It's rare that I manage to read any longer pieces on arXiv that aren't strictly about galaxy evolution, but today I indulge myself. And I'm glad I did, because this particular piece was highly provocative and well worth reading in full. This write-up will itself constitute something of a long read, so I advise getting some tea before we begin.
Ready ? Good. Here goes then.
David Hogg's self-proclaimed "very white" paper thankfully isn't so in the horribly racist sense, but in the "here are some semi-organised thoughts that might be worth considering" sense. It's entitled "Why do we study astrophysics ?" and it's ostensibly written in the context of ever-increasing AI development. His goal is to provide a stepping stone to understanding when and how LLMs should be employed for astrophysical studies. He doesn't attempt to come up with a final answer to that question, because conscious or not, the effect of a machine that can answer questions more accurately than human experts still represents a technological and sociological singularity. Seeing beyond that point is too big of an ask.
Instead, he tries to tackle the fundamentals of why we do our job at all, thinking that this is a necessary precondition for how and why we might go about automating parts of it. If we can figure out why we're doing what we do, maybe we can better understand what we should do given expected technological progress.
As an essay, I found this one thoroughly excellent. There's much here I disagree with and much I support, all of it well-argued and clearly stated. It sticks to its central theme but covers a very wide array of topics along the way. There's actually not too much about AI in here, the focus being more on the human side, but I think it may have at least a germ of an answer as to how we'll proceed come the killer robot uprising technological singularity.
To be fair, Hogg's essay isn't the most linearly organised piece in the world, often feeling like something of a memoir. I've tried to keep this summary-cum-commentary to the linked themes and bits I thought I had something worth contributing to; I've deliberately avoided issues where I disagree but don't think the argument would get us anywhere (such as whether LLMs are truly thinking or not... this is very interesting to me, but makes no difference to the arguments here).
First, I'll look at the main thrust of the article : what astrophysics is and why we do it, which involves various thoughts on how we go about this. Then I'll conclude with a much shorter section on what this might mean in that future where we have vastly more powerful analysis capabilities than anything we possess today, but which we can reasonably expect to have in the coming years.
1) Astronomy Today
The professionalism of science
Much of astronomy is now done using large facilities by enormous groups, and the way these operate is inevitably different to small groups in a lab in someone's basement. Here I think the word he's searching for is industrialisation, not professionalism. The career-based nature of astronomy has already been long established, but the escalation of scale is still relatively new. The point is that you can't have people just casually mucking around on dedicated survey tools or slapping their own instruments onto billion-dollar facilities. This was absolutely possible in the old days – the underside of the Arecibo dish was littered with discarded receivers – but this kind of approach is all but dead already.
Astronomical data production is becoming extremely professionalized, and in a very particular way. Astronomers, in the case of Gaia, are just end users; end users of curated, calibrated data, delivered by a combination of the ([military-built] secret) spacecraft and the (absolutely great, professional, and open) DPAC... it wasn’t built or operated by astronomers.
Even university-based projects endeavour to produce science-ready data products that can be queried through application programming interfaces, plotted, and analysed without much worry about where they came from or how they got here. I have been involved in bringing about this change, and in many ways it is absolutely great. It democratizes astronomy, since it lowers barriers to entry. It creates an open-science space, in which every project benefits from the output of every other project.
But it does have a strange consequence, which is also related to professionalization : for some kinds of projects in astrophysics, there isn’t a huge difference in capability between a classically-trained astronomer and a newly trained data scientist... a data scientist who has taken an astronomy class might be better prepared than an astronomer who has taken a data science class.
Which is relevant, of course, because LLMs can absolutely do data science.
I think this kind of development is only partially inevitable though. We need big data and big data needs big facilities. But we also need small data, that is, data we can analyse in extreme detail. That may still need big instruments but it also needs small groups, and small groups can, and should, continue to operate in their current fashion. Analysis of big statistics is surely going to change, but on smaller scales, perhaps, is a realm where we can still do the low-level stuff ourselves. Here is where we can invest considerable time doing some aspect of the data reduction and analysis by hand and still have a good chance of making useful, interesting discoveries.
My reasoning is purely pragmatic. Manual data reduction and analysis can be extremely time consuming, but is perfectly manageable on small data sets. We've already abandoned this approach for the largest data sets because it's just not possible to do them by hand. But I fervently believe there is a great deal of value in learning to do the low-level stuff (the hard bit), even if later you never do it again. As a general rule, the better you can operate without a specialist tool, the better you'll be when you get to use it. Maybe for humans, then, the future lies not in big data, but in small data, in extreme specialisation rather than generalisation.
What is astrophysics ?
It's that which produces novel information about the Universe, says Hogg. Reading about it doesn't count, you have to do actual research. But... you also have to document the results. Unrecorded data doesn't contribute to the pool of knowledge from which others draw, so if you don't document it, you might as well not bother.
Astrophysics, then, is the literature, in his view, and it's this act of producing literature after a novel investigation which best describes the process of doing astrophysics.
I think it's hard to dispute this, but he has a couple of other points which might be more controversial. One is that software isn't as important as the results it produces. I actually do agree with this, because he explicitly declares that software should have associated papers. This then makes it just as important for certain metrics as actual science, and I think it's absolutely right that the effort of software development be properly recognised.
What he means here, I think, is only that software itself is not science. We write software so that we can analyse data, not because it's intrinsically worth having. Software which isn't used is as pointless as data that's not recorded.
He also notes :
Astrophysics, like any science, contains a lot of “implicit knowledge” or folklore about things like how to observe, how to reduce data, how to organize projects, how to visualize data and models, how to read and write, and so on. Much of this never appears in the literature. Is that not also astrophysics ? Yes it is, but it is astrophysics practice. The results of astrophysics — the scientific conclusions and debates — are in the literature.
Yes, but my answer here would be that we should absolutely record as much of this "folklore" as we possibly can. Some sort of journal of astrophysical methods – not describing mathematical procedures, but the really low-level stuff of what to do with the data and how to interpret it – would be valuable, I think. Such papers wouldn't have the lasting value of results papers, but they would make a lot of people's lives a lot easier. Implicit knowledge should be made explicit wherever possible*.
* Though it is ultimately impossible to record literally everything. Some things you simply have to do.
His second more controversial comment, with which I vehemently but provisionally disagree, concerns papers as a metric :
The second comment is that I often hear software (and hardware and engineering-oriented) people say that they “have to” write papers because papers — and the citations that they generate — are “the coin of the realm.” Papers (and the authorships on those papers) and the citations of those papers are not “coin” of anything ! They represent our recording of what happened, what we learned, what we know, and how we know it. Citations deliver provenance, not reward.
I'd love to agree with this but I can't. In terms of career advancement, it's not sensible at all. Like it or not, astronomy as a professional/industrial career does have certain requirements common to all employment. We need to get paid and we need to ensure job security, and we can't and shouldn't ignore this. The era of the gentleman-scholar is long over : I mean, sure, I'd love to give everyone tenure and a sack of money, but until we do that, papers absolutely and undeniably are the coin of the realm.
In fact, even in terms of strict science, I still don't think I can agree. If astrophysics is the literature, as Hogg claims, and our goal is doing science... surely it's literally true by definition that papers are the "coin of the realm" : inasmuch that if we should be judged by what we produce at all, this should be the primary means by which we do so. I can't get my head around the alternative, which would be a bit like saying that we shouldn't judge a painter on either the quality or quantity of the paintings they produce.
People are the ends, not merely the means
What might explain the above difficulty is what to me feels like Hogg's most controversial and complex point, one which I'm allocating three subsections to examining.
People, says Hogg, are what astrophysics is really all about. It's not about the Universe at all (and he's emphatic and explicit on this point, on which more below), it's about enriching ourselves.
When we employ a graduate student to perform some work, it absolutely must be because the graduate student will benefit from that work, not merely because that work needs to get done. I have heard it said, more than once, in research contexts, that an LLM can do some task “better than a graduate student.” That language makes me uncomfortable, because it is taking an extremely instrumental view of graduate students. Are graduate students in our groups and our laboratories and our universities to do work ? Or are they here to learn ?
We train PhD students not merely to amplify our own research programs, but to create opportunities, and specifically opportunities for them. Every person is a human being, whose personal development is more important than our short-term scientific accomplishments.
I mean... sure, to a point. I think it's the old Platonic point about whether education is about discovery or change, and it can only ever be both. We shouldn't be using graduate students as literal tools to solve the problems we want solving; they are not there to do the boring grunt work that needs doing but we don't want to do ourselves. But at the same time, we should be getting work done. We should be solving problems ! We should be learning about what Nature is, not just endlessly pontificating on what it might be.
The only real solution here, I think, is to find graduate students who share our interests so that the result is mutual benefit. We should be giving them problems that both advance knowledge and advance their own abilities. Otherwise, we risk running into one of Plato's weirder quotes (Republic, book VII, 503b) :
Then if, by really taking part in astronomy, we’re to make the naturally intelligent part of the soul useful instead of useless, let’s study astronomy by means of problems, as we do geometry, and leave the things in the sky alone.
I do think it's important to realise that the people doing the work are first and foremost people, not tools. The further we can get away from the mentality that productivity is the only result that matters, the better; the more we can suppress the need to be competitive, the more we can suppress the idea that we need to live to work – even when that work is something we really enjoy – the better the situation will be for everyone.
But two important tangential points crop up here. First, Hogg declares that this means that not citing relevant papers is (ignorance aside) actually unethical.
It is ethically required that our papers cite the work that is relevant to the work we are doing. You can’t decide not to cite a relevant paper because you don’t like the author, or don’t like the author’s institution, or don’t like their funding sources. In particular, if the literature gets flooded with work of relevance to your research program, you have reading to do, and citing to do.
I object very loudly to this ! And not just because I'd read dozens of papers that damn well should have cited me but didn't. First, pragmatically, the increasingly industrial scale of astronomy literature means that reading every paper on a topic is not a sane choice. As per the last post, reading hundreds of pages of largely-irrelevant text (and astronomical papers tend to be extremely dry, which is not a minor point) is going to result in negative value, not merely slowing things down. So no, exactly for the sake of not treating people like instruments, you absolutely do not have to read and cite absolutely everything. That's mentally destructive, not productive. You don't have a duty of self-destruction.
Secondly, from a moral viewpoint I also disagree. I see nothing at all wrong in deliberately not citing papers where we don't find the results and/or methods credible, and might even object if I was compelled to cite something I didn't believe – and I'd certainly have a very big problem if I was told by a reviewer to make something sound more plausible than I thought was really the case*. This is not to say we should avoid controversy and it certainly doesn't mean only citing the things we agree with. It only means that we don't have to cite the whole history of a research program and give equal weight to every long-discredited idea or failed avenue of inquiry.
* Hogg actually says himself that you can't cite work you don't trust, but seems to think it's obvious that we can trust people and can't trust LLMs.
There's one aspect here with which I do, however, violently agree :
Every scientific paper is written to help all of its writers, and all of its readers, learn and grow, no matter their career stages.
My take here is not about the content of the paper so much as their style. We need to write for each other, as human beings (which Hogg does very well indeed), not as automatons who require total clarity and unambiguity. If you want me to cite more papers, reform the standard requirements for a manuscript. Make them shorter, better organised (results first, then detailed methods) and more readable (allow the occasional joke, stop being anal about contractions and punctuation FFS). But this is a well-worn hobby horse of mine so I'd best not go down that route again today.
Hogg goes further and suggests that grant funding to hire students to do work might also be unethical ! And again, I cannot agree with that. We're not running a charity and it's not at all wrong to expect productive output (though we absolutely do need to be flexible in our expectations of that output). This is also in stark contrast to his later claim that we need to use our resources efficiently and get correct, rigorous results. We should seek good working conditions, but ultimately we are doing work. The results do matter.
The answers don't matter
Here we come to the heart of the problem. Hogg genuinely believes that the results of our research aren't important. In one of his oddest moments, he says that if we really cared about the results, we wouldn't do astrophysics ourselves but pay other people to do it for us... this is weird every way I look at it. I just don't think that's how people work, because extending that reasoning, nobody would ever do anything for themselves at all. And of course, it's a pretty perfect example of so-called effective altruism, which Hogg calls an "absurdity" ! He's not wrong about that, but my goodness me, the contradiction is as a glaring as glaring can be.
This baffling oddity aside, Hogg's argument is not to say he thinks we should all quit and do something else. His claim that the results don't matter is more specific and more strict than that... he think the investigations are worth doing, that that's where the benefit lies – in improving ourselves – but that what's actually going on in the Universe is of no consequence to what's going on down here.
That's, err, quite the hot take there. But it deserves more examination.
Hogg has this highly annoying phrase, "clinical value" which he best expresses thus :
I like to say that the sciences have a “left edge” which is about fundamental understanding, and understanding for understanding’s sake. They also mostly have a “right edge” which is about what I like to call “clinical value” but you could call application or use in the world for technologies or policies.
I claim (and maybe this is a bit controversial) that astronomy has no right edge. That is, there are no useful things in the world that flow from astronomical discoveries and results. I have spent years of my life estimating the comoving volume of the Universe, measuring the local dark-matter density, and finding planets around other stars. No human outcome or pragmatic capability has been affected in the slightest by any of my results. Literally nothing helpful to humanity arises here.
No sir ! No, I won't have it. First, the reasons why we do astrophysics – the whole title of the essay – are to me obvious. I cannot understand people who don't have any interest in understanding the nature of the world in which we live, and for those that do, then understanding the most miniscule corner of it and ignoring all the rest seems like a clear sign of insanity. For me, observational astrophysics is absolutely a fundamental science, more so, I would argue, than theoretical physics : that's just making up a bunch of stuff, which is valuable, but ultimately tells us nothing about reality, at least not with any certainty. It is observation alone which can do that.
Knowledge of what's beyond the sky is not some abstract wishy-washy thing, but essential in understanding the truth of our own existence. Would it not matter if the stars were holes in the curtain or night rather than fusing spheres of hydrogen ? Would it not matter if the nature of reality were that we were actually inside a giant koala rather than an immense vacuum ? I think it would, and in fact it might well form the basis of all our other knowledge.
Astrophysics is useless
Which leads to the final part of this section, and the second, closely-related reason I think we do astrophysics. Hogg claims that it's not for spin-offs and these don't count as the "clinical value" or right edge. With very few possible exceptions such as discovering dangerous asteroids, and in previous eras understanding chronology and navigation, he claims that these aren't the reasons at all :
Nothing in the world of things or people hangs on the precise value of the age of the Universe. Astrophysics may occasionally and accidentally produce something useful. But astrophysics is not done with the goal of obtaining clinical or practical value. A science has a right edge if and only if the associated clinical work actually tests or exercises the specific results of the science. None of astrophysics is justified in these right-edge terms. No astronomer (that I know) is improving the calibration of JWST instruments because they want the US Navy to have a higher kill rate.
No ! Astronomy's right edge is not in "the clinical value [which] lies in its feeding of humanity’s love" or some other airy-fairy thing that Hogg justly raises as failed counter-arguments. It lies in telling us what is not true. It defends us against ignorance, and the price of ignorance can be extraordinarily high. It becomes extremely difficult to maintain that you need to sacrifice people to appease the gods of the sky when you realise that there simply aren't any. Cosmology has direct moral implications : just because we no longer take a direct moralistic approach to cosmology, as was done throughout medieval history and earlier, and as Tolkien did brilliantly in fiction, it doesn't mean that our morality isn't affected by our understanding of cosmology.
Now to be fair, the precise values of different parameters do not always constitute such a hard right edge. It's not obvious how the exact distance of Proxima Centauri or the HI content of the M31 galaxy could have any moral value whatever. But collectively, we need all these incremental findings to get to the good stuff. We need the flies in the ointment to break our understanding and shatter our conceptual frameworks every once in a while. We need things like the perihelion of Mercury to tell us that Newton is wrong and time and space are themselves not at all what we thought they were, the full moral implications of simultaneity breaking still being something we haven't got a handle on. And we only get those results through slow, careful, methodical measurements.
Does it matter that those of us working on astronomy aren't doing so for the hope of such a breakthrough moment ? Does our motivation being purely intellectual simulation invalidate this hard right edge I've suggested ?
No, I don't think so – not at all. It's true that many of us like the pure research side of things, that we do our jobs (in part) precisely because we can avoid having to be responsible for other people. I too like the fact that nobody's daily lives are at all likely to be impacted by the velocity width of a dwarf galaxy I publish deep in a table of a paper that hardly anyone will ever read. But this does not mean the work isn't worth doing for its own sake, that it won't potentially contribute, albeit in a small way, to the revolutions in thinking which will eventually and inevitably follow. And those who are working with more express goals – if there actually is anyone out there calculating the distance to Proxima Centauri purely to refute astrologers or the hope of fortune and glory – well, more power to them, and equally, their motivations aren't invalidated by the pure interest sake that the rest of us pursue.
2) Astronomy Tomorrow
How does all this mean we should prepare for an astronomical future in the age of AI ?
Hogg proposes two extremes, both of which he views as undesirable. One is that we hand over everything to the LLMs and literally let them do everything, or at most, we try and curate their findings to sort the good from the bad. This would seem to be a pointless exercise in which we don't ever really learn anything, we abandon the joy of the process and reduce ourselves to mere instruments. Pretty much nobody wants that.
See, I think Hogg does have a point that the human element matters : we do astrophysics for our own enrichment and reward, and both the process and the results matter to us. Even if an LLM had such emotional motivations, there would seem to be self-evidently no value whatever in letting them do everything. That'd be like sending someone to go on a rollercoaster on your behalf. Having the experience, not just having casual access to the results, matters.
When we offload that work to LLMs, we are no longer doing astrophysics, we are no longer becoming astrophysicists, and, eventually, we no longer are astrophysicists. The let-them-cook policy, in the end, leads to the death of astrophysics, the end of astrophysics at universities, and the end of astrophysics education. Astrophysics would no longer be by humans, and then it would no longer be for humans.
The second extreme is that we ban LLMs altogether. This Hogg views as bad because LLMs can be genuinely useful, the effort to seek-and-destroy LLM content would be hugely inefficient and wasteful (again, we'd become mere instruments), and telling people how they can and can't do their research self-evidently violates their freedoms.
Hogg's tentative and intriguing suggestion for a middle route is that we treat LLMs as colleagues who aren't part of our own team :
You might ask your colleague for help finding something in the literature, but you wouldn’t ask your non-coauthor colleague to write the introduction of the paper you are writing. You might ask your colleague to help speed up your code, but you wouldn’t ask your colleague to write your code.
I quite like this. Necessarily, the middle route must be allowing the LLMs do some of the work rather than all or nothing, and the essence of having some simple guidelines for good practice is sensible.
I don't think it will work out exactly like this though : recently, I've been "vibe coding" a quite elaborate program and I'm convinced this is indeed the way of the future. I've resisted this practise for a long time, but decided that there was one particular code I really wanted to exist that I didn't have the time to work on (to be shared in a future post). Vibe coding is not zero effort, far from it, but it does work. I very much doubt that we're going to insist on coding by hand for too much longer, any more than we insist on adding and dividing using pencil and paper. Still, the basics of Hogg's idea are interesting.
I need to finish with a few assorted caveats :
Another idea is that, given our respect for our readers, we shouldn’t ask them to engage with something that took way less time to write than to read.
I don't think so. The content isn't any the less valuable based on effort. What seems obvious to one person can be profound to another... I mean, I heard a podcast of Mary Beard – Mary BEARD, for crying out loud – dismissing Marcus Aurelius' Meditations as of "no value". So much for that.
A second is that of the nature of LLMs : according to Hogg, we cannot trust their output, the text isn't meaningful until a human reads it, LLMs aren't reproducible, they currently only produce slop, and they can't take responsibility. All of these I think are only partial truths : we can apply the same methods of trust as we do for humans; the fact that humans will always have to read the text would seem to make the argument that LLMs lack any true understanding to be largely pointless; I agree that LLMs are not deterministic but this isn't quite the same as lacking reproducibility; the idea they only make slop is simply a garbage claim; and much more development is needed here as to what we actually mean by "taking responsibility".
And finally one throwaway, tangential claim I cannot responsibly let slide :
Of course it is important to remember that the human practice of astrophysics, at least in its current form, is also very damaging to the environment.
Without reading the citations provided I instinctively think this statement is of negative value. There's no way that astrophysics represents any sort of significant environmental problem. The kind of practises which are truly damaging are those of big businesses, industries, and the exploits of billionaires. It's right and proper that we try and set an example and constrain our environmental footprint. But we should do so only insofar as this helps curtail the much worse damage that's being done by others. Otherwise we risk falling into a Calvinist sort of pointless guilt, in which all we accomplish is to feel horribly depressed for expending energy on a Zoom call while billionaires continue to fly first class across the Atlantic for weekly holidays.
Conclusions
Phew ! Well done if you made it this far.
I wanted to try and venture a few thoughts as to what might happen next. But, having rewritten this section several times and always ending up with content I never quite believed, I decided to abandon this approach. Instead, I can maybe offer some thoughts about why these predictions are so difficult – and maybe just a hint of something more.
The obvious reasons are that trying to predict what something more capable than ourselves might achieve is fundamentally difficult, and of course the rapid pace of development makes prediction inherently uncertain. A slightly more interesting factor is that different people will adopt different approaches. Some will despise AI, some will love it, some will see it as a tool, others will use it more like collaborators. That we already see this happening makes giving any one answer about what's going to happen next a flawed question, like insisting that there can be only one answer to "what happened in Britain after the Romans left ?" when in fact there are many.
Related to this, I also think that people have very different ideas as to which part of the analysis they'd like to automate away. For me, visual inspection of the data is the fun part. For others, that's the bit they most want to avoid and they want to concentrate on the mathematics or the hypothesis-generation. So prediction difficulties are hit by a double-whammy (at least !) on inhomogeneities : people have different attitudes to AI and different preferences to what they want to automate. Maybe it'll all just balance out.
Another difficulty is feedback : that we don't know how this level of automation will affect us. I'd like to think that it'll be linear, that we all pull back on the stuff we don't like (different though that will be for everyone) and concentrate our mental resources on the stuff we do. That is, our mental capacities won't diminish, we'll just redirect ourselves. I think that's probably likely to be the case most of the time, because while Hogg takes it too far, people in academia do value the experience of problem-solving in itself. They aren't likely to want to just stop doing that – indeed, some of them might even not be able to. But we also have to consider the temptations towards laziness, to jump straight to the final answer... and more insidiously, that maybe only the low-level stuff is sufficient to really keep the brain working at peak ability or prevent it from degrading.
People on different sides of the AI debate all have very different views on this. I lean towards "this will just be a good thing, most of the time". My personal experience is this is something which really lets us get shit done, and nothing remotely comparable in getting-shit-done abilities has preceded this in my lifetime. Not even close. It's hard not to be optimistic about that, and I incline towards the view that a thing which is good for getting shit done is highly unlikely to actually decrease the amount of getting shit done. Even so, I don't think the effects are fully predictable, and I don't dismiss the tendency to skip the legwork* and get to the answer instead.
* Isn't skipping itself legwork though ?
I also have to recognise the special privilege of astronomy here. It's not quite that our results are of no importance, as Hogg claims. It's a question of precision and timescales. On the long term, our broad results are as important as anything else in any field of knowledge. But the exact values we determine in the short term are indeed of no importance to anyone else except ourselves. This frees us from any immediate need to be productive : our findings almost certainly won't cure cancer or relieve pain or solve famine, except possibly through spin-offs. So this means we can, and perhaps will, continue to do some of the low-level stuff genuinely for enjoyment, just as I've written this extremely long blog entirely by hand* because it's something I wanted to do, not because I think more than half-a-dozen people are likely to read it or even because I thought I could do a better job than a chatbot.
* I deliberately added a keyboard shortcut to make typing en dashes easier, so don't let those fool you. I happen to like en dashes, mmkay ?
The importance of small data in astronomy remains key to allowing humans to make genuine contributions, just as amateur astronomers can and do still make valuable discoveries for the professionals. This is not something that has direct equivalences in other fields, but it does give us some clues to the (short-term) future. While Hogg may be right that an LLM can write a paper 100,000 times faster than a human, they don't have any innate desire to do so. There's no topic an LLM actually has an interest in because they literally don't exist until prompted by a human : they have a crude agency, but no consciousness. And yes, while it might eventually be possible the deploy the large-scale compute Hogg hypothesises* could lead to factors more in the billions, where we could simply ask, "Please solve all problems in astronomy" and get something back that wasn't drivel, I will go so far as to say this isn't happening this decade.
* If I were him, I would make it my professional mission to have a hypothesis named after me.
So we're safe for the foreseeable future. The techbro predictions are hype, but they're not made up of nothing. AI is and will be transformative for astronomy. Anyone thinking that we can ignore it, that things will carry on as normal, or even that they can clearly see where this is going, well, enjoy your blissful ignorance, but I'm afraid you didn't get the memo. My only prediction is that the solution will be obvious after the fact and a handful of people who got lucky with the right call will proclaim themselves wise sages... unless they too are replaced with killer robots. Only time will tell.
