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Why would the model not find the vulnerability during implementation or testing before release?

If it requires a lot of compute and trying, this is something that could be provided for common software.

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Sad that this could well be that the path to OpenAI and Anthropic profitability of this arms race between defending LLM white hatting a company’s website and the black hat LLMs attacking it?

So the whole thing is forcing the good guys to outspend on tokens to preemptively defend against the risk of the bad guys outspending them on tokens, rather than buying tokens to actually add features to the product etc.

So are they creating a market for the solution by helping create the problem? A kind of rent-seeking AI security-industrial complex!!

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Assuming an equal level of impact per token spent, the scales have tipped in favour of the attacker.

White hats are constrained by needing to pay for their own tokens, only using (expensive) vendors who meet governance and risk requirements etc. Black hats are free to take over accounts and steal services from wherever they can.

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The only thing AI has changed is that it has dropped both: the cost of attack and the cost of defense. Nothing in the game has materially changed; the game has just sped up.
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Who gets rent has changed. It puts me in mind of cloudfare et al
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Not really. Actually, for the purposes of cybersecurity, local models are far superior. Both offense and defense.
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The game has increased in scope.
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That is the direct effect of reduced cost. Jevon's paradox type effect: cost goes down demand goes up. You can AI-check so many more things that would be very time consuming earlier.
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The path to vast OpenAI profitability is trivial: advertising. Monetizing several hundred million users = $100+ billion ad network. 900 million active weekly users. Silicon Valley can do ad networks extraordinarily easily. Anybody doubting the ability of OpenAI to build an ad network around GPT will likely be embarassed in the near future.

The path to substantial profitability for Anthropic is questionable. The Chinese LLMs threaten them by far the most of the three major US LLMs. The money for Anthropic is certainly not in $20-$200 subscriptions. And they don't have anywhere near the consumer potential that GPT does, in terms of unleashing an ad spigot. So how far will the API money scale while being undercut by China.

OpenAI has to fight with Google for the ad business, they're specifically building Gemini to focus on consumer + search. Anthropic's business looks cute next to Google's search ad business (which is entirely at risk in this inflection). Meta looks like the biggest potential loser right now, ad dollars will be sucked out of the rotting Facebook network (not Instagram) and redirected to the rapidly expanding, hyper rich context LLM interaction. Advertising on Facebook will feel like running dumb banner ads on Excite in a few years, compared to what GPT will know about its users.

People that think Chinese LLMs are a general threat, don't understand consumer destination services, which is what GPT's future is. China currently has nothing to threaten with in that realm. There is half a trillion dollars of advertising up for grabs.

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> Silicon Valley can do ad networks extraordinarily easily.

This is just not true, building an effective advertising platform costs significant amounts of money, time and people.

Remember that you need to hire a sales force for this, and sales scales linearly rather than sub-linearly like engineering.

Additionally, you need to spend a lot of money dealing with fraud, fake and malicious ads.

Furthermore, you need to figure out where to put the ads and how to rank them.

Finally, advertising is a zero sum game (given that the internet has already killed lots of print & OOH advertising), so the only way to win is to better better/cheaper (preferably both) than Google/Meta/Amazon. Best of luck with that (although to be fair to OpenAI they did hire Fidji who knows a lot of this stuff from her time at Facebook).

They don't have a Sheryl Sandberg type figure, and she was also really important in selling FB ads to large advertisers.

Just looking at their leadership team I don't see anyone with a background in (successful) ads companies, so I'm pretty sceptical that they can build this out quickly enough to matter.

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Because it's far cheaper to to not spend the tokens finding the vulnerabilities, and software is now being created and released magnitudes faster than ever before. I could see the huge software companies maybe having fewer vulnerabilities, but I expect to see so much more in the smaller side of things.
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The surface of potential issues is growing with complexity of all connected parts of the system. That applies to not only software. To prevent issues you either spend proportional amount (dollars, tokens, hours) on testing or reduce complexity of the system.
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Because people need to spend time and money on that, which they won’t. The implementation is cheap, the review and follow-up is not (speaking from a pure LLM only workflow). My ratio is around 1:2 currently, so twice as much time spent fixing vs building.
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> it requires a lot of compute

This is one reason

> and trying

and this is the other.

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Those companies that produce more RCEs than they close will sink and those that don’t won’t.
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If customers actually cared about this, Microsoft would’ve gone bust 20 years ago.
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