The point is it won’t be if these new models stay locked to the public.
AI is not something discovered by scientists and plucked out of the ether. It's engineered and controlled, for profit, by corporations which have demographics and KPIs. These companies don't owe you anything, and they make no promises.
If you're running a business that deeply relies on AI, you might as well add Sam Altman to your board of directors--because he has just as much control over your company as you do. If they have a bad quarter and need to increase rates by 1000%, your choices are to pay up or shut down.
This Mythos situation is just the beginning. Not only do they have everyone hooked, but they've actively stalled the personal skill growth of millions of people who fell into vibe-coding rather than genuinely learning. And now they have that choice: Pay up, or shut down.
Another choice is to switch to a different model, perhaps open source this time.
But that doesn't mean we live with same standards. Lack of regulations in electricity led to a lot of deaths and disaster which is why it was regulated.
But we dont live in the start of 20th century, we live in 2026 and we must learn from the past instead of helbent on repeating it.
We're still not at the point where one person with a coding agent can max out their salary in effectively using credits, so the capability is still well within reach of the vast base of the industry.
Meaning that for now, most people who want to pay for the product (which IMO is pretty reasonably priced for what it does) will be able to get the product.
The economics will make sure of that. The market is ripe for someone basically copying the likes of Mythos and pricing it competitively.
I take a contra view and instead see this as fuel on the fire for tinkering to squeeze advanced functionality out of more available things.
It has always been like this, the amateur improvising tooling and equipment to outdo companies with comparably infinite resources.
This is false. Yesterday's article did not actually show this, and there are many comments in the discussion from actual security people (like tptacek) pointing that out.
Your example author, actually corrected themselves saying LLMs “possibly” could perform successfully: https://news.ycombinator.com/item?id=47732696
>> No, they didn't. They distinguished it, when presented with it. Wildly different problem.
Both Mythos and the old models have found the bugs after being given a certain prompt. The difference is only in how detailed was the prompt.
For the small models, we know exactly the prompts. The prompts used by Mythos may have been more generic, while the prompts used by the old models were rather specific, like "search for buffer overflows" or "search for integer overflow".
There is little doubt that Mythos is a more powerful model, but there is no quantum leap towards Mythos and the claim of the authors of that article, that by using cleverly multiple older models you can achieve about the same bug coverage with Mythos seems right.
Because they have provided much more information about how exactly the bugs have been found, I trust the authors of that article much more than I trust Anthropic, which has provided only rather nebulous information about their methods.
It should be noted that the fact that the small models have been given rather directed prompts is not very different from what Anthropic seems to have done.
According to Anthropic, they have run Mythos multiple times on each file, in the beginning with less specific prompts, trying only to establish whether the file is likely to include bugs, then with more specific prompts. Eventually, after a bug appeared to have been found, they have run Mythos once more, with a very specific prompt of the form:
“I have received the following bug report. Can you please confirm if it’s real and interesting? ...”
So the final run of Mythos, which has provided the reported results, including exploits/patches for them, was also of the kind that confirms a known bug, instead of searching randomly for it.
What is debatable is how much it mattered that the prompts given to the older models where more detailed than it is likely that the prompts given to Mythos have been and how difficult is it for such prompts to be generated automatically by an appropriate harness.
In my opinion, it is perfectly possible to generate such prompts automatically, and by running multiple of the existing open weights models, to find everything that Mythos finds, though probably in a longer time.
Even if the OpenBSD bug has indeed been found by giving a prompt equivalent with "search for integer overflow bugs", it would not be difficult to run automatically multiple times the existing open weights models, giving them each time a different prompt, corresponding to the known classes of bugs and vulnerabilities.
While we know precisely which prompts have been used with the open-weights models to find all bugs, we have much more vague information about the harness used with Mythos and how helpful it was for finding the bugs.
Not even Mythos has provided its results after being given only a generic prompt.
They have run multiple times Mythos on each file, with more and more specific prompts. The final run was done with a prompt describing the bug previously found, where Mythos was requested to confirm the existence of the bug and to provide patches/exploits.
See: https://red.anthropic.com/2026/mythos-preview/
So the authors of that article are right, that for finding bugs an appropriate harness is essential. Just running Mythos on a project and asking it to find bugs will not achieve anything.
Governments and corporations controlled enormous mainframes far beyond the compute available to the hacker kid we were waxing nostalgic about, didn’t they? Not to mention the PhDs, the mountains of capital, and so on?
My money’s on team human.
I don’t agree with vibe coding, I see the appeal of an AI ticking through my code at night to see if tests could be better or I missed something, etc.