PC succeeded pretty much because IBM broadly didn't take it seriously. If IBM believed in the market opportunity, they would have screwed it up by making it all in-house and proprietary. See when it did start to get traction and they decided to try to lock it down with MCA.

Their hardware though is just refresh fodder for mainframe/mini/aix. Sure they toss it some concessions to hype but ultimately customers are currently looking for the most cost effective platform to enable running stuff on nVidia Blackwell, and IBM isn't even vaguely in that game, not since a brief time with POWER9 where they tried to foster a close nVidia relationship.

No fab, no x86 systems, no resell of third party stuff that would work for getting in on Grace/Vera based systems. There's not really an angle for their hardware to get in.

If nVidia were still a bit hungry then IBM enthusiasm might have helped, but nVidia in-housed CPU aspirations and broadly do not care a bit about who their partners are and what they want, they correctly see themselves as the center of the universe for now and have pretty much sold every chip they plan to make for a long time. In that circumstance, there's just no way in unless IBM pulls off a long shot with an nVidia alternative (not just technical, people actually have to *believe* in it, and IBM has squandered their credibility over time).

IBM has offloaded the hardware that would have had them most relevant to the boom. They basically just have hardware to cater to long term mainframe and AIX customers. Even many of those customers are probably deciding to postpone their IBM hardware refresh to secure more hype-compatible gear from other vendors.

IBM chased what they saw as more plush software and services margins and chopped away to fixate on those fronts, except the locked in mainframe/mini/aix customers that they can keep gouging without fear of strong competition.

There's a way, but it doesn't jive with AI not becoming radically more powerful.

LLMs would evaporate if something superseded it by being appreciably better.

But if one asserts AI isn't going to become radically more powerful, then it is a silly statement to say LLMs won't be a thing. There's clearly *some* appetite and use for them. The bubble may pop and maybe some LLM applications will back off, but they are certainly going to hang in there.

That is true, out of 70 reports, only 1 was spot on and only one other was technically incorrect, but it was adjacent to a real problem.

The thing was that as maddening it was to review the 70 reports, the two issues either directly or indirectly responsible were worth it.

However, in an open source ecosystem, you have a whole bunch of users so those 68 bogus 'findings' get repeated for every rando that feels like 'helping' with issues... So open source gets hit harder due to the higher chance for duplicates...

Suppose the question is 'high quality'.

It absolutely floods the field with false positives.

However, depending on the context, you can still see some issues a human is likely to miss. It's an idiot with crazy attention to detail.

So you can't count on it to catch things a human would and in many scenarios it will fire off more false positives than anything vaguely right, but it does represent a value in a more manageable haystack to catch certain issues.

But go back to a reasonable level of skepticism about suggested fixes, and ensure even if you agree with the LLM on an issue, that you understand it well enough to not get screwed over by the LLM suggestion...

Note that even if it is motivated by dubious ways, even if it is propaganda, that doesn't *necessarily* mean it is disinfirmation.

As they say, the best propaganda is true.

It's not sufficient, and it has a lot of false positives, but it can help get a smaller haystack with some of the needles that a human review can miss.

Code review for quality and security is something the LLMs can help without much inherent downside, so long as you do not trust the review but use it instead as an additional pass and make sure you follow up and understand any 'finding'. It *is* a risk if you take it as replacing the need for carefully considered human review, but with discipline you can have the best of both worlds: actually intelligent human review and the detailed coverage an LLM tends to get.

I've found that it's usually wrong, but a fair amount of time despite being wrong, understanding the area it was wrong about yields an a real problem it didn't catch. Then of course, upon occasion it is simply correct.

I would be extra wary of suggested remediations just like other generated code, but I would look into things it flags as somehow tricky.

But what about when the people *are* tools?

While you may be correct that the claim about consciousness being from beyond the observable universe is not falsifiable thus beyond the scope of scientific credibility, so too is any current understanding that would support an assertion that "Anthropics models work *just* like human consciousness".

We have pondered the question in a philosophical way, and can assert certain trends based on evidence, but in a comprehensive fundamental sense, the question has to date remained philosophical at the levels that the Anthropic paper needs. Note that the paper even explicitly acknowledges this facet of their work as philosophical, and the generated responses reflect upon that. It puts a lot of weight on the models being able to self-assess accurately and then using the result to show that they can self-assess a consciousness. Including one area where I noted that the "j-space ablated" output indicated that it was just token prediction, and seems like they use that to illustrate that it is conscious that it lacks consciousness...There's a lot of circular reasoning around the headline claim.

Feel like those are more akin to science philosophy than concrete scientific understanding. Which tends to happen in scientific pursuits when we dig deeper than what we can realistically actually explore in a strictly scientific way. Biggest problem is we struggle to recognize it as philosophy when it comes out of scientific thinking. We *want* to know more than we can piece together through scientific basis and for lack of a better option we go for the unfalsifiable and subjective opinion.

I recall a physicist explicitly addressing the phenomenon in his field. He refrained from participating in the musings because all he could do was figure out what math worked and hope someone comes up with better math, the wilder speculation upon unobservable implications was beyond the scope of what he felt science could do, but it's a very popular thing in physics.

I perused the paper, I think the comment was pretty spot on. Specifically the part where they have as a casual assumption that they know how human consciousness works.

It's basically shaking the magic 8 ball and it works, but it's totally a judgement call when it has worked 'enough'. It doesn't necessarily progress from easiest to hardest issues to find, it just is a bit random. Hard to say how many passes before you've *probably* got the real ones. My experience has not been that false positive rate increases, sometimes you might have all false positives but a continuation will flag real issues.

Indeed, they take needles in haystacks and make smaller haystacks that have some of the needles. They don't catch some things and they falsely flag a lot of things, but they do either directly or get close to something.

One issue in our codebase that was caught was pretty much spot on. Now upon it being highlighted, it was obvious to anyone that the inexperienced developer screwed up, problem was no one had the attention span to notice. The other issue close to real that it caught was actually not the flaw it indicated, however while looking at the jankiness that caused it to mis-indicate, a real issue was discovered. So it's indicated "fix" would have done nothing and left the codebase vulnerable, but the finding *was* useful in identifying an actual problem and fix.

However, it does open up a gigantic mess in customer engagement. Customers like to use every security tool at their disposal and hold the vendor accountable. Fine, except now we are inundated with false positives because the LLM indicates something, the customer has no way of knowing if the LLM is right or not, and we have to address false positives. We are now having to consider how to "fix" false positives by steering the LLMs away from indicating bogus things when analyzing our product. When it was the occasional misguided security researcher making a misunderstanding, no huge deal, we could discuss nuance and come to an accord. With the LLM mess, there's no skilled human to appeal to, a skeptical customer, and just a lot of volume...

Whatever you may think of capabilities, the narrative has always been about what they think sounds best in the moment.

So at one point, only the owner class matters, investment is King and every other concern can be bulldozed. The business opportunity down market would never be as much as what employee replacement would be.

Now they see a need to balance it because shockingly the people still have some leverage, whether it's banning construction projects or getting skittish with retail investment. Further the stories around employee replacement have gone permanently bad at prominent companies like Ford. So they need to move the goal posts to dodge the negative optics and recalibrate expectations.