I more usually don’t trouble myself about what long dead people who were wrong about almost everything were thinking centuries ago. So I have no opinion on whether Descartes was or had to be presupposing anything.

But if we get rid of that guy and just ask ourselves whether the undeniability of present experience requires presupposing the LNC, the answer then becomes to reject the question: that logic is a presupposition is a bullshit congame played by Christian nutters. The LNC is not presupposed. It is self-validating. It’s really just a semantic referral to the fact that distinctions exist, and therefore it is literally meaningless to say my present experience does and doesn’t exist. It exists. That simply is the case. That is distinct from it not existing, which it’s simply not doing at the moment.

So arguably, “direct present experience is undeniable” precedes or manifests the LNC, it does not presuppose it. It simply is an instantiation of the LNC, with respect to “existence” and “direct present experience.”

That isn’t what my article is arguing at all.

You seem to have just skimmed and not read. This is an article about warranted belief, not proof or even truth per se.

If you want something dealing with the probability problem in regards to proofs and truth see my article on The Gettier Problem.

Here the issue is what establishes warrant, i.e. when can we stop doubting and believe something, which is not directly about truth conditions. In warranted belief, the question is whether you get to believe things you haven’t even slightly proved at all (can you just “assume” something is true, the answer to which is no, but explaining why takes time), and whether there are things you cannot doubt (the answer is yes, but only Cartesian knowledge, which things are far fewer in number and less useful than most things we need to believe to get around).

Does Descartes’ Cogito presuppose the Law of Non-Contradiction?

The intuitionist interpretation says no. It’s a direct self-evident apprehension, not an inference, so LNC isn’t being smuggled in as a hidden premise.

But even granting that, the direct experience of “I think” still seems to be this thought rather than that thought, which already involves bare distinction. When we experience the “raw feels”, is the LNC baked into the background?

In other words, with regards to a particular claim, it may be the case that one cannot prove that one could be mistaken about that claim. In other words, one cannot even prove that one shouldn’t be certain about a specific claim!

Correct. The reduced labor share and the switch of pay from high to low skill work do not necessarily mean a reduction in wages… but if the labor share goes down and the population hasn’t changed (or if profits/GDP have not increased absolutely massively), it inherently will mean reduction in wages. More importantly, what Minnitti et al. are identifying (and not just from “AI” in the sense of LLMs but decades of various forms of automation – which makes citing them as a study for AI in the current sense actually even more useless) is, effectively, an increase in inequality and a decrease in skills, and at the scale of huge regions. This is a serious concern (we should be trying to reduce not increase inequality), and the magnitude of that shift from higher and medium skilled to lower skilled workers (obscured by the relatively small magnitude of the general labor share decline) is worth addressing. Now, again, we agree that the concern isn’t automation per se but its implementation and specifics, as well as the broader political climate.

I didn’t say labor share can’t be measured for a nation. I said it doesn’t measure wages or jobs. Obviously the same metric can be aggregated for all companies not just one company. But it’s still the same thing. The rest follows (including the laughable effect size etc.).

Hence there is no evidence here that AI is killing jobs or lowering wages.

No, labor share is quite clearly (and is in Minnitti et al. ) a national metric. To quote Wikipedia: “In economics, the wage share or labor share is the part of national income, or the income of a particular economic sector, allocated to wages (labor). It is related to the capital or profit share, the part of income going to capital,[1] which is also known as the K–Y ratio.[2] The labor share is a key indicator for the distribution of income”. Minnitti clearly discuss national inequality metrics, so that is clearly the context they’re using it in. In their Appendix, they state clearly and repeatedly, “The dependent variable is the labor share, defined as the ratio of employees’ compensation to regional gross value added (at current prices)”. The regional gross value is not a firm-based calculation, it is the economic value of a given region. Regional gross value is how you calculate regional GDP: “It is the aggregate of gross value added (GVA) of all resident producer units in the region, and analogous to national gross domestic product”.

They’re not discussing the internal share of tasks in a firm. And, on the scale of a national economy, a small effect can affect hundreds of thousands of lives. As their conclusion states, “Our findings indicate that AI innovation is associated with a significant decline in the labor share, potentially accounting for up to one third of a percentage point of the overall decrease observed since the early 2000s. This highlights the notable impact of AI in exacerbating income inequality in terms of functional income distribution, particularly in regions more engaged in developing AI-related technologies, underscoring AI’s role in driving regional disparities in labor income distribution”. They’re referring to national macro statistics and their share is since the early 2000s so this effect when taking into account how relatively recent the AI we’re discussing is could be quite serious. (Also, I think that Minnitti et al. are using “AI” to mean computerized automation writ large, which means it’s not very useful for our purposes here, but they don’t have a good operational definition section).

So obviously it’s true that companies may lie about why they fired some workers to exaggerate the effect, but they also may lie when they did in fact fire someone for AI when they didn’t, and they mean fire folks to cover up for losses due to AI. So while that method is not ideal (it’s always hard to get at the internal strategy of companies), I don’t see a reason why it inherently biases one direction rather than the other. And if they add jobs due to AI, great, but that doesn’t help the folks who were laid off. If the result of the system is large-scale localized disruptions (for, again, systems that are of limited value), and then they also expand some enterprises for jobs that are likely to collapse if they realize these systems suck, that’s pretty bad.

And July was actually a really big month for AI. China announced they were going deep into it, AWS and Google had a number of new services, etc. Also, companies could very well have had an internal time (which lines up six months into the year) to determine if they were making redundancies, or that could have been when reportage happened. And I don’t find the idea that they’d blame AI, something that the capital sector is deeply interested in pitching as a total good, over tariffs, something most business have indicated is an issue (even against Trump’s retaliation), all that compelling either. I agree their method can’t sort that out.

As https://fortune.com/2025/08/08/ai-layoffs-jobs-market-shrinks-entry-level/ points out (though it definitely is frustrating that it is only the Challenger study on this topic), “Layoffs are surging in the U.S., with companies announcing more than 806,000 job cuts so far in 2025, the highest figure for that period since 2020, according to Challenger, Gray, & Christmas. The tech sector has been hit the hardest, with over 89,000 layoffs in the industry alone. The firm found that more than 27,000 tech job losses since 2023 have been directly attributed to AI-driven redundancy, as companies streamline operations and restructure departments. At the same time, companies are becoming more selective about who and where they hire. Entry-level roles are feeling the worst of this impact as the technology is increasingly good at automating junior-level work. Many firms are seeing easy cost-cutting opportunities at the entry level. “A lot of entry-level work when you’re fresh out of college is knowledge-intensive jobs where you’re collecting data, transcribing data, and putting together basic visualizations, and learning the organization from the ground up,” Tristan L. Botelho, associate professor of organizational behavior at Yale School of Management, told Fortune. “AI can do that quite well, and I’ve heard many managers say things like: ‘We can reduce our entry-level headcount.’ … The biggest disruption is likely among these low-level employees, particularly where work is predictable, tech-savvy, or more general'”. So that’s corroboration from experts, including McKinsey saying they are actively using AI and using it to make employees redundant. And the fact that the annual job cuts are not only so large but consolidated in tech (which, even given component costs going up from tariffs, is internationally diversified enough and service-centered enough to not be really as affected by tariffs as much more capital-intensive companies are, and also got some exemptions: https://www.ainvest.com/news/political-cost-tariffs-impact-tech-sector-valuations-2508/) I think is indicative of a potential trend. (Of course, tech companies are famous for bullshit layoffs, so that could easily be them just preparing for a lean time and using AI as an excuse, but it could also be them getting sucked into the AI hype, and the number of people in this Fortune article testifying to the latter makes me dubious that it’s all purely dishonest).

That diverted investment could be paying someone if it doesn’t end up being locked into various financial instruments due to our crap monetary velocity. But if it does, it could be going to massive consulting firms, tech firms with huge internal inequality, etc. etc. It is wholly possible for companies to go from investing into something that created hundreds of thousands of jobs to invest into something that only creates thousands. That’s one of the ways you can get medium-term systemic unemployment.

I should also add:

That diverted investment (like every other) is paying someone. Every dollar diverted to investing in AI hardware, software, etc. is paying for jobs somewhere else (the people building the hardware, software, etc., and selling it, delivering it, maintaining it, and managing all those people, and all the support effects, e.g. for every dollar of this, some is going to pay for the guy who fills the coke machine in the AI development office, the janitors, the security guards, etc.).

Even the companies that are growing overhead share for AI acquisitions are also likely hiring or paying existing employees to use it. Which effect is also not being measured by “labor share.”

So “labor share” is a completely useless metric for our purposes here. It tells us nothing useful about the effect of AI on jobs or wages.

measure is labor share, which is compensation paid to workers

Incorrect.

Labor share means the amount of an investment that goes to labor.

For example, a steel factory has a low labor share because most of its overhead is buying steel and coke.

Their study simply says that companies that invest in AI divert capital to hardware, software, etc., which is simply just always the case (every year capital is redirected to new things, like new cash registers, new market development, building new stores, etc.); doesn’t have anything to do with taking money away from labor (where the capital is directed from, or whether new capital is raised for it, is not being measured here); and their effect size (the amount of capital they claim is diverted) is so small as to be literally LOL.

Why bother with all this R&D outside of key markets when you can just have poor people drive?

A concept Andor nailed: actually, prison labor is cheaper than droids.