Claude Mythos leak signals a major AI capability step change — and why your current AI stack may need rebuilding
Nate B Jones of AI News & Strategy Daily discusses the leaked Claude Mythos model from Anthropic, what it signals about the next generation of AI capability, and how individuals and organizations should restructure their AI workflows before it releases.
Summary
Nate B Jones opens by describing the leak of Claude Mythos — Anthropic's next flagship model, reportedly the first trained on Nvidia's GB300 chips and given a new lineage name ("Capybara") — as a genuine inflection point rather than an incremental update. He highlights that security researchers have already demonstrated Mythos finding zero-day vulnerabilities in widely-used open-source software that experienced human researchers had missed, causing cybersecurity stocks to drop five to nine percent on the news alone. The core argument of the episode is that when models make a step-change leap in capability, the correct response is to dramatically simplify your AI stack — stripping out procedural scaffolding, over-specified prompts, and human handoffs that were only ever compensating for model limitations. Jones walks through four specific areas likely to break when Mythos arrives — prompt scaffolding, retrieval architecture, hardcoded domain knowledge, and evaluation design — and closes with a framework for what a "Mythos-ready" system actually looks like.
Key Takeaways
FULL TRANSCRIPT
Claude Mythos: What the Leak Means
Nate B Jones: There are moments in AI when everything changes, and we just had one of those in the last few days. Claude Mythos leaked.
Claude Mythos is the first model, as far as we know, that has been trained on Nvidia's new GB chips. It is a massive model. It is a step forward. Anthropic has confirmed its existence and given it a new lineage name — so it won't be called Sonnet, it won't be called Opus. It appears to be called Capybara. I don't know why we've switched to furry animals, but here we are.
This is the biggest model in the world by most measures, and it is going to be the most powerful model in the world. But don't just take my word for it, and don't just take Anthropic's word for how powerful it is. Look at what security researchers themselves are saying. Security researchers are saying that Claude Mythos is terrifyingly good at finding vulnerabilities in your own infrastructure — better than a human.
In fact, one of the most experienced security researchers in the world stood up at a conference in San Francisco in the past few days and said that Claude Mythos immediately found zero-day vulnerabilities in Ghost, which is a 50,000-star GitHub repo that has never had major issues before. As soon as Mythos was let loose on it, it found a bunch of issues that even the world's best security researchers hadn't found.
This is why Anthropic is taking the unusual step of allowing security researchers to dig into Mythos — to battle-test it against some of the most popular utilities on the internet and harden up their defenses ahead of time. Because as soon as Mythos is released, it is going to be able to act as a threat to any IT repo out there and identify vulnerabilities that even good security researchers haven't been able to find. Which immediately suggests that the first thing you should do, if you are in IT or security, as soon as Mythos comes out, is to say: let's battle-test it against our own systems and see what vulnerabilities it finds. That is job number one, day zero. You've got to do that.
Why This Is an Inflection Point
But let's go beyond that. Let's look at what Claude Mythos means. I want to be really honest with you. This model is one of the inflection points in 2026 that we all need to pay attention to. This is a chance to catch up before the train leaves the station.
I know you may hear from me that things are getting faster — things are getting faster a lot. But that's what it's like when you're on an exponential curve. Things keep getting faster. And I'm here to tell you: this moment, before Mythos releases — and it may release as soon as next month or the month after — this moment is your chance to get things figured out before it arrives and upends everything. And I don't just mean about security. I mean how you build stuff, how you prepare.
Why is that? When models get bigger, they force you to simplify. They force you to think: what can I delete about my systems and my practices, because the model can do so much more now that it couldn't do before? That is what we have to understand. That is what we would call the bitter lesson of building with LLMs.
We as humans think we have a lot of value to add to these models. We can add our judgment. We can complexify. We can add a lot of scaffolding and systems around these models and it will make them better. And as they get more powerful, the bitter lesson is that simpler works best.
So I'm going to go through, in this video, some of the things you should be checking ahead of time. But there's a larger thing you should take away, which is simply: be sure that you are taking the idea that the LLM can do a lot more seriously. We'll cover Mythos when it comes out. But for now, as we go through the next few questions, as you audit yourself, as you think about whether you're ready for Mythos, that's the larger lesson.
What are the specific questions that help you know if you're ready for a big model change? If you're ready for effectively a step change? Models come out all the time, but step changes are more rare. What we get when we do a pre-training run on GB300s — much larger and more powerful underneath — is the scaling law in effect. Yes, there'll be another model along from somebody in the next week or so, but until Mythos comes out, and until there are other models in that lineage from other model makers that are also big and also trained on GB300s, those are the step changes we're going to see in the first half of the year.
You need to learn to differentiate between the models that are 5, 10, 15% better and the models that are significantly better — a step-change better. That's why this is a big deal. We are going to have another lurch up where the models get significantly stronger in the next few months, and you need to get ready for that now.
The Four Things Most Likely to Break When Mythos Arrives
Here are four specific things to look at that are going to likely break when Claude Mythos comes out. If you are building in AI at all, this is going to come up.
Number one: your prompt scaffolding. How you think about prompting to drive results. Ask yourself — and this is not a per-prompt document question, this is a per-line question, really dig in — is this instruction here because the model needs it, or is it here because I needed the model to need it?
Anthropic's recommendation is very unambiguous: consider adding complexity only when it demonstrably improves outcomes. And this is true not just for Anthropic models. OpenAI's Codex guide tells us something similar when it says just tell it what you need without writing long instructions. We are going to more and more be asked to communicate to the model what and why we need something in the end, and less and less about how.
For example, let's say you have a customer support agent with a 3,000-token system prompt and half of it is procedural — first classify the intent, then check your response for hallucinated URLs, then do X, then do Y. That sequence was written down because the model would skip knowledge checks enough of the time that it was needed. Think about whether you're really going to need that. When a model gets two or three times smarter, you may be able to delete 30, 40, 50% of that 3,000-token prompt because so much of the procedural is just not needed when the model is more intelligent.
Let's say you're not building agents. What's the implication for you if you're just chatting, if you're using a co-worker AI and you're wanting to get work done and you're less technical? It's pretty simple. Ask for what you want in the end and explain why in plain language. You don't need to elaborate on how to get there. As long as the model has access to the inputs it needs, the data it needs, it's going to get there on its own.
Increasingly across 2026, this is the bitter lesson we have to learn. All of the ways we have described process — the things that are precious to us, associated with our ability to execute work in a certain series of steps — we've decided that's an important reflection of our work identity. What Claude Mythos and similar models are going to teach us is that that doesn't matter anymore. What matters is the outcome and our ability to name the outcome and let go of the process. You've got to let go of the process with these models.
And that also goes for when you're auditing. Let's say you have a business process that you thought you couldn't make better. I would bet you that if you give it to Mythos, or maybe you give it to Andrej Karpathy's Auto Research, you're going to find ways to make it better that a human can't figure out. The LLMs are now better than us at finding efficient ways through process, and we should stop overspecifying. So that's point number one. Check your prompts. Stop overspecifying.
Lesson number two is about retrieval architecture and memory. You're basically asking: how is the model changing the way it relates to memory based on the model's gain in capabilities? Another way of looking at it is: in the past, we had to carry a lot of the logic for retrieval on our side so the model would retrieve correctly. But really, how much of my retrieval logic should belong to the model if the model's smarter?
This is a much more nuanced take than takes that say RAG is dead. I've heard people beating on retrieval-augmented generation a lot. Think more broadly. If you're in a large context window situation — a million tokens, 10 million tokens, 100 million tokens — you should increasingly start to think about how the model wants to handle retrieval for that situation and how the model thinks it can handle it efficiently. And you should think less about predetermining how all of that works.
There are going to be pieces you have to decide from the get-go. You'll have to decide what goes into the initial context window. You're going to have to decide whether the model is going to be invited to look at particular repos. But once you've made those initial decisions, a lot of the rest of it with these very powerful models essentially relies on you being able to present a really well-organized, searchable repo of some sort — maybe it's documents, maybe it's code, maybe it's the file system on your computer if you're non-technical. And then you need to say: you go ahead and have a look. You look for what you want. And you need to trust the model to find what it needs to find.
And there again you see the theme. The theme is we have to let go so the model can do more.
If we want to take advantage of the power, one of the job skills that is hard to measure in 2026 is the ability of someone to see a new model coming like Mythos and to say: I can see in advance, I understand how to model the improvements that a new intelligence is going to bring to the table, and I'm going to change my workflow. I'm going to be ready so that I cut down the prompt where I need to. I'm going to be ready so I adjust my retrieval strategy and I'm not overspecifying retrieval. I'm going to let the model pick what it wants to pick out of this file system, out of this repo.
Because that is where we need to go. We need to assume that the model is increasingly going to be better than we are at deciding what to put in its context window — if we can specify where it should go, if we can have a directed prompt, and if we understand with the model what we want to accomplish. Our goal increasingly is to say: here is the goal, go get it done — and then to measure success. That's it. That is the goal. And the smarter the model gets, the more our work resolves down to that. We need to get out of the way.
And in my observation, that is really hard. That's why it's called the bitter lesson — because we humans like to think that our contributions here are special. That we have a way of doing scaffolding. That we have a way of doing RAG. That our special system prompt matters. No — let it go. That was for that moment. That was for that model. We need to start thinking increasingly about how we put these models at the heart of our workflows in ways that allow them to do their most powerful work in a direction we set, in alignment with where we want to go. Our job is to point them in that direction very clearly.
So retrieval architecture is another example of that. You just have to be very clear: here are the resources you can access, here is the goal — and increasingly the model is going to figure that out.
Now, do I have specific advice for you because Mythos is already out in the world and I've tested it against 20 different things? I do not. We will get to that when it comes out. But I do have very high confidence that Mythos is going to be much better at intelligently filling its context window than previous models, because that is exactly what you get with the scaling law. When you get more intelligence, you get better at using your context window. This is going to be another jump in that direction.
And it is a good chance to remember one of the big lessons we should have learned over and over again over the last two years: as models improve, this stuff gets simpler.
Number three: look at how much you have to hardcode domain knowledge versus how much the model can infer. When you're thinking about things you want to emphasize over and over again to a model, ask yourself: which of these business rules did I write down because the model could not infer this from context, and which of these can I actually let go of?
This is true for non-technical folks when we look at how we work with AI co-workers as well. How often are you writing down "this is what I do, this is my role" — maybe you have a saved prompt for that, or maybe you have it in memory somewhere? You're increasingly not going to need to do that because the model is going to infer it from the context you give so reliably that you'll be fine.
Another example: let's say you have a house style for writing your client reports. The model can just infer that at very high fidelity from a given example report. That's the whole point of the scaling law — intelligence gets better at reliably answering your query. A query about constructing a report will be more reliably answered in the voice of that example. So you should count your rules. Count the things you have to remind the model of. Ask yourself: do you really need to fill the token window for better models with this? Be prepared to let go of some of that.
And look, this is also hard for me. This is not something I'm saying isn't hard for all of us. I had an example recently where there was a prompt I was using around how I do research. I'd been using it for a couple of model generations. One day I forgot to put the full ten-line prompt in and I just put a one-liner and said "go research" — and I got a better result back. Because the ten-line prompt was more detailed about methodology than it needed to be and was over-constraining my model. It had hardcoded my domain knowledge about what resources to look up. That had been really good two model generations ago. And now I needed to let it go. I needed to just let the model go find the right resources and come back with a research report.
So as much as the art of prompting for the first couple of years of LLMs was about what you put in, increasingly the art of prompting is about what you leave out. And it's still an art. It's still hard. You still have to prompt. The skill is valuable. It's just that the skill is evolving because the models are getting better.
Question number four is all about: did the model do what we asked it to do? Can we sniff-check it and show that it worked?
If you're doing non-technical work, the answer is going to be increasingly clear. You're going to look at it, say "wow, that looks really good," and move on. A lot of people are going to do that. And the art of checking non-technical work is increasingly going to involve you having a very high standard. One of the challenges of working with good models is learning to make sure our bar is really high. Don't be afraid, just because this model is really good, to look at something it produces — like a PowerPoint deck or an Excel — and say "this isn't quite right. You got 99% of this right, but this is the 1% I want fixed." Apply your high standard. That's how we get good work. That's how we don't pass slop on.
But if you're building software, this is one of those things you have to apply a lot of judgment to, because 99% and verification checks in software is a very different game from 85% in verification checks. And we are in a world now where we are closer to 99% right more of the time than we are to 85%. And Mythos is just going to push us more in that direction.
Which suggests to me that we need to be really smart about writing our evals. We are moving toward a point where we want one eval gate at the end of the software process, and it needs to check absolutely everything and send things back when it doesn't work. Because if we do intermediate evals along the way, net, there's enough right about what these systems build that it's just not worth it. You don't want to mess around with a more complicated pipeline. Simplify, simplify. Just write the eval at the end. And when you write the eval at the end, make sure that it tests absolutely everything — your functional requirements, your non-functional requirements. And then make sure, when it comes back, that if you read that eval script and all the tests pass, you're confident it's good.
Because increasingly we need to be at a point where we're not the ones who are the bottleneck for telling whether a particular piece of software reliably calls dependencies, whether the code is clean and has appropriate exception handling, whether edge cases are handled. All of that needs to go into automated evals, or else we're going to be overwhelmed by the number of pieces of software we can produce and the amount of checks we have to do.
There are already conversations being had in San Francisco around the fact that humans cannot review all the code, and we have to find ways to start to scale out of ourselves. We are the bottleneck. Mythos is going to make that worse. If you are depending on humans and human handoffs as a key part of your agentic software development pipeline, you're in trouble.
And there is an analogy there for non-technical folks. If you are depending on humans as a handoff between PowerPoint and Excel and you're doing AI for both of those pieces, you should be looking seriously at whether you can automate those non-technical artifact handoffs, because Mythos can help you do that.
And I'm not talking about Mythos as if this is the only model that will ever be able to help you with this. I'm saying it is the first leaked model of a new class. We're going to see similar models come out from other hyperscalers. Google will have one. OpenAI will have one. They will also probably drop them in the next couple of months. We will all, together, regardless of the underlying model we use, be in this new world — and we need to think about that.
Cost, Access, and the Premium Plan Question
Do you know another reason why we need to think about simplicity? These are not cheap models. Anthropic has basically confirmed that these are going to be very expensive models to run. You want to be very efficient with them. You want to make sure they're using tokens as efficiently as possible. You don't want to clutter them up with a bunch of human-described process. You want to use them as effectively as you can.
I am willing to bet that Mythos, when it launches, is only going to initially be available for max plan users for Claude, because it's so expensive to serve. And we are headed toward a world where increasingly the first and best models are only going to be available on those premium plans because of how expensive they are to build, train, run, and serve.
So we need to ask ourselves: am I in a position where I can invest in intelligence — in one of these plans — in order to get access to this, and then leverage it to the hilt and make it worth it? You can do this now. You don't have to wait for Mythos. Just tell your AI: look at my recurring subscriptions for my household and find me $200 in savings. Most households in America can find $200 a month in savings somewhere from their subscriptions, just because the subscription economy latches onto our credit cards like barnacles and we just need to find some space. LLMs right now, without waiting for Claude Mythos, are already very good at that.
Think about the kind of intelligence you want to purchase and the timeline you want to buy it on, because the people who are able to get Claude Mythos now — if you have a plan to leverage it effectively — are going to be ahead. There's just no other way to put it. They have access to a better brain.
Now, that doesn't mean Mythos will always be expensive. We have the Vera Rubin chipset coming up behind the GB300 — a whole new generation of chip from Nvidia. As that starts to come online and even better models start to come out, it becomes cheaper and cheaper to serve Mythos. We are going to be in a position in a few months — six months, maybe less — where you start to see the cost come down and you start to instead see more expensive models come out that are even better than Mythos.
So when you start to think about what you're investing in — the plans you're purchasing, what you want to invest in — this goes for corporations buying for their employees, it goes for individuals: think about it as what trajectory, what curve do I want to be on? Do I want to pay to be on the cutting edge curve, and I'm going to use it to the hilt and I'm going to build really cool things and I'm going to learn and I'm going to leverage it to 10x myself? Or do I want to be a step behind on a pro plan or whatever the $20-a-month plan is? And in that case, I'm willing to wait, and I'm not going to be the first. I'm willing to take the hit on my career for that. I'm willing to take the hit for my employees if I'm a company.
And in that situation, I should not expect productivity that will resemble what people who are on those cutting-edge plans get. And that's a very serious thing. You might think, "Oh, but talent will make up for it." No, human talent will not. Increasingly the whole point of human talent is to simplify and get out of the way so that AI can do its thing. And the bigger the models get, the more that becomes obvious.
Mythos is one of those moments when I think it's going to be very obvious that we are in a different world, and that the people who have that $200-a-month plan are going to effectively have superpowers. Think about whether you want those superpowers or not. I'm not saying there's an easy answer. I know $200 a month is a lot for a lot of us. And I know it's a lot for companies — if you're paying for it by token, it's also not going to be cheap. Think about whether it's worth it. Think about whether you can leverage it. Think about your multi-model strategy if you're a company. What are you using for Mythos-shaped problems? Do you have a reliable way to see what is a complicated problem that is worth putting a cutting-edge model on versus not?
And by the way, if you're thinking I'm kidding about the gain in capability — we had another one of those stock market spook events where cybersecurity stocks dropped between five and nine percent as soon as Claude Mythos was even leaked. The entire world is starting to take AI models seriously because they have seen enough evidence to show what these models are capable of doing.
So yes, I fully expect that the leaked blog post that came out on the Anthropic servers is broadly correct. It is going to be a big jump in coding. It is going to be a big jump in the ability to produce excellent artifacts like Excel and PowerPoint. It is going to be a big jump in the ability to reason. It is going to be a massive jump in cybersecurity and more. We should take that seriously. When you look at what Mythos is going to mean, you should assume it means a step change that's coming in the next month or two.
What a Mythos-Ready System Actually Looks Like
I've been telling you 2026 isn't slowing down. This is an inflection point. Pay attention.
So what does a simpler system look like? What does a Mythos-ready system look like? I think a lot of the time the larger public conversation has been around the security thing — oh, this is a big model — and those are fair to call out. But I want to go past that. I want to look at what it means to have a well-architected system that lets you sleep at night before Mythos comes.
Number one: make sure you have very clear outcome specifications. Specify your intent in a way that makes sense for a smarter model. Let me give you an example. Back in the customer service world, let's say what you want to specify is: resolve this customer's issue using our knowledge base, our policies, and our account history — and make sure the model has access to that. The customer should leave satisfied, and the resolution should comply with our return policy. That is actually a decent outcome specification.
Compare that to what most production systems I see look like. It's essentially a process: first, classify the intent into one of 14 categories. Then route to the appropriate handler. Then retrieve the top five knowledge-base articles using hybrid search with alpha equals 0.7. Then generate a response using only the retrieved context. That is what most of the prompts I look at today look like. You see the difference between that and the outcome one. The outcome one just says: I need to resolve the issue, it needs to be in line with this policy, here's where the policies are. You may not be able to do that today with today's models. But be ready to do that. Get ready now, because it's going to take time to rearchitect your systems. Start to think about how you need to prioritize your work to get set.
Number two: think about your constraints and guardrails. These are things that must be true regardless of how the model achieves the outcome. Because when you give the model more flexibility, you need to be more clear about your constraints and guardrails. For example: never disclose customer financial data. That's a pretty good guardrail. Always verify refund eligibility against our policy — there's another one. These should survive model upgrades because they represent ongoing business rules that you want any model to follow regardless of how smart it gets. Get good at those.
Number three: think about the tools your model can use. You want to make sure you have an excellent set of tools that have the right capabilities, where a model can look at the tool set and say: I know what this does, I can use it, and it's going to be effective at what it does. Maybe it's search your knowledge base. Maybe it's look up an account. Maybe it's process a refund. The model is going to decide increasingly what to call and in what order, and it's going to be up to you to define what the tools do and to be effective about that and present those tools effectively. So put some work into your tool definitions.
Number four: think about multi-agent coordination patterns. I've talked about this before — Cursor's idea that you have a two-agent hierarchy is much more effective than an agent swarm. You now need to be thinking about long-term planning with agents and what it means to let an agent like Mythos spin up agents from a variety of capability sets to get a task done.
You should be in a position where you understand how to present an outcome spec that Mythos can take, how you then present an overall set of evals Mythos has to work against, and how you give Mythos a tool suite that allows it to spin up a lot of instantiated agents to execute against that plan. How it has places to track progress and how it can measure itself with eval. You need to take the idea that Mythos is the planner of your software more seriously.
And again, I don't just mean Mythos. Any of these next-generation models is going to do this. Codex is going to drop a new model not too long from now, and it's also going to be more and more able to do this. All we're doing is extending the current pattern we see at Factory.ai, at Cursor, and saying: these models are more capable, you're going to give them more to work with, you're going to be more confident that they will get it done well, and you're going to make sure the evals allow them to assess themselves in a way that is reliable so you can trust the results.
And by the way, you can also have a different instantiated Mythos model assess the work of that other model so that you have a second pair of eyes. You don't need to trust the model that did the work to do the eval as well. But really, increasingly, once you can specify the outputs and the guardrails and the tools and you just give the model a sense of the harness and architecture to get to the outcome, you're going to step out of the way. And that's increasingly going to be the case not just for technical work but also for knowledge work.
The Rise of Non-Technical "Under the Desk" Software
We're going to start to architect, in a sense, technical flows for non-technical work. We're just at the beginning of this, but I think Mythos is going to invite us to start thinking about that. Our under-the-desk software — the software we build as non-technical people — is going to get increasingly sophisticated because we're going to have access to models that can do increasingly sophisticated things just by communicating an intent in plain chat.
So we should be looking, as IT administrators, as leaders, at the idea that this under-the-desk software category that was just for personal use may increasingly be developing really useful team applications that never end up touching the engineering team. How do you maintain that? How do you think about that? How do you teach the idea that you want these concepts — guardrails, outcomes, specifications — for non-technical folks, for folks who are like, "What is a spec?" Well, a spec is just defining what you want the model to do. There are ways we can teach this that are really not going to be intimidating and that don't require using the terminal or the command line. And it will allow you to build useful working software to solve non-technical problems.
And this goes for families too. Let's say you want to build a family calendar. It's increasingly going to be possible to not just build one for your personal use that you start up on your laptop, but to build one you can deploy and maintain for the family — and it's not even going to be something you think about as a piece of software, because you never touched the code. It just got built for you because you specified it and you said "please hook into my Google." Now, do we need better agentic primitives? Do we need better ways to hook into things like Google Calendar so it's easier? Absolutely — that is one of the big pain points right now. But that is the direction we're headed.
And so if you're looking at this from a role perspective, from a team perspective, you should be stepping back and asking yourself: how much of my role is compensating for a model's limitations today, versus how much of my role is really thinking about how to architect and correctly aim artificial intelligence so it accomplishes a lot of work? That's the part you want to be on. That's where you want to lean your role. And everybody has, to some degree, a chance to lean there — to lean more into "I want to guide the model toward work that it needs to do" and less into "my job is to compensate for model limitations," because model limitations are going to keep shrinking. We're about to have another big step change there with Mythos.
Final Recommendations
So what does this mean for you? I want to step back and simplify this in the spirit of Mythos. You need to take the idea that we are going to keep seeing model generations getting better very, very seriously. We are not hitting a wall. That means you need to think across your role and across your technical systems about how you can dramatically simplify. How can you simplify so that the model has room to be intelligent? If you take one thing away, that is what I want you to take away.
And yes, you absolutely will need to look in detail to get there, because most of us have added a lot of guardrails and a lot of cruft around our work so that we feel like we add value, and so that the weaker models can do their jobs. That's fine. The challenge is to grow by being able to simplify what you ask the model to do against a larger outcome set, so that we get out of the way and let the model do its job. We focus on making sure that we are aiming the model in the direction of a big, cool goal. We focus on making sure we're building the pipeline and the support and the tool availability and the availability of data so the model can do its job. And we also have a good sense of what good looks like.
Is the code clean? Is the Excel absolutely right? That's not going to stop being important. We just need to make sure that we give the model what it needs so that it can accomplish those goals successfully, and when we measure it and check it, we have confidence that the model got it done.
Claude Mythos is coming. The inflection point is here. This is another one of those moments when you need to be able to catch the train before it leaves the station. If you look at the checklist in this video, you're going to need to do some work to get ready for this. If you want to be ready day one, your systems need to be ready to simplify. So take the time — whether it's your own personal checklist, asking what canned prompts do I use and can I simplify those, or whether it's a work checklist and you're looking at AI systems and how you prepare for them and guardrails. Get ready. It's coming. It's not going to be that long. It's a matter of weeks now.