when we were looking at the design of the system, uh, you know, is that it was future-proof. So you know, we designed our algorithm in a way that uh, yeah, the model architecture could change and the verification would still work. Uh the underlying hardware uh could actually change significantly, and things would be totally fine. Um and uh we expect the hardware to change. Uh we expect the model architectures uh to change. And I agree with you, like this idea that uh one group had cracked the code for a field as broad as uh machine learning is very arrogant. Um and you know, possibly only explainable by you know someone being in their early 20s and only ever have really having really worked at one company.

SPEAKER_01 1:13:26

No, I I'm glad it I at the time I was happy with how forward-looking his article was, but I'm more so happy with how how correct your your response was and how solid that I recommend people um go look look at it. Um Travis, we have like 15 left. Um we use it, we use it. But what one of the other things I want to ask you, I know we're jumping around a lot, but there's just so many different things to cover. Um in that world that you're describing where these countries are releasing open source models, um maybe I'm being overly bullish here, so you could back, you could agree or disagree, but do you think that there's a world where they say, hey, look, you know, we don't have the expertise to do this, we don't have the hardware, the energy, the data, whatever. Let's buy ambient. Let's let's let's let's join an open source alternative that we can just buy the asset of. Like, do you think that that's a viable sell to these countries, right?

SPEAKER_00 1:14:20

I do actually, because I think it can be a meaningful expression of soft power. Um, you know, if you have countries that band together in the world uh to contribute the to the uh pre-training of an alternative model, uh, you know, who become part of uh a global expert discussion about uh how to improve uh collective capabilities, uh, I think those kind of forces are always gonna be more powerful than unilateral action on the part of one country. And I think a balance of power in the world and a spreading of wealth around the world, uh, and there's gonna be plenty to go around because I think that we we can have abundance with these technologies, uh, is uh what we need to have global peace.

SPEAKER_01 1:15:07

That is a good point. And then uh unrelated, but the usage of ambient, the on-chain nature versus the off-chain nature, right? The privacy aspect comes into play, right? If I type in response into ambient, is that on-chain? Do I have to worry about what I'm what I'm typing into an ambient front end?

SPEAKER_00 1:15:25

Oh, yeah, it's a it's a great question. Um, so no. Uh so the way it's set up is uh it happens via an auction. Uh and on the auction, you say, I have this query that's this long, and I need it back in this amount of time, and I would like to pay 30 ambient uh for that. Uh and then miners come in, and in order to make a bid, a miner puts uh some money in escrow so we don't have Sybil attacks and spamming. Uh and uh they say, Hey, I can do your query uh for uh 15 ambient uh and you know 20 seconds or whatever. Uh and uh the second lowest bid wins. It's kind of a reverse uh auction. Uh and uh you know what that uh enables is a kind of economic rationality in the sorting of queries to different miners. Uh so uh and that helps uh dynamically adjust the price uh to match the actual value uh that's being delivered. Um but what it also does uh is uh it's a mechanism for uh obfuscating uh the source and nature of the queries uh and only revealing those uh to the miner uh and the validators of that inference. Uh so uh you know, if you're the miner, uh you're necessarily going to see the query and its output, uh, but you're not gonna know where that query came from. Uh and that query is gonna be in an economically similar block of queries, queries that are all kind of the same length that need to be delivered in the same time frame, uh, that uh you know is is an unintelligible jumble uh to you. Um moreover, uh in times of low traffic, and there are those on every network, uh we're mixing uh programmatically generated system queries into the blocks. Uh so it's gonna be really confusing. Um, our model is essentially like the same security model as RunPod. Uh like you know, you you show up on RunPod, you run different operations. Uh yeah, like the um the user could uh the operator of that server could uh look at individual things you were doing on there because they have control of their uh machine. Um but what we think with is a little different in LLM world is that unlike in RunPod, uh where you have observation of someone's continuous uh usage, uh it's very, very difficult uh to build any sort of useful profile of someone uh if you don't know where the queries come from, uh if the queries are all uh mixed uh together, uh, and if you can't do any flow on the queries to correlate uh the queries to each other. And so we're we're taking measures to address all of those pieces. Uh, and we think that uh that achieves effective uh anonymity uh and uh you know is going to be is going to make it uh easy uh for for people to uh adopt the network because they can have uh they can have confidence that the design of the system uh makes it incredibly difficult to glean anything useful from any particular query.

SPEAKER_01 1:18:43

That is awesome. No, it's great to know that you're approaching it from three main angles to maintain that anonymity. And you you mentioned like 30 ambient, 15 ambient. I'm trying to think through a world where I price intelligence in ambient, right? For the small inferences, the little little asks, you know, what what the sky is blue, what's five plus five, those I won't really worry about. But do you think we'll ever get to a world where you and I are sitting across from each other and you're saying, hey Tommy, you can go start a business with 10,000 ambient. Do you think we'll get to a point of that?

SPEAKER_00 1:19:16

I do, because I think that uh, you know, to to make a fun example of this, uh let's say that uh you want to start a coffee shop. Let's make this very concrete. You remember yeah, like with Bitcoin, it was really hard uh to buy a coffee or a pizza, you know, and like you probably have regret for the rest of your life if you did that because it's worth now like $300 million.

SPEAKER_01 1:19:40

$30 million. Yeah.

SPEAKER_00 1:19:42

Um and you know, but the the thing about Ambient is that uh unlike um Bitcoin, uh you can set up a uh sort of perpetual motion machine of value creation. And in order to do that, all you need to do is make it so that uh your intelligence profit uh exceeds your intelligence costs on an ongoing basis. Uh so you know, let's look at the coffee shop. Uh I have a coffee shop, I have some physical fixed costs uh, you know, that are probably unavoidably in fiat uh at some level, uh like you know, my place and my machines. Uh but then the rest of this uh might be denominated in Ambient. And the reason for that is like your whole back office is a series of agents. Uh your accountant is an agent, um, your bookkeeper is an agent. Uh, you have a coffee supply chain agent who is negotiating uh the best prices for the best brews and you know, reading hundreds of reviews of different brews and looking at chemical analyses of you know product uh and you know, picking things that are going to align with the exact preferences of the type of people who are coming into your shop. Uh and uh you might also have a coffee barista robot. Uh and uh you know, if you're an upscale place, uh it might be conversational. And that conversation uh might be powered by ambient uh because you know people like having a fun, intelligent uh interaction uh you know when they uh go into the coffee shop. Maybe uh they're uh Barista has a particular personality and is Quippie. Uh and that's uh that's powered by Ambient. And so uh you know, we could go on and on, but uh it starts to look like the entire cost basis of your business is denominated in the machine intelligence that really powers it. And the question you find yourself asking is if that's the majority denomination, why would I do any conversion? You know, why not just deal directly in that? Uh and that's uh what Ambient aspires to be, is that facilitation, you know, that that currency that's facilitating that kind of uh direct economic exchange, not an abstraction on something, someone else's value proposition, uh, but a direct way to act upon the world using the dominant economic system of the world, which is machine intelligence.

SPEAKER_01 1:22:19

And the the robots themselves on the network between each other, these agents, it's actually an extra step for them to go from ambient to dollars, right? Like if they're talking, like why would they like they're talking, they say, Hey, I need you to do this job. It's gonna probably be this many tokens, this much time, whatever. Why would I then why would they then do the extra step? It's a small step, but why would they, it's extra?

SPEAKER_00 1:22:44

Yeah, well, it's extra, and if you do it, you suddenly become beholden to the powers that be. So payment processors are notorious uh for enforcing their will. There are whole categories of businesses that simply cannot exist under payment processing regimes. Uh and they make it hellish up until recently and still uh for uh you know people to get into crypto. Uh right, like the KYC can be ridiculous. Uh it can the whole thing can feel encumbered and uh they can rug pull you at any moment. And uh, you know, the the crypto onboarding firms just have to deal with like incredible amounts of paperwork in every country uh to be able to function. And so yeah, anytime you enter into fiat, uh it's probably bad for you. Uh and we should avoid it. Uh and I I I think Ambient enables you to do that in a world where machine intelligence is what provides most of the economic value.

SPEAKER_01 1:23:49

I don't know if it was Vitalik or Anatoly or somebody else. I apologize for not remembering whoever you are, but they said like the whole startup world under $1 transaction fees never, definitely Anatoly, probably, never happened, right? Just because of those costs. Um just cool to see what could happen. I mean, for you, it's under what, a millionth of a penny or something for these robots to talk to each other, but yeah. Um it's pretty cool. So, Travis, just to close out, I want to get your overall thoughts on um when we talked like you know, long time ago, and when we even started on the crypto AI side, there was a pretty considerable leap on this all starting to pan out, right? Like we didn't have this whole concept of global reasoning traces in a distributed manner and proving a model, right? We didn't have this notion of owning the piece of a model because everyone said there's no moats and you'll own something and it's worthless, we can copy the weights. It seems like the whole economic totality of open source decentralized crypto AI, whatever you want to call it, is really taking shape. And Ambient's sort of at the forefront of like just capturing that image. Are you is there certain aspects that you're much more confident under the economic lens of Ambient being successful for the user, for the miner, for the investor, for the person that wants to build an app? Are you feeling much better about that now than let's say a year ago?

SPEAKER_00 1:25:13

Uh honestly, yes. Um, because uh, you know, I think uh like everybody, I was wondering uh what economic leaps we would need to take in order to deliver the same value as the closed source um providers. Um and uh, you know, what we aspire to do, what we've been talking about, is to be uh the biggest and the best open decentralized provider of machine intelligence uh to everyone, uh to uh to web two, uh to web three. Uh there are gonna be lots of competitors. There are gonna be lots of different um approaches to the economics of this. Uh, but we what we believe in the beginning was true, we still think holds very true, uh, which is that uh people always need recourse uh to a big open weights model uh that is economically and functionally competitive with the closed source uh options. Uh you may reach for other tools, uh you may do lots of other interesting uh cases, um, but that bread and butter sort of needs to exist uh in order uh for uh you know the economy to function smoothly. Uh you just have to be able to reach for that. You can't um denominate yourself, you know, like a lot of the early um ex dot com agents that we were seeing, you know, you can't be using OpenAI for those things uh forever if you want to maintain credibility as a decentralized uh solution. Um but overall, like I think yes, we were all probably very early to this trend. Um and there were a lot of uh leaps of faith. And uh, you know, uh much credit to you guys on the uh the Delphi uh side. Um I felt like we were speaking the same language almost immediately, like five seconds into the conversation, we were reading the same things, we were uh using the same vocabulary. Um it felt very fluent. Um and I think that uh we're still early, and that for everyone who's getting into this, uh you know, it's not too late actually, uh, to come on board. This is the this space is the most fun, and I think it's gonna just continue to be the most fun, uh, probably for like 20 years. Uh so yeah, join us.

SPEAKER_01 1:27:43

No, I'm I'm totally with you. Travis, what's the best way for you know, somebody listen to this, they really agree with your view, they agree with Ambient. Like, if they're a hardware provider, if they're a tech genius, if they want to leave OpenAI and come over, like what's the best way for everyone to get involved with Ambient? Ambient.

SPEAKER_00 1:28:00

Yeah. Well, I'll answer that in part. So for uh the folks who want to develop applications uh on ambient or who want to even develop uh you know applications on another chain that need a really good verified inference solution uh that's cost competitive with other providers, like please uh you know send me send me a message. Uh if you look at our website, which is uh ambient.xyz, uh we've got an email address, uh hello at ambient. Uh and uh we read all those emails. We've responded to hundreds of them in the past uh few days uh in the wake of the announcement. So you know, builders are uh welcome. Uh if you're someone who has been sitting in big tech purgatory uh and you would like to join uh this this side of uh the equation and help balance out uh some of the oligopolistic uh tendencies uh that exist there, uh we welcome you. Uh we love research collaborators, uh, we love uh skilled programmers and machine learning experts. Uh you know, we're a bunch of hardcore techies, and uh you know, we we respect fellow hardcore techies. Uh so you know we think you're gonna be in good company. Um if you are someone uh who you know feels the same way that we do, uh that something like ambient uh is necessary, uh, you know, we would ask you to uh help advocate for us. Uh you know, we're uh in the scheme of things like pretty small. Uh we're just at the beginning of this. Uh and it can make a huge difference to us uh if you support us, uh if you talk about uh Ambient's goal, uh which we think is returning to the roots of crypto and delivering on the promise of a world currency. Uh if you if you believe that also, uh we appreciate your advocacy and your public support because that's the only way uh that we can build critical mass and a movement to counterbalance uh some of the other forces uh in the world. Um and you know, we we want to interact with you more uh as a community. We've we've just come out of stealth and we want to build this with you. We want to build something that serves your interests and a currency that ultimately serves the world's interests instead of the interests of a few.

SPEAKER_01 1:30:32

Thanks so much, Travis. It it it's just um it's just such a more open vision, right? Like not investment advice, but if I have a couple of bucks abroad and I want to invest, or if I want to build an application, or if I want to do a fine-tune of your foundational model, which you really can't do with a closed source model like that, like the whole stack is just open for every value creator to come in, which is just huge. So, Travis, thank you so much for coming on the show for the third time and thanks, Raven and so as investors. We're extremely excited for uh for Ambient.

SPEAKER_00 1:31:04

Thank you so much, Tommy. I as always really appreciate uh the perceptive questions uh and your understanding of the space. Thank you so much, Travis.

SPEAKER_01 1:31:14

See you soon.