Hey Herman, did you catch that news story Daniel was mentioning about the United States military using Claude? It feels like we have hit a new milestone when the Pentagon starts picking their favorite large language models for field operations. We are sitting here in February of twenty twenty-six, and it feels like the line between silicon valley and the department of defense has basically evaporated.
I did see that, and it is absolutely fascinating. Herman Poppleberry, at your service, by the way. It is a bit of a surreal moment, right? You have this model, Claude, which is built by Anthropic—a company that practically branded itself on A-I safety, constitutional principles, and a certain level of academic detachment—and now it is being deployed in high-stakes military contexts. We are talking about integration into Palantir’s platforms and running on A-W-S Secret Clouds. It really highlights the tension between the idealistic, safety-first roots of these A-I labs and the cold, hard reality of global geopolitics.
Exactly. And Daniel’s prompt really gets to the heart of the technical mystery here. He was looking at OpenRouter, which is a great tool for anyone who wants to toggle between different models, and he noticed something that I think a lot of people overlook. When you use a closed-source model like Gemini from Google or Claude from Anthropic through a third party, you are not necessarily talking directly to the company that made the model. You might be talking to an inference provider like Amazon Web Services or a specialized provider like Together A-I or even DeepInfra.
Right, and that is where the brain starts to itch. If these models are the crown jewels of these companies—worth tens of billions of dollars and protected by layers of legal and digital armor—how are they just handing them over to third parties? Daniel was joking about someone walking over with a hard drive, but in a weird way, he is not that far off from the physical reality of how data has to move. The question is, how do you hand over the keys to the kingdom without the other person being able to change the locks? Or worse, just making a copy of the keys and selling them on the black market?
That is the perfect way to frame it. Today, we are going to pull back the curtain on the world of third-party inference. We will look at how a model goes from a research lab to a server rack in an Amazon data center, how the licensing works, and most importantly, how they stop people from just stealing the weights and running away with the secret sauce.
It is a massive topic because it touches on everything from low-level hardware security to the way the entire A-I economy is being structured right now. It is not just about the code anymore. It is about the compute, the physical custody of the weights, and the cryptographic trust that binds it all together.
So, let us start with the basics for anyone who might be a bit fuzzy on the terminology. When we talk about an inference provider, what are we actually talking about? Because I think most people assume that when they type a prompt into a box on the Claude website, it goes to a computer owned by Anthropic, and that is the end of the story.
Right. To understand this, we have to separate the life of an A-I model into two phases. Phase one is training. This is the Herculean task. It takes thousands of G-P-Us, months of time, and hundreds of millions of dollars in electricity and talent. During training, the model "learns" by adjusting its internal parameters. By the end, you are left with a massive file—or a collection of files—called the "weights." These weights are essentially a giant matrix of numbers, sometimes hundreds of billions of them, that represent everything the model knows.
And that file is the "brain" of the A-I.
Precisely. Now, phase two is inference. This is the act of actually using those weights to generate a response. When you send a prompt, the computer takes your text, turns it into numbers, and runs those numbers through the weights using a series of massive mathematical operations. Out comes the response. Now, here is the catch: while training is a one-time cost, inference has to happen every single time a user hits "enter." If you have ten million users, you need a massive amount of hardware running twenty-four seven just to handle the math.
And that process requires a lot of compute power, but not nearly as much as training, right?
Per request? No. But at scale? It is a logistical nightmare. If you are Anthropic, you are a research lab. You are great at math and safety and data science. But do you really want to be in the business of managing global server infrastructure? Do you want to be the person who has to worry about the cooling systems in a data center in Frankfurt or the fiber optic latency in Singapore? Probably not. That is where companies like Amazon Web Services, Microsoft Azure, and Google Cloud come in. They are the landlords of the internet. They have the racks, they have the power, and they have the global reach.
So it is a division of labor. Anthropic builds the brain, and Amazon provides the nervous system and the body to house it. But this brings us to Daniel’s big question. If Anthropic gives those weights to Amazon so they can run them on their servers, what is stopping an engineer at Amazon from just copying that file? If I have the weights for Claude three point five Sonnet, I basically have the model. I could start my own company called Corn-A-I and run the exact same model for half the price because I didn't have to spend the five hundred million dollars to train it.
That is the nightmare scenario for these labs. It is called "weight exfiltration." And it is why the mechanics of these partnerships are so tightly controlled. It is almost never a case where Anthropic just sends a download link to an Amazon engineer and says, "Hey, put this on a thumb drive and plug it into the server." Instead, they use something called "managed services" or "containerized deployments" with very specific hardware locks.
Okay, let us dig into that. How do you actually lock a file so that it can be used to do math, but it cannot be read or copied by the person holding the hardware?
This is where we get into the world of "Confidential Computing." One of the primary ways this happens is through something called a Trusted Execution Environment, or a T-E-E. Think of it like a high-security black box inside the processor itself. When Amazon runs Claude on their "Bedrock" service, they are often using specialized hardware like A-W-S Nitro Enclaves.
Nitro Enclaves? That sounds like something out of a sci-fi movie.
It is pretty close! Essentially, it allows the model owner—Anthropic in this case—to package their model weights in an encrypted format. These weights are sent to the server, but they are never decrypted in the server's general memory. Instead, they are loaded into this isolated "enclave" inside the C-P-U or G-P-U. The processor decrypts them inside this secure memory space, performs the inference math, and then spits out only the final text response. Even if you have "root access" to the server—even if you are the lead engineer at Amazon—you cannot see what is happening inside that enclave. The memory is encrypted at the hardware level.
That is fascinating. So, the hardware itself is the guarantor of the trade secret. It is not just a legal agreement; it is a physical barrier.
Precisely. And it goes a step further with something called "Remote Attestation." Before Anthropic’s software releases the decryption key to the server, the server has to "prove" its identity. It sends a cryptographic signature that says, "I am a genuine, unmodified A-W-S Nitro chip running the exact version of the firmware you expect." If the signature doesn't match—if someone has tried to tamper with the hardware to intercept the data—the key is never sent. The weights remain a useless pile of encrypted gibberish.
So when Daniel sees Claude on OpenRouter, and it says the provider is "A-W-S Bedrock," there is this whole invisible handshake happening where the hardware is proving to Anthropic that it is a safe place to live.
Exactly. And this is why you see such tight integration between the model builders and the hardware providers. When Anthropic partners with Amazon, they are often optimizing for specific chips like the Trainium or Inferentia chips that Amazon designed in-house. These chips are built from the ground up with these security protocols in mind. They are designed to run these specific types of workloads—massive matrix multiplications—while keeping the model weights opaque to the host system.
But what about the smaller providers? Daniel mentioned that OpenRouter shows different upstream providers. Some of those are big names like Amazon, but others are smaller, more specialized inference labs like Together A-I or Fireworks. Do they all have this high-level hardware security?
That is a great catch. The answer is: it depends on the model. For a "closed-source" model like Claude or Gemini, the lab usually only licenses it to a very small number of "Tier One" partners who can prove they have that level of hardware security. You won't find the raw weights for Claude Opus sitting on a random small-time provider's server. If a smaller provider offers Claude, they are almost certainly acting as a "proxy" or a "reseller."
Oh, I see. So they are just a middleman.
Right. They are basically just passing your request along. You send your prompt to the small provider, they send it to A-W-S Bedrock via an A-P-I, get the answer, and pass it back to you. They might do this to offer a simpler billing interface or to bundle it with other models. But the actual "inference"—the math with the weights—is still happening in that secure Amazon vault.
That makes sense. But let us go back to the military example Daniel brought up. If the United States military is using Claude, they are probably not just using the standard public A-P-I that you or I would use. They are likely using a completely isolated instance.
Oh, absolutely. This is a huge part of the business model now. The military uses what are often called "air-gapped" or "sovereign" clouds. Amazon and Microsoft both have specific regions—like the A-W-S Secret Region or the Top Secret Region—that are physically separated from the public internet. They have their own power supplies, their own fiber lines, and they are located on guarded military installations.
So in that case, Anthropic is actually allowing a copy of Claude to be "installed" inside that high-security fence?
Yes, but the same rules apply. Even in a military bunker, the weights are likely running inside those Trusted Execution Environments. The trust is multi-layered. Anthropic has to trust that the military and the cloud provider have the physical protocols to ensure the hardware isn't tampered with, and the military has to trust that the model doesn't have any "phone-home" features.
Wait, "phone-home" features? You mean like the A-I secretly sending the military's prompts back to Anthropic headquarters?
Exactly. That is the military's biggest fear. They are worried about "data leakage." If a general asks Claude to "analyze the troop movements in this specific sector," they cannot have that prompt leaving the secure environment. This is why these deployments use a "Data Plane" versus "Control Plane" separation. The "Data Plane"—where your prompts and the model's answers live—is totally isolated. Anthropic never sees it. The "Control Plane" is just for updates and telemetry—basically telling Anthropic, "Hey, the model is still running and it has processed a million tokens, so send us a bill."
This really changes the way I think about "closed source." We usually think of it as a binary. Either everyone can see the code, or only the creator can. But what we are describing is a tiered system of access. Amazon gets to "host" it but can't "see" it. The military gets to "own" a copy but can't "modify" it. And the original lab still holds the ultimate copyright.
It is more like a franchise model, honestly. Think of it like McDonald's. McDonald's corporate has the secret recipe for the Big Mac sauce. They don't just put it on the internet for everyone to see. But they do ship it to thousands of franchisees in sealed, branded canisters. Those franchisees have the sauce, they have the equipment to serve it, but they are legally and technically bound not to reverse engineer it or sell it under a different name. They are "inference providers" for the Big Mac.
That is a great analogy. But here is where I want to push a bit deeper. In the software world, we have seen people reverse engineer things for decades. If I have the weights, even if they are in a secure enclave, can't I perform a "side-channel attack"? Can't I watch the power consumption of the chip or the timing of the responses to figure out what those numbers are?
You are thinking like a true security analyst, Corn! And the answer is... theoretically, yes. Side-channel attacks on neural networks are a very real and very scary area of research. Researchers have shown that you can sometimes reconstruct parts of a model by analyzing the electromagnetic radiation coming off a chip or the tiny fluctuations in how long a calculation takes. If the chip takes two microseconds longer to process a "zero" than a "one," and you measure that a billion times, you can start to map out the weights.
I knew it! So the "black box" is not perfectly black.
It is not. But here is the thing: the scale is our friend here. These models are unimaginably vast. We are talking about hundreds of billions of parameters. To reconstruct a model like Claude three point five or the rumored Claude four through side-channel attacks would be a task of such monumental complexity that it is practically impossible with current technology. It would be like trying to reconstruct the entire blueprint of a skyscraper by watching the shadows it casts on the ground over the course of a year. You might get the general shape, but you are not going to get the plumbing or the electrical wiring.
That puts the scale into perspective. So the sheer size of the model acts as its own form of security. It is "security through obscurity" but on a mathematical scale.
Precisely. And there is another layer here that Daniel touched on, which is the idea of "model theft" versus "model distillation." You don't actually need to steal the weights to steal the "intelligence" of a model.
Oh, right. This is where you use a smaller, cheaper model to "learn" from the big model. I have heard about this. You feed the big model a million questions, see how it answers, and then train your small model to mimic those answers.
Exactly. That is called "distillation," and it is a huge concern for companies like Anthropic and OpenAI. It is much easier to do than stealing weights from a secure server. If I have an A-P-I key, I can just hammer the model with questions and use the outputs to "fine-tune" a cheap, open-source model like Llama three. This is why these companies have very strict terms of service that forbid using their A-P-I to train other models. They also have automated systems—A-I "bouncers," if you will—that look for patterns of usage that look like someone is trying to "scrape" the model's brain.
So, if I start asking Claude a series of very specific, repetitive questions designed to map out its logic, their system might flag me and cut off my access.
Precisely. It is a constant game of cat and mouse. They are protecting the weights at the hardware level, the data at the network level, and the "intelligence" at the behavioral level. It is a defense-in-depth strategy.
Let us talk about the licensing side for a second. When Daniel sees those third-party providers on OpenRouter, how is the money actually moving? Because I imagine it is not just a flat fee.
It is almost always a "revenue share" or a "per-token" royalty. If you are a provider like A-W-S Bedrock, you are charging the customer for the compute and the model access. Let's say it costs one dollar for a million tokens. A portion of that dollar goes to Amazon to cover the electricity, the hardware depreciation, and their profit margin. The other portion—the "royalty"—goes back to Anthropic.
It is like a digital tax. Every time you generate a sentence, a fraction of a cent is flying back to the lab that trained the model.
Exactly. And this is why the "provider" matters so much on a site like OpenRouter. Different providers might have different margins, or they might have different optimizations. One provider might have found a way to run the model ten percent more efficiently on their specific hardware cluster, so they can offer it at a slightly lower price while still paying Anthropic their full royalty.
That explains why you see price variations for the same model. It is not just about the model; it is about the efficiency of the "factory" running it.
Right. And some providers might offer better "throughput" or lower "latency." If you are a high-frequency trading firm or a real-time translation service, you might be willing to pay a premium for the provider that has the fastest connection to the backbone of the internet, even if the model itself is identical.
I want to go back to the military aspect because I think it is a really important edge case. When the military uses these models, they often have requirements for "determinism." They need to know that if they give the same input, they get the same output every single time. Does third-party inference make that harder?
It can. Determinism is actually quite difficult with these massive models, especially when they are running on distributed hardware. Tiny differences in how a specific G-P-U handles floating-point math—the way it rounds off very small numbers—can lead to slightly different results. If you are Anthropic, you have spent a lot of time tuning your software stack to be as consistent as possible. When you hand that over to a third party, you have to ensure that their "stack"—the drivers, the libraries, the math kernels—is identical to yours, or the model might start "hallucinating" in slightly different ways.
That sounds like a nightmare for quality control. You are not just licensing a file; you are licensing an entire environment.
That is exactly what it is. It is often delivered as a "container," which is a piece of software that includes everything the model needs to run—the weights, the libraries, the specific versions of the math tools. It is like a pre-packaged kitchen. You just plug it in and it should work exactly the same way every time.
But wait, if it is a container, can't I just "look inside" the container?
Not if it is an "encrypted container" designed to run on a "Trusted Execution Environment." We are back to that hardware lock. The container is encrypted, and only the specific, verified hardware has the key to open it. It is a very sophisticated "keep out" sign.
It is amazing how much of this comes down to the chips themselves. It feels like the power in the A-I world is shifting. It started with the researchers who could build the models, but now it feels like it is moving toward the people who control the "secure silicon."
You are hitting on a major trend for twenty twenty-six, Corn. This is why you see NVIDIA's stock price where it is, and why Amazon and Google are racing to build their own A-I chips. It is not just that they make fast chips; it is that they are building the "security architecture" for the future of the global economy. If every major company and every major military is going to be running their operations on these models, the hardware that hosts them becomes the most important real estate on earth.
So, let us look at the practical side for a minute. If I am a developer or a business owner, and I am choosing between using Anthropic's direct A-P-I or using a third-party provider via something like OpenRouter or A-W-S, what should I be thinking about in terms of data security?
That is a great question. The first thing is to look at the "privacy policy" of the provider, not just the model maker. If you use Claude through A-W-S Bedrock, your data is governed by Amazon's security agreements. For a lot of big enterprises, that is actually a plus. They already trust Amazon with their databases and their websites, so adding A-I to that existing agreement is easy.
Right, it is the "nobody ever got fired for buying I-B-M" logic.
Exactly. But if you are using a smaller, more obscure provider because they are cheaper, you have to ask yourself: what are they doing with your prompts? Are they logging them? Are they using them to "distill" their own models? This is why transparency from platforms like OpenRouter is so important. They tell you who is actually running the model so you can make an informed choice.
And what about the risk of the "middleman" going down? If I build my app on a specific inference provider and they have a server outage, my app dies, even if Anthropic's own servers are perfectly fine.
That is the big trade-off. You are adding a link to the chain. More links can mean more points of failure. But it can also mean more redundancy. One of the cool things about the ecosystem Daniel mentioned is that you can build your app to automatically switch providers. If Provider A is slow or down, your code can instantly flip over to Provider B. It is like having multiple gas stations on the same corner. It actually makes your app more resilient if you set it up correctly.
That is a huge advantage for reliability. It is almost like a decentralized version of a centralized model.
It is! It is a "market-based" approach to inference. And it is only going to get more complex as we see more "specialized" models. We might see providers who specialize in "low-latency" for gaming or "high-security" for healthcare.
I want to touch on one more thing from Daniel's prompt. He mentioned "Claude Opus" and the evolution of these models. As of our discussion today, we are seeing the three point five series dominate, but the infrastructure to deploy the next generation is already being built.
That is the secret to the speed of this industry. The "deployment pipeline" is being built in parallel with the "research pipeline." When Anthropic finishes training their next flagship model, they don't then start a six-month project to figure out how to put it on Amazon. The "hooks" are already there. The secure enclaves are ready. The licensing agreements are signed. They can basically flip a switch and have it available to millions of developers in a matter of days.
It is a well-oiled machine. But it also feels a bit fragile, doesn't it? If a major vulnerability is found in the hardware security of a specific chip—like a new version of the "Spectre" or "Meltdown" bugs we saw years ago—could that lead to a "mass leak" of all these closed-source models?
That is the "black swan" event for the A-I industry. If someone finds a way to bypass the encryption on the latest NVIDIA or Amazon chips, the world's most valuable intellectual property could be copied onto the internet overnight. It would be the biggest "heist" in history, and it wouldn't even involve a physical robbery. It would just be a stream of bits flowing out of a secure enclave.
That is a terrifying thought. It makes you realize why these companies are so obsessed with "red teaming" and security audits. They aren't just worried about the model saying something offensive; they are worried about the model itself being stolen.
It is a high-stakes game. And as the models get more powerful—as they start to be able to write their own code and design their own hardware—the security around them will only get tighter. We are moving toward a world of "Fortress A-I," where the most powerful models live in highly guarded digital vaults.
So, to wrap up this part of the discussion, third-party inference is the bridge between the "ivory tower" of A-I research and the "real world" of commercial and military application. It is made possible by a complex mix of hardware-level encryption, strict legal licensing, and a multi-tiered ecosystem of providers.
And it is what allows a small startup or even a government agency to use the world's most advanced technology without having to build a billion-dollar data center themselves. It is the "democratization" of compute, but with very, very high walls around the secrets.
I think we have covered a lot of ground here. Let us take a step back and think about the practical takeaways for our listeners. Because this isn't just "tech trivia"—it actually affects how you should build and use A-I tools.
Absolutely. The first takeaway is: know your chain. If you are building a product that handles sensitive customer data, you need to know not just which model you are using, but who is running the inference. Are you okay with your data passing through a third-party provider? Often the answer is yes, but you should know who they are.
Right, and check if they have "zero-retention" policies. A lot of the big providers now offer "opt-out" for data training, which is crucial for business use.
Second, consider redundancy. If you are using an aggregator like OpenRouter, take advantage of the ability to switch providers. It is a great way to protect your app from the "single point of failure" problem.
Third, don't assume "closed source" means "invisible." As we talked about, there are ways to "distill" or "reconstruct" models behaviorally. If you have a secret sauce in your own business, be careful about how much of it you "reveal" to the model in your prompts, because that information could theoretically be learned by the system over time, even if it is not "stored" in the traditional sense.
That is a really important point. The model is a "learning machine," and every interaction is a potential data point. Even with all the security we talked about, the best way to keep a secret is still not to tell it to anyone—even an A-I.
And finally, for the tech-curious out there, keep an eye on the hardware. The next few years are going to be a battle between chip makers to see who can provide the most "trusted" environment. That is going to be just as important as who has the fastest floating-point operations.
Well said. It is a fascinating intersection of physics, math, and law.
I really enjoyed digging into this. It is one of those topics that seems dry until you realize it is basically a high-tech spy novel happening in real-time.
Exactly! With billions of dollars and global security on the line. What more could you want?
Before we go, I want to say a huge thank you to Daniel for sending in this prompt. It was a great catch, looking at that "provider" tab on OpenRouter. It is those little details that often reveal the biggest stories.
Definitely. It is a great reminder to always look under the hood.
And to our listeners, if you are finding these deep dives helpful or if they are sparking new questions for you, we would love it if you could leave us a review on your podcast app or on Spotify. It really does help the show reach more people who are curious about these "weird prompts."
Yeah, it makes a huge difference. We love seeing the community grow.
You can find all our past episodes—we have over six hundred and fifty of them now—at our website, myweirdprompts.com. There is a search bar there, so if you want to see if we have covered a specific topic before, that is the place to go.
And if you have a prompt of your own, there is a contact form there too. We are always looking for the next "rabbit hole" to jump down.
This has been My Weird Prompts. I am Corn.
And I am Herman Poppleberry.
Thanks for listening, and we will catch you in the next one.
Goodbye everyone!
