When the problematic adjudicator isn't the dominant one, one can either safely ignore them, or escalate to someone less problematic who does hold power, so there's no benefit in sabotage, and there's reputational harm.
Relatedly I think the only real solution to the "lying with statistics" problem is the formation of epistemic communities where you're allowed to accuse someone of lying with statistics, it's adjudicated with a preponderance-of-evidence standard, and both false accusations and evidence that you're lying with statistics are actually discrediting, proportionate to the severity of the offense and the confidence of the judgment.
That last bit seems wrong to me bc the "good location" premium is so large, e.g. https://www.crackshackormansion.com/. Davis and Palumbo (2006) estimated land value as 50% of residential real estate value, up from 32% in 1984, and home prices in aggregate have continued to rise for the same reasons.
Your "cannon fodder" argument got me thinking; I don't exactly think the argument depends on a new sort of fully distinct intelligence emerging, but rather a change in how our existing superorganisms are constituted. Modern states emerged in part as a mass-mobilization technology, and were therefore biased towards democracy. But as we learn to automate more things, smaller groups of humans better at implementing automation can outcompete larger groups of people mobilized by ideologies or other modern methods. If this keeps going, maybe we'll end up like the Solarians in Asimov's The Naked Sun for a while, a low-fertility skeleton crew of highly territorial lonesome tech-yeomen. If the skeleton crew is sufficiently infertile, it may leave behind a rigid set of automations that eventually collapse for want of maintenance by a living mind, much like the house in Ray Bradbury's story There Will Come Soft Rains.
I think there's a moderately likely limit to LLMs and other applications of the present machine-learning paradigm. Humans are powerful general intelligences because we can, individually and collectively, make use of different cognitive modules in a way that converges on coherence, rather than splitting off into different and conflicting subagents. Our brains seem to have stopped growing not when individuals hit diminishing intelligence returns, but when we got smart enough to network Dunbar-sized bands into low-latency collective intelligences, and then shrunk a bit when the Dunbar bands figured out how to network themselves - as The Flenser does in Vinge's A Fire Upon the Deep - into larger, more differentiated, but higher-latency lower-bandwidth collective intelligences. While this obviously doesn't guarantee that human+ level AGI will be nice to all other such GIs (that's not true of humans either) it does suggest that if a superintelligence functions in the same modular-convergence ways humans do, it will tend to recognize similarly constituted coherent clusters that it can talk with as something analogous to near kin or other members (actual or potential) of its community, much like we do.
LLMs are a bit surprisingly useful, but they're nowhere near being as inventive and enterprising as an Einstein or Feynman or Moses or a hunter-gatherer band (the ancestral ones who were investigating new tech and invented horticulture and animal domestication, not the contemporary atavists selected for civilizational refusenikhood), though maybe within a few decades of being able to do most of what a Von Neumann can do, if their development works out well enough; we've discovered that a lot of the "knowledge work" we pretended took real thought can be done by ghosts if we throw enough compute at them. That's pretty cool, but it only looks "PhD level" because it turns out the marginal PhD doesn't require anything a ghost can't do.
Seems like public corporations make ownership decisions close to the finance-theoretical ideal where they minimize the assets they hold that aren't part of their production function to increase return on capital, and people who want to hold claims on rents buy them separately, consistent with the model I advanced in The Domestic Product.
"Land is a minority of capital" is reassuring that this is mostly a summary of accumulated productive tools rather than of rent claims on natural resources rendered valuable by the productive use others can make of them. But it's in some tension with Gianni La Cava's claim that the increase in capital's share of income is largely due to increases in home values.
Presumably the solution to this paradox is that land values are mostly privately held, while public corporations tend to hold other forms of 'real capital,' so that rentiers still largely hold real estate, as they did when the term was coined. It would be interesting to learn whether privately held corporations' holdings are more similar to those of public corporations or natural persons.
I think your first paragraph is functionally equivalent to "if someone feels that the dominant discourse is at war with them (committed to not acknowledging their critiques) they may sympathetically try to sabotage it." Does that seem right?
"Conclusions are often drawn from data in ways that are logically invalid" seems sufficiently well-attested to be a truism.
2
1One argument for the TBTF paragraph was in the immediately prior paragraph. The posts I linked to at the end of the first comment in this thread are also in large part arguments in support of this thesis. Pre-WWII the US had a much weaker state. Hard to roll that back without constituting a regime collapse.
At this point I feel that I'm repeating myself enough that I don't see how to continue this conversation productively; I don't expect saying the same things again will lead to engagement, and I don't expect that complaining about the problem procedurally will get a constructive response either. If you propose a well-operationalized bet and an adjudicator and escrow arrangement I will accept or reject the proposal.
I would expect PhD value to mostly be affected by underlying demographic factors; they're already structurally on an inflationary trajectory and I expect that to be more important than whether they're understood to be fake or real. No one thinks Bitcoins contain powerful knowledge but they still have exchange value.
If there's a demographic model of PhD salary premium with a good track record (not just backtested, has to have been a famous model before the going-forward empirical validation) I might bet strongly against deviation from that. If not, too noisy.
Variance (and thus sigma) for funding could be calculated on basis of historical YOY % variation in funding for all US universities, weighted by either # people enrolled or by aggregate revenue of the institution. Can do something similar for h-index. Obviously many details to operationalize but the level of confusion you're reporting seems surprising to me. Maybe you can try to tell me how you would operationalize your "dropping pretty sharply" / "drop relatively intensely" claim.
Less than a sigma seems like it can't really be a clear quantitative signal unless most of the observed variance is very well explained (in which case it should be more than a sigma of remaining variance). Events as big as Stanford moving from top 3 to top 8 have happened multiple times in the last few decades without any major crises of confidence.
I agree the disagreement about academia at large is important enough to focus on, thanks for clarifying that that's where you see the main disagreement.
By EoY 2026 I don't expect this to be a solved problem, though I expect people to find workarounds that involve lowered standards: https://benjaminrosshoffman.com/llms-for-language-learning/
By EoY 2030 I don't expect LLMs to usually not mess up tasks like this one (scroll down a bit for the geometry fail), though any particular example that gets famous enough can get Goodharted even with minor perturbations via jerry-rigging enough non-LLM modules together. My subjective expectation is that they'll still frequently fail the "strictly a word problem" version of such problems that require simple geometric reasoning about an object with multiple parts that isn't a typical word-problem object.
I don't expect them to be able to generate Dead Sea Scroll forgeries with predominantly novel content specified by the user, that hold up to good textual criticism, unless the good textual critics are all retired, dead, or marginalized. I don't expect them to be able to write consistently in non-anachronistic idiomatic Elizabethan English, though possibly they'll be able to write in Middle English.
Not sure these are strictly the "easiest" but they're examples where I expect LLMs to underperform their vibe by a LOT, while still getting better at the things that they're actually good at.