More assessment of AI X-risk potential

I’m been stimulated by Luciano Floridi’s recent article in Aeon “Should we be afraid of AI?”. I’m surprised that this issue hasn’t been settled yet, since it seems like “we” have the formal tools necessary to solve the problem decisively. But nevertheless this appears to be the subject of debate.

I was referred to Kaj Sotala’s rebuttal of an earlier work by Floridi which his Aeon article was based on. The rebuttal appears in this APA Newsletter on Philosophy and Computers. It is worth reading.

The issue that I’m most interested in is whether or not AI risk research should constitute a special, independent branch of research, or whether it can be approached just as well by pursuing a number of other more mainstream artificial intelligence research agendas. My primary engagement with these debates has so far been an analysis of Nick Bostrom’s argument in his book Superintelligence, which tries to argue in particular that there is an existential risk (or X-risk) to humanity from artificial intelligence. “Existential risk” means a risk to the existence of something, in this case humanity. And the risk Bostrom has written about is the risk of eponymous superintelligence: an artificial intelligence that gets smart enough to improve its own intelligence, achieve omnipotence, and end the world as we know it.

I’ve posted my rebuttal to this argument on arXiv. The one-sentence summary of the argument is: algorithms can’t just modify themselves into omnipotence because they will hit performance bounds due to data and hardware.

A number of friends have pointed out to me that this is not a decisive argument. They say: don’t you just need the AI to advance fast enough and far enough to be an existential threat?

There are a number of reasons why I don’t believe this is likely. In fact, I believe that it is provably vanishingly unlikely. This is not to say that I have a proof, per se. I suppose it’s incumbent on me to work it out and see if the proof is really there.

So: Herewith is my Sketch Of A Proof of why there’s no significant artificial intelligence existential risk.

Lemma: Intelligence advances due to purely algorithmic self-modificiation will always plateau due to data and hardware constraints, which advance more slowly.

Proof: This paper.

As a consequence, all artificial intelligence explosions will be sigmoid. That is, starting slow, accelerating, then decelerating, the growing so slowly as to be asymptotic. Let’s call the level of intelligence at which an explosion asymptotes the explosion bound.

There’s empirical support for this claim. Basically, we have never had a really big intelligence explosion due to algorithmic improvement alone. Looking at the impressive results of the last seventy years, most of the impressiveness can be attributed to advances in hardware and data collection. Notoriously, Deep Learning is largely just decades old artificial neural network technology repurposed to GPU’s on the cloud. Which is awesome and a little scary. But it’s not an algorithmic intelligence explosion. It’s a consolidation of material computing power and sensor technology by organizations. The algorithmic advances fill those material shoes really quickly, it’s true. This is precisely the point: it’s not the algorithms that’s the bottleneck.

Observation: Intelligence explosions are happening all the time. Most of them are small.

Once we accept the idea that intelligence explosions are all bounded, it becomes rather arbitrary where we draw the line between an intelligence explosion and some lesser algorithmic intelligence advance. There is a real sense in which any significant intelligence advance is a sigmoid expansion in intelligence. This would include run-of-the-mill scientific discoveries and good ideas.

If intelligence explosions are anything like virtually every other interesting empirical phenomenon, then they are distributed according to a heavy tail distribution. This means a distribution with a lot of very small values and a diminishing probability of higher values that nevertheless assigns some probability to very high values. Assuming intelligence is something that can be quantified and observed empirically (a huge ‘if’ taken for granted in this discussion), we can (theoretically) take a good hard look at the ways intelligence has advanced. Look around you. Do you see people and computers getting smarter all the time, sometimes in leaps and bounds but most of the time minutely? That’s a confirmation of this hypothesis!

The big idea here is really just to assert that there is a probability distribution over intelligence explosion bounds that all actual intelligence explosions are being drawn from. This follows more or less directly from the conclusion that all intelligence explosions are bounded. Once we posit such a distribution, it becomes possible to take expected values of functions of its values and functions of its values.

Empirical claim: Hardware and sensing advances diffuse rapidly relative to their contribution to intelligence gains.

There’s an material, socio-technical analog to Bostrom’s explosive superintelligence. We could imagine a corporation that is working in secret on new computing infrastructure. Whenever it has an advance in computing infrastructure, the AI people (or increasingly, the AI-writing-AI) develops programming that maximizes its use of this new technology. Then it uses that technology to enrich its own computer-improving facilities. When it needs more…minerals…or whatever it needs to further its research efforts, it finds a way to get them. It proceeds to take over the world.

This may presently be happening. But evidence suggests that this isn’t how the technology economy really works. No doubt Amazon (for example) is using Amazon Web Services internally to do its business analytics. But also it makes its business out of selling out its computing infrastructure to other organizations as a commodity. That’s actually the best way it can enrich itself.

What’s happening here is the diffusion of innovation, which is a well-studied phenomenon in economics and other fields. Ideas spread. Technological designs spread. I’d go so far as to say that it is often (perhaps always?) the best strategy for some agent that has locally discovered a way to advance its own intelligence to figure out how to trade that intelligence to other agents. Almost always that trade involves the diffusion of the basis of that intelligence itself.

Why? Because since there are independent intelligence advances of varying sizes happening all the time, there’s actually a very competitive market for innovation that quickly devalues any particular gain. A discovery, if hoarded, will likely be discovered by somebody else. The race to get credit for any technological advance at all motivates diffusion and disclosure.

The result is that the distribution of innovation, rather than concentrating into very tall spikes, is constantly flattening and fattening itself. That’s important because…

Claim: Intelligence risk is not due to absolute levels of intelligence, but relative intelligence advantage.

The idea here is that since humanity is composed of lots of interacting intelligence sociotechnical organizations, any hostile intelligence is going to have a lot of intelligent adversaries. If the game of life can be won through intelligence alone, then it can only be won with a really big intelligence advantage over other intelligent beings. It’s not about absolute intelligence, it’s intelligence inequality we need to worry about.

Consequently, the more intelligence advances (i.e, technologies) diffuse, the less risk there is.

Conclusion: The chance of an existential risk from an intelligence explosion is small and decreasing all the time.

So consider this: globally, there’s tons of investment in technologies that, when discovered, allow for local algorithmic intelligence explosions.

But even if we assume these algorithmic advances are nearly instantaneous, they are still bounded.

Lots of independent bounded explosions are happening all the time. But they are also diffusing all the time.

Since the global intelligence distribution is always fattening, that means that the chance of any particular technological advance granting a decisive advantage over others is decreasing.

There is always the possibility of a fluke, of course. But if there was going to be a humanity destroying technological discovery, it would probably have already been invented and destroyed us. Since it hasn’t, we have a lot more resilience to threats from intelligence explosions, not to mention a lot of other threats.

This doesn’t mean that it isn’t worth trying to figure out how to make AI better for people. But it does diminish the need to think about artificial intelligence as an existential risk. It makes AI much more comparable to a biological threat. Biological threats could be really bad for humanity. But there’s also the organic reality that life is very resilient and human life in general is very secure precisely because it has developed so much intelligence.

I believe that thinking about the risks of artificial intelligence as analogous to the risks from biological threats is helpful for prioritizing where research effort in artificial intelligence should go. Just because AI doesn’t present an existential risk to all of humanity doesn’t mean it doesn’t kill a lot of people or make their lives miserable. On the contrary, we are in a world with both a lot of artificial and non-artificial intelligence and a lot of miserable and dying people. These phenomena are not causally disconnected. A good research agenda for AI could start with an investigation of these actually miserable people and what their problems are, and how AI is causing that suffering or alternatively what it could do to improve things. That would be an enormously more productive research agenda than one that aims primarily to reduce the impact of potential explosions which are diminishingly unlikely to occur.

Lenin and Luxemburg

One of the interesting parts of Scott’s Seeing Like a State is a detailed analysis of Vladimir Lenin’s ideological writings juxtaposed with one of this contemporary critics, Rosa Luxemburg, who was a philosopher and activist in Germany.

Scott is critical of Lenin, pointing out that while his writings emphasize the role of a secretive intelligentsia commanding the raw material of an angry working class through propaganda and a kind of middle management tier of revolutionarily educated factory bosses, this is not how the revolution actually happened. The Bolsheviks took over an empty throne, so to speak, because the czars had already lost their power fighting Austria in World War I. This left Russia headless, with local regions ruled by local autonomous powers. Many of these powers were in fact peasant and proletarian collectives. But others may have been soldiers returning from war and seizing whatever control they could by force.

Luxemburg’s revolutionary theory was much more sensitive to the complexity of decentralized power. Rather than expecting the working class to submit unquestioningly to top-down control and coordinating in mass strikes, she acknowledged a reality that decentralized groups would act in an uncoordinated way. This was good for the revolutionary cause, she argued, because it allowed the local energy and creativity of workers movements to move effectively and contribute spontaneously to the overall outcome. Whereas Lenin saw spontaneity in the working class as leading inevitably to their being coopted by bourgeois ideology, Luxemburg believed the spontaneous authentic action of autonomously acting working class people were vital to keeping the revolution unified and responsive to working class interests.

artificial life, artificial intelligence, artificial society, artificial morality

“Everyone” “knows” what artificial intelligence is and isn’t and why it is and isn’t a transformative thing happening in society and technology and industry right now.

But the fact is that most of what “we” “call” artificial intelligence is really just increasingly sophisticated ways of solving a single class of problems: optimization.

Essentially what’s happened in AI is that all empirical inference problems can be modeled as Bayesian problems, which are then solved using variational inference methods, which are essentially just turning the Bayesian statistic problem into a solvable form of an optimization problem, and solving it.

Advances in optimization have greatly expanded the number of things computers can accomplish as part of a weak AI research agenda.

Frequently these remarkable successes in Weak AI are confused with an impending revolution in what used to be called Strong AI but which now is more frequently called Artificial General Intelligence, or AGI.

Recent interest in AGI has spurred a lot of interesting research. How could it not be interesting? It is also, for me, extraordinarily frustrating research because I find the philosophical precommitments of most AGI researchers baffling.

One insight that I wish made its way more frequently into discussions of AGI is an insight made by the late Francisco Varela, who argued that you can’t really solve the problem of artificial intelligence until you have solved the problem of artificial life. This is for the simple reason that only living things are really intelligent in anything but the weak sense of being capable of optimization.

Once being alive is taken as a precondition for being intelligent, the problem of understanding AGI implicates a profound and fascinating problem of understanding the mathematical foundations of life. This is a really amazing research problem that for some reason is never ever discussed by anybody.

Let’s assume it’s possible to solve this problem in a satisfactory way. That’s a big If!

Then a theory of artificial general intelligence should be able to show how some artificial living organisms are and others are not intelligent. I suppose what’s most significant here is the shift in thinking of AI in terms of “agents”, a term so generic as to be perhaps at the end of the day meaningless, to thinking of AI in terms of “organisms”, which suggests a much richer set of preconditions.

I have similar grief over contemporary discussion of machine ethics. This is a field with fascinating, profound potential. But much of what machine ethics boils down to today are trolley problems, which are as insipid as they are troublingly intractable. There’s other, better machine ethics research out there, but I’ve yet to see something that really speaks to properly defining the problem, let alone solving it.

This is perhaps because for a machine to truly be ethical, as opposed to just being designed and deployed ethically, it must have moral agency. I don’t mean this in some bogus early Latourian sense of “wouldn’t it be fun if we pretended seatbelts were little gnomes clinging to our seats” but in an actual sense of participating in moral life. There’s a good case to be made that the latter is not something easily reducible to decontextualized action or function, but rather has to do with how own participates more broadly in social life.

I suppose this is a rather substantive metaethical claim to be making. It may be one that’s at odds with common ideological trainings in Anglophone countries where it’s relatively popular to discuss AGI as a research problem. It has more in common, intellectually and philosophically, with continental philosophy than analytic philosophy, whereas “artificial intelligence” research is in many ways a product of the latter. This perhaps explains why these two fields are today rather disjoint.

Nevertheless, I’d happily make the case that the continental tradition has developed a richer and more interesting ethical tradition than what analytic philosophy has given us. Among other reasons this is because of how it is able to situated ethics as a function of a more broadly understood social and political life.

I postulate that what is characteristic of social and political life is that it involves the interaction of many intelligent organisms. Which of course means that to truly understand this form of life and how one might recreate it artificially, one must understand artificial intelligence and, transitively, artificial life.

Only one artificial society is sufficiently well-understood could we then approach the problem of artificial morality, or how to create machines that truly act according to moral or ethical ideals.

ideologies of capitals

A key idea of Bourdieusian social theory is that society’s structure is due to the distribution of multiple kinds of capital. Social fields have their roles and their rules, but they are organized around different forms of capital the way physical systems are organized around sources of force like mass and electrical charge. Being Kantian, Bourdieusian social theory is compatible with both positivist and phenomenological forms of social explanation. Phenomenological experience, to the extent that it repeats itself and so can be described aptly as a social phenomenon at all, is codified in terms of habitus. But habitus is indexed to its place within a larger social space (not unlike, it must be said, a Blau space) whose dimensions are the dimensions of the allocations of capital throughout it.

While perhaps not strictly speaking a corollary, this view suggests a convenient methodological reduction, according to which the characteristic beliefs of a habitus can be decomposed into components, each component representing the interests of a certain kind of capital. When I say “the interests of a capital”, I do mean the interests of the typical person who holds a kind of capital, but also the interests of a form of capital, apart from and beyond the interests of any individual who carries it. This is an ontological position that gives capital an autonomous social life of its own, much like we might attribute an autonomous social life to a political entity like a state. This is not the same thing as attributing to capital any kind of personhood; I’m not going near the contentious legal position that corporations are people, for example. Rather, I mean something like: if we admit that social life is dictated in part by the life cycle of a kind of psychic microorganism, the meme, then we should also admit abstractly of social macroorganisms, such as capitals.

What the hell am I talking about?

Well, the most obvious kind of capital worth talking about in this way is money. Money, in our late modern times, is a phenomenon whose existence depends on a vast global network of property regimes, banking systems, transfer protocols, trade agreements, and more. There’s clearly a naivete in referring to it as a singular or homogeneous phenomenon. But it is also possible to referring to in a generic globalized way because of the ways money markets have integrated. There is a sense in which money exists to make more money and to give money more power over other forms of capital that are not money, such as: social authority based on any form of seniority, expertise, lineage; power local to an institution; or the persuasiveness of an autonomous ideal. Those that have a lot of money are likely to have an ideology very different from those without a lot of money. This is partly due to the fact that those who have a lot of money will be interested in promoting the value of that money over and above other capitals. Those without a lot of money will be interested inn promoting forms of power that contest the power of money.

Another kind of capital worth talking about is cosmopolitanism. This may not be the best word for what I’m pointing at but it’s the one that comes to mind now. What I’m talking about is the kind of social capital one gets not by having a specific mastery of a local cultural form, but rather by having the general knowledge and cross-cultural competence to bridge across many different local cultures. This form of capital is loosely correlated with money but is quite different from it.

A diagnosis of recent shifts in U.S. politics, for example, could be done in terms of the way capital and cosmopolitanism have competed for control over state institutions.

equilibrium representation

We must keep in mind not only the capacity of state simplifications to transform the world but also the capacity of the society to modify, subvert, block, and even overturn the categories imposed upon it. Here is it useful to distinguish what might be called facts on paper from facts on the ground…. Land invasions, squatting, and poaching, if successful, represent the exercise of de facto property rights which are not represented on paper. Certain land taxes and tithes have been evaded or defied to the point where they have become dead letters. The gulf between land tenure facts on paper and facts on the ground is probably greatest at moments of social turmoil and revolt. But even in more tranquil times, there will always be a shadow land-tenure system lurking beside and beneath the official account in the land-records office. We must never assume that local practice conforms with state theory. – Scott, Seeing Like a State, 1998

I’m continuing to read Seeing Like a State and am finding in it a compelling statement of a state of affairs that is coded elsewhere into the methodological differences between social science disciplines. In my experience, much of the tension between the social sciences can be explained in terms of the differently interested uses of social science. Among these uses are the development of what Scott calls “state theory” and the articulation, recognition, and transmission of “local practice”. Contrast neoclassical economics with the anthropology of Jean Lave as examples of what I’m talking about. Most scholars are willing to stop here: they choose their side and engage in a sophisticated form of class warfare.

This is disappointing from the perspective of science per se, as a pursuit of truth. To see where there’s a place for such work in the social sciences, we only have to the very book in front of us, Seeing Like a State, which stands outside of both state theory and local practices to explain a perspective that is neither but rather informed by a study of both.

In terms of the ways that knowledge is used in support of human interests, in the Habermasian sense (see some other blog posts), we can talk about Scott’s “state theory” as a form of technical knowledge, aimed at facilitating power over the social and natural world. What he discusses is the limitation of technical knowledge in mastering the social, due to complexity and differentiation in local practice. So much of this complexity is due to the politicization of language and representation that occurs in local practice. Standard units of measurement and standard terminology are tools of state power; efforts to guarantee them are confounded again and again in local interest. This disagreement is a rejection of the possibility of hermeneutic knowledge, which is to say linguistic agreement about norms.

In other words, Scott is pointing to a phenomenon where because of the interests of different parties at different levels of power, there’s a strategic local rejection of inter-subjective agreement. Implicitly, agreeing even on how to talk with somebody with power over you is conceding their power. The alternative is refusal in some sense. A second order effect of the complexity caused by this strategic disagreement is the confounding of technical mastery over the social. In Scott’s terminology, a society that is full of strategic lexical disagreement is not legible.

These are generalizations reflecting tendencies in society across history. Nevertheless, merely by asserting them I am arguing that they have a kind of special status that is not itself caught up in the strategic subversions of discourse that make other forms of expertise foolish. There must be some forms of representation that persist despite the verbal disagreements and differently motivated parties that use them.

I’d like to call these kinds of representations, which somehow are technically valid enough to be useful and robust to disagreement, even politicized disagreement, as equilibrium representations. The idea here is that despite a lot of cultural and epistemic churn, there are still attractor states in the complex system of knowledge production. At equilibrium, these representations will be stable and serve as the basis for communication between different parties.

I’ve posited equilibrium representations hypothetically, without having a proof or example yet on one that actually exists. My point is to have a useful concept that acknowledges the kinds of epistemic complexities raised by Scott but that acknowledges the conditions for which a modernist epistemology could prevail despite those complexities.


appropriate information flow

Contextual integrity theory defines privacy as appropriate information flow.

Whether or not this is the right way to define privacy (which might, for example, be something much more limited), and whether or not contextual integrity as it is currently resourced as a theory is capable of capturing all considerations needed to determine the appropriateness of information flow, the very idea of appropriate information flow is a powerful one. It makes sense to strive to better our understanding of which information flows are appropriate, which others are inappropriate, to whom, and why.


Seeing Like a State: problems facing the code rural

I’ve been reading James C. Scott’s Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed for, once again, Classics. It’s just as good as everyone says it is, and in many ways the counterpoint to James Beniger’s The Control Revolution that I’ve been looking for. It’s also highly relevant to work I’m doing on contextual integrity in privacy.

Here’s a passage I read on the subway this morning that talks about the resistance to codification of rural land use customs in Napoleonic France.

In the end, no postrevolutionary rural code attracted a winning coalition, even amid a flurry of Napoleonic codes in nearly all other realms. For our purposes, the history of the stalemate is instructive. The first proposal for a code, which was drafted in 1803 and 1807, would have swept away most traditional rights (such as common pasturage and free passage through others’ property) and essentially recast rural property relations in the light of bourgeois property rights and freedom of contract. Although the proposed code pefigured certain modern French practices, many revolutionaries blocked it because they feared that its hands-off liberalism would allow large landholders to recreate the subordination of feudalism in a new guise.

A reexamination of the issue was then ordered by Napoleon and presided over by Joseph Verneilh Puyrasseau. Concurrently, Depute Lalouette proposed to do precisely what I supposed, in the hypothetical example, was impossible. That is, he undertook to systematically gather information about all local practices, to classify and codify them, and then to sanction them by decree. The decree in question would become the code rural. Two problems undid this charming scheme to present the rural poplace with a rural code that simply reflected its own practices. The first difficulty was in deciding which aspects of the literally “infinite diversity” or rural production relations were to be represented and codified. Even if a particular locality, practices varied greatly from farm to farm over time; any codification would be partly arbitrary and artificially static. To codify local practices was thus a profoundly political act. Local notables would be able to sanction their preferences with the mantle of law, whereas others would lose customary rights that they depended on. The second difficulty was that Lalouette’s plan was a mortal threat to all state centralizers and economic modernizers for whom a legible, national property regime was the procondition of progress. As Serge Aberdam notes, “The Lalouette project would have brought about exactly what Merlin de Douai and the bourgeois, revolutionary jurists always sought ot avoid.” Neither Lalouette nor Verneilh’s proposed code was ever passed, because they, like their predecessor in 1807, seemed to be designed to strengthen the hand of the landowners.

(Emphasis mine.)

The moral of the story is that just as the codification of a land map will be inaccurate and politically contested for its biases, so too a codification of customs and norms will suffer the same fate. As Borges’ fable On Exactitude in Science mocks the ambition of physical science, we might see the French attempts at code rural to be a mockery of the ambition of computational social science.

On the other hand, Napoleonic France did not have the sweet ML we have today. So all bets are off.

industrial technology development and academic research

I now split my time between industrial technology (software) development and academic research.

There is a sense in which both activities are “scientific”. They both require the consistent use of reason and investigation to arrive at reliable forms of knowledge. My industrial and academic specializations are closely enough aligned that both aim to create some form of computational product. These activities are constantly informing one another.

What is the difference between these two activities?

One difference is that industrial work pays a lot better than academic work. This is probably the most salient difference in my experience.

Another difference is that academic work is more “basic” and less “applied”, allowing it to address more speculative questions.

You might think that the latter kind of work is more “fun”. But really, I find both kinds of work fun. Fun-factor is not an important difference for me.

What are other differences?

Here’s one: I find myself emotionally moved and engaged by my academic work in certain ways. I suppose that since my academic work straddles technology research and ethics research (I’m studying privacy-by-design), one thing I’m doing when I do this work is engaging and refining my moral intuitions. This is rewarding.

I do sometimes also feel that it is self-indulgent, because one thing that thinking about ethics isn’t is taking responsibility for real change in the world. And here I’ll express an opinion that is unpopular in academia, which is that being in industry is about taking responsibility for real change in the world. This change can benefit other people, and it’s good when people in industry get paid well because they are doing hard work that entails real risks. Part of the risk is the responsibility that comes with action in an uncertain world.

Another critically important difference between industrial technology development and academic research is that while the knowledge created by the former is designed foremost to be deployed and used, the knowledge created by the latter is designed to be taught. As I get older and more advanced as a researcher, I see that this difference is actually an essential one. Knowledge that is designed to be taught needs to be teachable to students, and students are generally coming from both a shallower and more narrow background than adult professionals. Knowledge that is designed to by deployed and used need only be truly shared by a small number of experienced practitioners. Most of the people affected by the knowledge will be affected by it indirectly, via artifacts. It can be opaque to them.

Industrial technology production changes the way the world works and makes the world more opaque. Academic research changes the way people work, and reveals things about the world that had been hidden or unknown.

When straddling both worlds, it becomes quite clear that while students are taught that academic scientists are at the frontier of knowledge, ahead of everybody else, they are actually far behind what’s being done in industry. The constraint that academic research must be taught actually drags its form of science far behind what’s being done regularly in industry.

This is humbling for academic science. But it doesn’t make it any less important. Rather, in makes it even more important, but not because of the heroic status of academic researchers being at the top of the pyramid of human knowledge. It’s because the health of the social system depends on its renewal through the education system. If most knowledge is held in secret and deployed but not passed on, we will find ourselves in a society that is increasingly mysterious and out of our control. Academic research is about advancing the knowledge that is available for education. It’s effects can take half a generation or longer to come to fruition. Against this long-term signal, the oscillations that happen within industrial knowledge, which are very real, do fade into the background. Though not before having real and often lasting effects.

arXiv preprint of Refutation of Bostrom’s Superintelligence Argument released

I’ve written a lot of blog posts about Nick Bostrom’s book Superintelligence, presented what I think is a refutation of his core argument.

Today I’ve released an arXiv preprint with a more concise and readable version of this argument. Here’s the abstract:

Don’t Fear the Reaper: Refuting Bostrom’s Superintelligence Argument

In recent years prominent intellectuals have raised ethical concerns about the consequences of artificial intelligence. One concern is that an autonomous agent might modify itself to become “superintelligent” and, in supremely effective pursuit of poorly specified goals, destroy all of humanity. This paper considers and rejects the possibility of this outcome. We argue that this scenario depends on an agent’s ability to rapidly improve its ability to predict its environment through self-modification. Using a Bayesian model of a reasoning agent, we show that there are important limitations to how an agent may improve its predictive ability through self-modification alone. We conclude that concern about this artificial intelligence outcome is misplaced and better directed at policy questions around data access and storage.

I invite any feedback on this work.

the “hacker class”, automation, and smart capital

(Mood music for reading this post:)

I mentioned earlier that I no longer think hacker class consciousness is important.

As incongruous as this claim is now, I’ve explained that this is coming up as I go through old notes and discard them.

I found another page of notes that reminds me there was a little more nuance to my earlier position that I remembered, which has to do with the kind of labor done by “hackers”, a term I reserve the right to use in MIT/Eric S. Raymond sense, without the political baggage that has since attached to the term.

The point was in response to Eric. S. Raymond’s “How to be a hacker” essay which was that part of what it means to be a “hacker” is to hate drudgery. The whole point of programming a computer is so that you never have to do the same activity twice. Ideally, anything that’s repeatable about the activity gets delegated to the computer.

This is relevant in the contemporary political situation because we’re probably now dealing with the upshot of structural underemployment due to automation and the resulting inequalities. This remains a topic that scholarship, technologists, and politicians seem systematically unable to address directly even when they attempt to, because everybody who sees the writing on the wall is too busy trying to get the sweet end of that deal.

It’s a very old argument that those who own the means of production are able to negotiate for a better share of the surplus value created by their collaborations with labor. Those who own or invest in capital generally speaking would like to increase that share. So there’s market pressure to replace reliance of skilled labor, which is expensive, with reliance on less skilled labor, which is plentiful.

So what gets industrialists excited is smart capital, or a means of production that performs the “skilled” functions formerly performed by labor. Call it artificial intelligence. Call it machine learning. Call it data science. Call it “the technology industry”. That’s what’s happening and been happening for some time.

This leaves good work for a single economic class of people, those whose skills are precisely those that produce this smart capital.

I never figured out what the end result of this process would be. I imagined at one point that the creation of the right open source technology would bring about a profound economic transformation. A far fetched hunch.